Some years ago, it was pointed out by Istepanian (Istepanian, 2003) some future trends in m-Health, indicating the way this technology could be impacted by 3G technology. Today, we are witnessing the arrival of 4G communication technology. 4G technology should be able to deliver peak download speeds of 1Gbps in stationary environments, and 100Mbps in high mobile environments. The impact of 4G on m-Health will be fantastic because m-Health solutions will introduce video images to the health professionals as they will have in their hands the powerful resources that real time imaging offers. In that direction, as reported in (Green Technology News, 2011), Washington Hospital Center, a teaching hospital in Washington DC, is using a different form of mobile technology. The hospital worked in collaboration with AT&T to develop "CodeHeart," a mobile application that provides realtime video and audio streams that can be used in critical care situations. This system allows physicians and first responders to communicate during an emergency situation working with real time images. Hospital cardiologists, for example, can view vital signs and test results captured through real-time video feed while simultaneously speaking with the patient's first responder or an attending emergency department doctor. Using the mobile application in this scenario, the cardiologist can assess and prepare cardiac treatment before the patient arrives at the emergency department, while emergency departments themselves can better prepare for the patient's arrival.

As can be seen, the progress of m-Health solutions is quite astonishing. In only a few years we have seen a huge amount of research and development in this area including local patient care, video conference systems, in which, a patient can be monitored at home by health professionals through communiction between the hospital and patient using video IP calls, internet communication with a remote server and remote monitoring using a mobile communications infrastucture. Jim Black (Black et al., 2010) reported a system to aid health workers using a hardware platform that can work with some sensors. The results were exibited in a cell phone application. So basically all applications in that system run locally and the whole system, hardware and cell phone, should be provided to the health professional. The applications on that system were developed using C# and the Microsoft .NET framework running on SmartPhones. Jim Black reported the development of applications like respiratory and pulse rate calculator, Gestational dates calculator, formulary/drug dose calculator, drip rate calculator, drug reminder alarm, among others. Joseph Finkelstein (Finkelstein, et al 2010) presented a solution that could also be used locally at home. It is basically an application using internet as patient at home can use. Data are sent to a remote server where a clinician can analyze the information and send back the proper actions for a patient to follow. The system mentioned in that application has been used for management of patients with asthma, COPD, hypertension, inflammatory bowel disease, and multiple sclerosis. Jablonski (Jablonski, et al 2010) reported a system that monitors the patient health at home, and sends the collected data to a remote server via internet Virtual Private Network (OpenVPN) using the HTTP protocol. The telemedical system therein described is dedicated to patients needing a long term monitoring of their lung function. Home monitoring using a local network incorporating a wireless transmission module like ZigBee has also been reported in (Wun-Jin Li, et al, 2010) to monitor blood pressure. David Mulvaney (Mulvaney, et al 2010) developed a system to monitor and collect data on heart diseased and diabetic patient in rural areas. This system is able to send data via internet or using the mobile communications infrastructure (GSM/GPRS)/3G). We are reporting here a system that is like that reported by Mulvaney in collecting patient data and sending it remotelly using the installed mobile communication infrastructure (data is sent as SMS via GSM modem). The next section describes the developed system.

#### **4. Hardware conception**

164 Advances and Applications in Mobile Computing

Some years ago, it was pointed out by Istepanian (Istepanian, 2003) some future trends in m-Health, indicating the way this technology could be impacted by 3G technology. Today, we are witnessing the arrival of 4G communication technology. 4G technology should be able to deliver peak download speeds of 1Gbps in stationary environments, and 100Mbps in high mobile environments. The impact of 4G on m-Health will be fantastic because m-Health solutions will introduce video images to the health professionals as they will have in their hands the powerful resources that real time imaging offers. In that direction, as reported in (Green Technology News, 2011), Washington Hospital Center, a teaching hospital in Washington DC, is using a different form of mobile technology. The hospital worked in collaboration with AT&T to develop "CodeHeart," a mobile application that provides realtime video and audio streams that can be used in critical care situations. This system allows physicians and first responders to communicate during an emergency situation working with real time images. Hospital cardiologists, for example, can view vital signs and test results captured through real-time video feed while simultaneously speaking with the patient's first responder or an attending emergency department doctor. Using the mobile application in this scenario, the cardiologist can assess and prepare cardiac treatment before the patient arrives at the emergency department, while emergency departments themselves

As can be seen, the progress of m-Health solutions is quite astonishing. In only a few years we have seen a huge amount of research and development in this area including local patient care, video conference systems, in which, a patient can be monitored at home by health professionals through communiction between the hospital and patient using video IP calls, internet communication with a remote server and remote monitoring using a mobile communications infrastucture. Jim Black (Black et al., 2010) reported a system to aid health workers using a hardware platform that can work with some sensors. The results were exibited in a cell phone application. So basically all applications in that system run locally and the whole system, hardware and cell phone, should be provided to the health professional. The applications on that system were developed using C# and the Microsoft .NET framework running on SmartPhones. Jim Black reported the development of applications like respiratory and pulse rate calculator, Gestational dates calculator, formulary/drug dose calculator, drip rate calculator, drug reminder alarm, among others. Joseph Finkelstein (Finkelstein, et al 2010) presented a solution that could also be used locally at home. It is basically an application using internet as patient at home can use. Data are sent to a remote server where a clinician can analyze the information and send back the proper actions for a patient to follow. The system mentioned in that application has been used for management of patients with asthma, COPD, hypertension, inflammatory bowel disease, and multiple sclerosis. Jablonski (Jablonski, et al 2010) reported a system that monitors the patient health at home, and sends the collected data to a remote server via internet Virtual Private Network (OpenVPN) using the HTTP protocol. The telemedical system therein described is dedicated to patients needing a long term monitoring of their lung function. Home monitoring using a local network incorporating a wireless transmission module like ZigBee has also been reported in (Wun-Jin Li, et al, 2010) to monitor blood pressure. David Mulvaney (Mulvaney, et al 2010) developed a system to monitor and collect data on heart diseased and diabetic patient in rural areas. This system is able to send data via internet or using the mobile communications infrastructure (GSM/GPRS)/3G). We are reporting here a system that is like that reported by Mulvaney in

can better prepare for the patient's arrival.

Aware of the importance that m-Health represents to provide health care to patients, our Group at *Federal University of ABC* (São Paulo, Brazil) conducted a study to figure out an application that would be able to be implemented in hardware, able to monitor vital signs and sent health data of a patient remotely. Specifically, the objective was to demonstrate the hability of our prototype in capturing data of the patient and sending it immediately to a remote location using SMS. The next step in our our decision making was to choose the signals that could be monitored. Monitoring of these signals should be done in the same electronic module, by just changing a switch position. It was conceived that the electronic module should be able to show data locally and with remote transmission capability. In the following section we describe the conception, and module implementation.

#### **4.1 System architecture**

Architecture of the developed system is shown in figure 1.

The system includes two electronic units for signal acquisition. One of these units allows acquisition of arterial pressure signals, and the second one acquires a signal for oximetry analisys. These acquisition blocks include amplification and filtering stages. Signals coming from these inputs are fed into a microcontroller. The microcontroller unit controls signal digitalization, signal processing and transfer of captured samples to a GSM modem, which, in turn, sends processed information to a remote location using text messages (SMS).

Fig. 1. Block Diagram of prototype implemented.

#### **4.2 Arterial pressure module**

The method used to measure blood pressure in this system was the oscillometric method which allows measurements of systolic and diastolic pressure. In this method, the arterial flow is blocked by inflating the pneumatic system (cuff) above the systolic pressure level. Afterwards, the pressure is reduced slowly and blood flows through the artery in an oscillatory way. When oscillations cross the cuff pressure, the systolic pressure is identified. As pressure in the system continues to decrease slowly, to the point it is under the cuff pressure, the diastolic pressure can be measured (Peura, 1998).

During the measurement cycle, the microcontroller activates the inflation and deflation of the pneumatic system. The cuff pressure is detected by a piezoelectric transducer and converted into an electrical signal proportional to the pressure and processed by two different circuits. One of them amplifies and makes a correction of the signal offsetting it before it is passed to an analog to digital converter (ADC). The second circuit consists of a high pass filter and amplifier.

The pressure signal acquisition module includes the acquisition units, the conditioning circuitry, including amplification and filtering. These conditioning subsystems are connected with the microcontroller unit. The microcontroller drives the pneumatic devices and also executes the digitalization of the captured samples to be sent to the communication interface. At this point, all processed information is ready to be sent to a local database bank, or to a remote location.

To measure the arterial pressure, a commercial sensor (MPX2050) was used. This sensor has a linear output proportional to the applied pressure (Freescale Semicondutor,2010). The sensor output has two signals, the pressure signal and a signal that contains the blood oscillation information (see figure 2a). The pressure signal is amplified and sent to the microcontroller to be digitalized, while the oscillations component is filtered out and then amplified (see figure 2b). In this way the continuous components contained in the oscillation signal are eliminated and adequately conditioned to be processed by the microcontroller.

Fig. 2(a). Voltage output of the pressure sensor. (Freescale Semiconductor. Application Notes).

The method used to measure blood pressure in this system was the oscillometric method which allows measurements of systolic and diastolic pressure. In this method, the arterial flow is blocked by inflating the pneumatic system (cuff) above the systolic pressure level. Afterwards, the pressure is reduced slowly and blood flows through the artery in an oscillatory way. When oscillations cross the cuff pressure, the systolic pressure is identified. As pressure in the system continues to decrease slowly, to the point it is under the cuff

During the measurement cycle, the microcontroller activates the inflation and deflation of the pneumatic system. The cuff pressure is detected by a piezoelectric transducer and converted into an electrical signal proportional to the pressure and processed by two different circuits. One of them amplifies and makes a correction of the signal offsetting it before it is passed to an analog to digital converter (ADC). The second circuit consists of a

The pressure signal acquisition module includes the acquisition units, the conditioning circuitry, including amplification and filtering. These conditioning subsystems are connected with the microcontroller unit. The microcontroller drives the pneumatic devices and also executes the digitalization of the captured samples to be sent to the communication interface. At this point, all processed information is ready to be sent to a local database bank,

To measure the arterial pressure, a commercial sensor (MPX2050) was used. This sensor has a linear output proportional to the applied pressure (Freescale Semicondutor,2010). The sensor output has two signals, the pressure signal and a signal that contains the blood oscillation information (see figure 2a). The pressure signal is amplified and sent to the microcontroller to be digitalized, while the oscillations component is filtered out and then amplified (see figure 2b). In this way the continuous components contained in the oscillation signal are eliminated and adequately conditioned to be processed by the

Fig. 2(a). Voltage output of the pressure sensor. (Freescale Semiconductor. Application

pressure, the diastolic pressure can be measured (Peura, 1998).

**4.2 Arterial pressure module**

high pass filter and amplifier.

or to a remote location.

microcontroller.

Notes).

Fig. 2(b). Oscillations at amplifier output. (Freescale Semiconductor. Application Notes).

Figure 3 illustrates the complete schematic of the arterial pressure meter. The pressure sensor (MPX2050) provides an output signal in the range of 0 mV to 40 mV, which corresponds to a pressure range of 0 mmHg to 375 mmHg. To amplify this signal, an instrumentation amplifier with a 100 voltage gain, allows an output range within 0 V to 4 V. The instrumentation amplifier output is connected to the microcontroller for processing.

As mentioned before, the inflation and deflation system is formed by a pneumatic pump and an electro valve. The pneumatic pump inflates the system, while the electro valve drains this system.

Fig. 3. Schematic of the Electronic Blood Pressure Meter. (Adapted from: Freescale Semiconductor).

As mentioned before, the inflation and deflation system is formed by a pneumatic pump and an electro valve. The pneumatic pump inflates the system, while the electro valve drains this system.

The filter consists of two RC networks from which two cut off frequencies can be obtained (0,48 Hz and 4,8 Hz). These two poles (see equation 1 ) were chosen to guarantee that the oscillation signal does not have distortion or signal lost.

$$\mathbf{F\_{c1}} = \frac{1}{2\pi \,\mathrm{R\_1C\_1}} \qquad \qquad \qquad \mathbf{F\_{c2}} = \frac{1}{2\pi \,\mathrm{R\_3C\_2}} \tag{1}$$

The sensor output, once amplified, is divided in two different components. One of them identifies the pressure signal of the pneumatic system (continuous component) and other signal identifies the oscillations that takes place in a pressure detection process (alternating component), coming from a heartbeat (aproximatelly 1 beat per second, corresponding to a frequency of 1 Hz).

The pressure signal is sent directly to channel 1 of an A/D converter input and at the same time the oscillatory signal is passed through a band pass filter to eliminate the continuous component and any noise coming from the electric network. After filtering, this signal is sent to channel 2 of the A/D converter. As soon as these signals are digitilized, the digital processing is executed by the microcontroller, and pressure data, systolic and diastolic pressure are left available for visualization in a display as well as sent to a GSM modem in text format message.

#### **4.3 Oximeter module**

A pulse oximeter is a medical instrument for monitoring blood oxygen concentration of patients in a non invasive way allowing measurement of heart beats. Oximeter operation is based on blood oxygen saturation identification, measuring the difference in absorption in two wavelenghts of light. When hemoglobin gets bonded with oxygen, oxygenated hemoglobin is formed and it becomes red. Otherwise, when dissociation of oxygen occurs in the hemoglobin, it becomes darker.

The variable that determines the hemoglobin oxygenation level in the bloodstream is called Oxygen Saturation, and is also known as SaO2. It is defined as the relationship between the oxygenated hemoglobin (HbO2) and total hemoglobin (Hb) in the blood. It is calculated with next equation (equation 2):

$$\text{CaO}\_2 = \frac{\text{HbO}\_2}{\text{Hb} + \text{HbO}\_2} \tag{2}$$

The pulse oximeter is built with a probe placed on a peripheral part of the body as a finger or ear. The probe is constructed using two led emitter diodes (LEDs), one of them operating with red light in the 660 nm light spectrum and the other one operating with infrared light in the 940 nm light spectrum. These LEDs are placed over one side of the probe, and a photodiode is placed on the opposite side of the probe (Wukitsch et al. ,1988).

The light beam emitted by the red and infrared diodes pass through the tissues, bones, veins and arteries, reaching the photodiode on the other side of the probe. The light absorbed by tissues and bones does not suffer significant variations during short time

As mentioned before, the inflation and deflation system is formed by a pneumatic pump and an electro valve. The pneumatic pump inflates the system, while the electro valve drains

The filter consists of two RC networks from which two cut off frequencies can be obtained (0,48 Hz and 4,8 Hz). These two poles (see equation 1 ) were chosen to guarantee that the

The sensor output, once amplified, is divided in two different components. One of them identifies the pressure signal of the pneumatic system (continuous component) and other signal identifies the oscillations that takes place in a pressure detection process (alternating component), coming from a heartbeat (aproximatelly 1 beat per second, corresponding to a

The pressure signal is sent directly to channel 1 of an A/D converter input and at the same time the oscillatory signal is passed through a band pass filter to eliminate the continuous component and any noise coming from the electric network. After filtering, this signal is sent to channel 2 of the A/D converter. As soon as these signals are digitilized, the digital processing is executed by the microcontroller, and pressure data, systolic and diastolic pressure are left available for visualization in a display as well as sent to a GSM modem in

A pulse oximeter is a medical instrument for monitoring blood oxygen concentration of patients in a non invasive way allowing measurement of heart beats. Oximeter operation is based on blood oxygen saturation identification, measuring the difference in absorption in two wavelenghts of light. When hemoglobin gets bonded with oxygen, oxygenated hemoglobin is formed and it becomes red. Otherwise, when dissociation of oxygen occurs

The variable that determines the hemoglobin oxygenation level in the bloodstream is called Oxygen Saturation, and is also known as SaO2. It is defined as the relationship between the oxygenated hemoglobin (HbO2) and total hemoglobin (Hb) in the blood. It is calculated with

���� <sup>=</sup> ����

photodiode is placed on the opposite side of the probe (Wukitsch et al. ,1988).

The pulse oximeter is built with a probe placed on a peripheral part of the body as a finger or ear. The probe is constructed using two led emitter diodes (LEDs), one of them operating with red light in the 660 nm light spectrum and the other one operating with infrared light in the 940 nm light spectrum. These LEDs are placed over one side of the probe, and a

The light beam emitted by the red and infrared diodes pass through the tissues, bones, veins and arteries, reaching the photodiode on the other side of the probe. The light absorbed by tissues and bones does not suffer significant variations during short time

�� �����

 F�� <sup>=</sup> �

�� ����

(1)

(2)

oscillation signal does not have distortion or signal lost.

 F�� <sup>=</sup> �

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this system.

frequency of 1 Hz).

text format message.

**4.3 Oximeter module**

next equation (equation 2):

in the hemoglobin, it becomes darker.

periods, and basically can be considered as a constant component of the signal (DC component). On the other hand, the signal associated with the arterial blood changes during short time intervals due to cardiac pulsation characterizing an AC component. With the DC and AC components identified it is possible to isolate the arterial blood component (Baura, 2002).

The oxygen saturation can be calculated accordingly with the next equation (equation 3):

$$\text{SAO}\_2 = \frac{\varepsilon\_{\text{d}\lambda 1} - \varepsilon\_{\text{d}\lambda 2}\,\text{R}}{[\varepsilon\_{\text{d}\lambda 1} - \varepsilon\_{\text{d}\lambda 1}] - [\varepsilon\_{\text{d}\lambda 2} - \varepsilon\_{\text{d}\lambda 2}]\,\text{R}} \tag{3}$$

Where: *1* and *<sup>2</sup>* are the wavelengths corresponding to the two wavelengths passing through the vascular body. *o<sup>1</sup>* is the absorptivity of oxygenated hemoglobin related with the wavelength *1*, *d<sup>1</sup>* is the absorptivity of desoxygenated hemoglobin. Similar nomenclature is valid for *<sup>2</sup>* wavelength.

Factor R in equation (3) can be found taking the ratio between AC component and DC component of the signal for both wavelengths *(1* e *2*) as follows:

$$R = \frac{\log\_{10}\left(\frac{lac}{lcc}\right)\lambda\_1}{\log\_{10}\left(\frac{lac}{lcc}\right)\lambda\_2} \tag{4}$$

Where: *iac <sup>1</sup>* is the current detected in the photodiode corresponding with the AC component of signal related with wavelenght *<sup>1</sup>*, *icc <sup>1</sup>* is the DC photodetected current related with wavelength *1*. Similar analysis is applied to the wavelength *<sup>2</sup>* parameter.

Equations (3) and equation (4) are the key to oximeter design and are calculated in the algorithm implemented in the microcontroller.

Figure 4 describes the architecture of the oximeter used to implement the protototype. An Oximeter signal, as usually done, is recovered using a transimpedance amplifier topology as shown in figure 5. This system includes biomedical signal acquisition units, signal conditioning unit (including amplification and filtering). The conditioning unit is connected with a microcontroller which, after signal digitalization, processes the signal and transmits the samples to a GSM modem. The modem sends processed information as text messages (SMS) to a cell phone or a remote database.

The transmitting and LED light intensity control is done via microcontroller that turns on and turns off the red and infrared LEDs in a frequency of 500 Hz. This frequency was chosen in order to extract the maximum power of the LEDs so they would have sufficient intensity to cross the exposed tissue with the probe. Thus, it is possible to apply maximum voltage to the LEDs in a short time interval.

The LEDs current is adjusted by a microcontroller in such a way that the photodiode current can be adjusted accordingly with the tissue sample of the patient. This situation might take place depending on the patient, making it neccessary to increase the LEDs current and therefore the photodetected current. The larger the tissue thickness is between emitter and receiver, the stronger the current through the LEDs should be. The LEDs luminosity is controlled by two PWM (Pulse Width Modulation*)* microntroller outputs. The conditioning circuitry used to process the photodetected signal is shown in figure 5.

Fig. 4. Diagram of the oximeter system architecture.

Fig. 4. Diagram of the oximeter system architecture.

Fig. 5. Schematic diagram of the processing stages used to recover the oximeter signal.

This final schematic was obtained after the necessary adjustments were done in order to have the oximeter signal totally conditioned before entering to the microcontroller A/D converter. All signal processing was simulated with Multisim usign this schemathic.

Stage 1 illustrates the photodetection and electonics for amplification. A transimpedance amplifier was used to amplify the generated current and to convert this current signal in a voltage signal. The cut off frequency was about 6 KHz (Feedback resistor and capacitor shown in stage 1 were tailored to get that frequency). This bandwidth is above the frequency of interest which is in the order of some Hertz, but it was decided to work with this frequency range to avoid using impractical values of *Rf*. The penalty with this approach is the need for additional filters to get the desired oximeter signal.

Second and third stages are Butterworth filters to eliminate the DC signal component as well as the LEDs modulation frequency (500 Hz). An additional low pass filter was included (stage 4) to avoid any noise influence into the system. Finally, there were included two stages (stage 5 and stage 6) for amplification and level shifting purposes, in such a way to make compatible the processed signal level with the analog to digital converter input of the microcontroller.

To illustrate the oximeter Multisim simulation that was conducted, simulation at first and last stages of circuit of figure 5 is presented. Figure 6 shows the simulation at stage 1.

Fig. 6. Plot from Multisim simulation at stage 1 of figure 5.

Photodiode signal was represented using an operational amplifier configured as a weighted adder with three input signals. A 100 mV – 500 Hz signal for LEDs driving. The second signal, a 100 mV – 2 Hz signal that represents the heart beat (120 beats per second), and a third signal representing a constant value, e.g., the non pulsating part of the arterial blood, corresponding to the signal through the bones and tissues as reported in Philips Electronics report (Philips Electronics, 2002).

Multisim simulation (figure 7) at stage 6, shows the signal obtained that finally goes into the microcontroller A/D input. Figure 7 show two traces. The trace in blue corresponds to the signal at stage 5 of figure 5, while the trace in yellow is the signal at stage 6. The only difference between these signals, is a level shifting introduced so yellow trace signal can be compatible with microcontroller A/D converter input.

Fig. 6. Plot from Multisim simulation at stage 1 of figure 5.

compatible with microcontroller A/D converter input.

report (Philips Electronics, 2002).

Photodiode signal was represented using an operational amplifier configured as a weighted adder with three input signals. A 100 mV – 500 Hz signal for LEDs driving. The second signal, a 100 mV – 2 Hz signal that represents the heart beat (120 beats per second), and a third signal representing a constant value, e.g., the non pulsating part of the arterial blood, corresponding to the signal through the bones and tissues as reported in Philips Electronics

Multisim simulation (figure 7) at stage 6, shows the signal obtained that finally goes into the microcontroller A/D input. Figure 7 show two traces. The trace in blue corresponds to the signal at stage 5 of figure 5, while the trace in yellow is the signal at stage 6. The only difference between these signals, is a level shifting introduced so yellow trace signal can be

Fig. 7. Plot from Multisim simulation at stage 6 of figure 5.

The system implemented is shown in figure 8. The prototype includes the following modules:

System operation can be described as follows: The conditioning circuit after proper filtering allows the capture of the oximetry signal which is then passed to main microcontroller. This microcontroller also has the task of driving the red and infrared LEDs of the probe in a frequency of 500 Hz.

After processing with the main microcontroller, processed information is sent to the GSM modem using the serial port of the prototype, as well as to the local display.

A second microcontroller (figure 9) was included in this system and is responsible for user interface tasks, allowing commutation between arterial pressure signals or oximetry signals measurements. Results regarding arterial pressure measurements with this prototype were published in (Serigioli, 2010). Also, this microcontroller controls all processing of the LCD display. Communication between microcontrollers is done through a communication protocol.

Fig. 8. Biomedical signals prototype: (1) Signal captured with oscilloscope; (2) Measurement of the signals with the prototype mounted inside a module; (3) Commercial oximeter probe used; (4) GSM modem used in experiment.

The GSM modem sends data using SMS to a cell phone for information management and communication. In this way, information can be sent as a text message using wireless networks to any cell phone that can be found in the area under covering. To establish communication between microcontroller and modem, AT commands were used. These commands are instructions used to control the modem (AT Commands Reference Manual, 2006).

After processing with the main microcontroller, processed information is sent to the GSM

A second microcontroller (figure 9) was included in this system and is responsible for user interface tasks, allowing commutation between arterial pressure signals or oximetry signals measurements. Results regarding arterial pressure measurements with this prototype were published in (Serigioli, 2010). Also, this microcontroller controls all processing of the LCD display. Communication between microcontrollers is done through a communication

Fig. 8. Biomedical signals prototype: (1) Signal captured with oscilloscope; (2) Measurement of the signals with the prototype mounted inside a module; (3) Commercial oximeter probe

The GSM modem sends data using SMS to a cell phone for information management and communication. In this way, information can be sent as a text message using wireless networks to any cell phone that can be found in the area under covering. To establish communication between microcontroller and modem, AT commands were used. These commands are instructions used to control the modem (AT Commands Reference Manual,

used; (4) GSM modem used in experiment.

2006).

modem using the serial port of the prototype, as well as to the local display.

protocol.

Fig. 9. Illustration of the prototype implemented in a printed circuit board: (1) DC power supply; (2) circuit for LEDs driving; (3) main microcontroller; (4) microcontroller to command the LCD display; (5) circuitry for oximetry signal processing.

Figure 10 illustrates the algorithm flowdiagram implemented in the microcontroller.

Fig. 10. Flowchart of the Program for microcontroller programing.

The user can select the biomedical signal of interest by pressing the proper button available. Button B1 selects the oximetry mode, and button B2 selects the arterial pressure mode. Button B3 enables the communication path to send the signals to the GSM modem. By selecting the oximetry mode, the microcontroller energizes the LEDs to initiate the oxygen blood saturation, SaO2 as was already described above in this section. When the arterial pressure mode is selected, the microcontroller makes the pressurization, depressurization of the pneumatic system as systolic and diastolic pressures can be determined as described in section 4.2. Then results can be visualized in the display, in local mode, or in a cell phone remotely.

Regarding microcontroller programming, code was written using the MPLAB IDE V8.36 environment from Microchip.

### **5. Modem GSM**

The GSM model (Global System for Mobile Communications) is a wireless solution that accesses the GSM network in the same way cell phones do. The GSM modem does not have the entire peripheral accompaniments that a cell phones might, for instance, keyboard, display, microphone, and speaker. With the lack of these kinds of resources, it is necessary to include other devices like the microcontroller.

The modem was used in this prototype to send biomedical signals that were captured to a cell phone using SMS messages available in a GSM network. Messages, as defined in the standard GSM 900/1800/1900, can have up to 160 ASCII characters. Text includes letters, numbers and alphanumeric characters. Modem used was the G24 from Motorola (Module Hardware Description - Motorola G24 Developer's Guide).

Main technical specifications of the modem are:

Quad band (850, 900, 1800 e 1900 MHz); Voltage: 3.3 to 4.2V Operation temperature: -20 to + 60 ºC; Currrent: up to 2,5mA; Connections: USB 2.0 and RS232 (300 bps a 460800 bps); Transmission speed: up to 85.6 Kbps; SMS: text mode and PDU; Commands AT (GSM 07.05, GSM 07.07 and GSM 07.10)

Communication between modem and prototype was done via serial communication. The configuration and resources utilization of G24 modem were implemented with commands called AT commands (AT Commands Reference Manual,Motorola G24 Developer's Guide). These commands can be written using a set of characters of the ASCII table, starting with an **"AT"** prefix. The prefix "AT" is derived from the word *attention*, and requests the modem to pay attention for a solicitation (command).

An AT command line might have one or more commands, separated by delimiters according to the structure shown below:

The user can select the biomedical signal of interest by pressing the proper button available. Button B1 selects the oximetry mode, and button B2 selects the arterial pressure mode. Button B3 enables the communication path to send the signals to the GSM modem. By selecting the oximetry mode, the microcontroller energizes the LEDs to initiate the oxygen blood saturation, SaO2 as was already described above in this section. When the arterial pressure mode is selected, the microcontroller makes the pressurization, depressurization of the pneumatic system as systolic and diastolic pressures can be determined as described in section 4.2. Then results can be visualized in the display, in local mode, or in a cell phone

Regarding microcontroller programming, code was written using the MPLAB IDE V8.36

The GSM model (Global System for Mobile Communications) is a wireless solution that accesses the GSM network in the same way cell phones do. The GSM modem does not have the entire peripheral accompaniments that a cell phones might, for instance, keyboard, display, microphone, and speaker. With the lack of these kinds of resources, it is necessary

The modem was used in this prototype to send biomedical signals that were captured to a cell phone using SMS messages available in a GSM network. Messages, as defined in the standard GSM 900/1800/1900, can have up to 160 ASCII characters. Text includes letters, numbers and alphanumeric characters. Modem used was the G24 from Motorola (Module

Communication between modem and prototype was done via serial communication. The configuration and resources utilization of G24 modem were implemented with commands called AT commands (AT Commands Reference Manual,Motorola G24 Developer's Guide). These commands can be written using a set of characters of the ASCII table, starting with an **"AT"** prefix. The prefix "AT" is derived from the word *attention*, and requests the modem to

An AT command line might have one or more commands, separated by delimiters

Prefix Command 1 Delimiter Command 2 Delimiter ... Command N Suffix

remotely.

environment from Microchip.

to include other devices like the microcontroller.

Main technical specifications of the modem are:

Quad band (850, 900, 1800 e 1900 MHz);

Operation temperature: -20 to + 60 ºC;

Transmission speed: up to 85.6 Kbps;

pay attention for a solicitation (command).

according to the structure shown below:

Hardware Description - Motorola G24 Developer's Guide).

Connections: USB 2.0 and RS232 (300 bps a 460800 bps);

Commands AT (GSM 07.05, GSM 07.07 and GSM 07.10)

**5. Modem GSM**

Voltage: 3.3 to 4.2V

Currrent: up to 2,5mA;

SMS: text mode and PDU;

Each AT command has an "AT" prefix and a suffix <CR>, called *Carriage Return*. The delimiter can be a comma or a space.

When a command is launched, the modem answers with a message, so called, *Result Code*.

An example of the writting of these commands, is as follows. See (table 1).

Table 1. Example of commands composition.

Among different commands available, it were used the commands set necessary to modem configuration, message composition and sending of messages. These commands are described below (table 2).

Table 2. Examples of commands used in this experiment.

The source code implemented in the microcontroller to establish serial communication between the microcontroller and the GSM modem is shown in the appendix.

#### **6. Results**

The initial step in the prototype implementation was a simulation with Multisim for each stage and for each subsystem (arterial pressure or oximeter circuit). Next, the electronics for the entire system was implemented in a board.

Signals corresponding with arterial pressure and oximetry are shown in figure 11 and figure 12 as obtained in tests conducted in the laboratory.

Figure 11(a) corresponds to the prototype for arterialpressure signal acquisition. It includes the MPX2050 pressure sensor, pump and electro valve. By activating the pump, the cuff is presurized up to the maximum pressure is achieved and when the electro valve is activated, the cuff is depressurized. With this cycle systolic and diastolic pressure is meassured.

Figure 11(b) corresponds to the arterial pressure signal, where the green trace (signal with negative slope) corresponds to the pressure sensor output when the pneumatic system is in deflation mode. The yellow trace is the oscillation corresponding to the pressure without the constant component (continuous component) that was extracted by filtering and amplified, as comparisons can be made by the microcontroller. These signals are connected individually with the channels of ADC conversion of the microcontroller.

Fig. 11(a). Arterial pressure Prototype for arterial pressure acquisition.

Fig. 11(b). Cuff Pressure Signal at the Output of the Pressure Sensor and Extracted Oscillation signal at the Output of the Amplifier.

Figure 11(a) corresponds to the prototype for arterialpressure signal acquisition. It includes the MPX2050 pressure sensor, pump and electro valve. By activating the pump, the cuff is presurized up to the maximum pressure is achieved and when the electro valve is activated, the cuff is depressurized. With this cycle systolic and diastolic pressure is meassured.

Figure 11(b) corresponds to the arterial pressure signal, where the green trace (signal with negative slope) corresponds to the pressure sensor output when the pneumatic system is in deflation mode. The yellow trace is the oscillation corresponding to the pressure without the constant component (continuous component) that was extracted by filtering and amplified, as comparisons can be made by the microcontroller. These signals are connected

individually with the channels of ADC conversion of the microcontroller.

Fig. 11(a). Arterial pressure Prototype for arterial pressure acquisition.

Fig. 11(b). Cuff Pressure Signal at the Output of the Pressure Sensor and Extracted

Oscillation signal at the Output of the Amplifier.

Fig. 12(a). Waveform of the oxymeter output as obtained in an oscilloscope

Fig. 12(b). Waveform of the oxymeter output.

Figure 12(a) corresponds to the oximetry signal observed in an oscilloscope. Results are available in a LCD display of the prototype in numerical format or as a waveform providing easy result interpretation for the health care workers (figure 12b).

After testing the functionality of the prototype, there were some tests conducted in order to verify the proper operation of the communication interface, especially the remote communication with a cell phone using the communication infrastructure available in our country. Results obtained indicated that the communication worked according to expectations with the oximetry and the arterial perssure data that were processed with the electronic module. Data sent via modem to a cell phone as an SMS message was received as intended. Figure 13 shows the results displayed on a cell phone for both, pressure and oximetry signals.

Fig. 13. Oxymeter and blood arterial pressure data sent to a cell phone via GSM communication.

#### **7. Conclusion**

We presented implementation of a prototype to acquire, process and transmit biomedical signals. It was designed eletronics for signal processing to accommodate arterial pressure signals as well as blood oxygen saturation.

Fig. 13. Oxymeter and blood arterial pressure data sent to a cell phone via GSM

We presented implementation of a prototype to acquire, process and transmit biomedical signals. It was designed eletronics for signal processing to accommodate arterial pressure

communication.

**7. Conclusion**

signals as well as blood oxygen saturation.

Quantitative results for SaO2 calculations were processed in a microcontroller from which, the results were made available using a LCD display to local visualization, or sent to a remote location, where results can be obtained in a cell phone.

The electronic module implemented is able to monitor two different kinds of biomedical signals, markedly, arterial blood pressure, and blood oxygen saturation.

Results obtained with this prototype demonstrates that it is quite possible to construct an infrastructure based on mobile communication, where data coming from biomedical equipment, can be made available remotely to medical specialists, to readily obtain precise diagnostics of a patient´s health. In this way, proper decisions can be drawn as preventive diagnostics, or as a diagnostic tool in a real emergency situation where a patient should be taken to a hospital for adequate medical assistance.

We hope this experiment will draw the attention of the government agencies of our country to give support to this kind of initiatives that could be benefical for many people, especially for people living in rural areas, or areas with scarce medical support.

Besides other vital signs that could be included in this system, it is necessary to develop other important part of an m-Healt system. For instance, the organization of a database in a server where patient´s collected information can be viewed and managed by clinicians.

From the point of view of global m-Health´s progress and trends, the trends of m-Health, are changing as pointed out in a study of Manhattan Research: "information gathering either from simply searching for health information or news—remains the most common m-Health behavior. However, that trend is shifting. The study found that three percent of consumers used prescription drug refills or reminders on their mobile phones in 2010, a number that inched to eight percent in 2011". The report also found that about 56 million U.S. consumers had accessed their medical information electronically by utilizing an electronic health record (EHR) system maintained by their physician. An additional 41 million people expressed an interest in accessing EHR's in the future.

The progress of m-Health as can be accessed through different demonstrations worldwilde is astonishing. First demonstrations took advantage of 3G communication technologies to deliver care services and information to the patient. In the coming years m-Health will benefit from 4G data rate transfer allowing applications with image and voice as medical teams will interact with first responders to take proper decisions and at the same time, make the necessary preparations for the treatment before the patient´s arrival. Also, 4G technology will allows reaching rural areas and will make easier the m-Health Programs implementation in less developed parts of the world.

GPS-based directions to the closest, and most appropriate, medical facility for the patients condition is another benefit in todays´s m-Health services.

#### **8. Appendix**

Described below is part of the microcontroller source code responsible for communication between the microcontroller and GSM modem. This code was written in C programming language and implemented in MPLAB IDE V 8.36 with C18 compiler.

```
#include <P18f4550.h >
#include < delays.h >
#include < usart.h >
void()
{
char cm_ini[]="AT"; // matrix type CHAR that stores AT command
char cm_texto[]="AT+CMGF = 1"; // matrix type CHAR that stores the command of
                          // configuration of messages in text format.
char cm_sms[]="AT+CMGS =81199644,145" // matrix type CHAR that stores the command
                                 // to send a message to destination phonenumber
// Configuration of serial communication of microcontroller to work in asynchronous mode
//with 8 bits and with speed of 9600bps.
USART_TX_INT_OFF & USART_RX_INT_OFF & OpenUSART USART_ASYNCH_MODE & USART_EIGHT_BIT & USART_CONT_RX

                                                      ; & USART_BRGH_HIGH , 25
//************* Modem startup*******************
putsUSART cm_ini ; // Send to the modem the "AT" command;
while (BusyUSART()); // Wait until all data is sent.
putcUSART 0x0D ; /

                          / Send to the modem the command CR carriage return
while (BusyUSART());
Delay1KTCYx 1 ; // Wait for 1ms
putsUSART cm_texto ; //Sends to the modem the command "AT+CMGF = 1"
while (BusyUSART());
putcUSART 0x0D ; //

                            0D in ASCII table corresponds to CR
while (BusyUSART());
while 1
{
//**************************** Send to the modem ************
if b3 // IF button b3 is pressed
{
putsUSART cm_sms ; // Send to the modem the comm

                                                  and"AT+CMGS = 81199644,145"
while (BusyUSART());
putcUSART 0x0D ;
Delay1KTCYx 1 ;
// Send to the modem the content of two variables, minimum and maximum,
corresponding to oximeter and arterial pre

                                     ssure signals.
fprintf stdout,"SAO2 = %d, Diastolica = %d,Sistolica = %d",sao2, minima,maxima ;
while (BusyUSART());
putcUSART 0x1A ; // Send to the modem thecommand CTRL+Z
while (BusyUSART());
}}}
```
Initially it was criated CHAR type matrices to store the AT commands after the USART configuration, asynchronous mode, with 8 bits of data and 9600 bps. To initialize the modem, it was sent through the serial interface of the microcontroller the "AT" characters followed by the CR command. After this, it was sent the "AT + CMGF = 1" command followed by the CR command. These commands are necessary for proper text mode modem configuration. After startup, the program remains in an infinite loop until the moment that the B3 button is pressed. At that moment, the microcontroller sends through the serial interface, a command with the destination telephone number ("AT +CMGS= ") and finally, the variables containing the desired information (data of the patients health) are sent. To close the process for sending the message, the command CRTZ+Z is used.

#### **9. References**

182 Advances and Applications in Mobile Computing

// configuration of messages in text format.

/ Send to the modem the command CR carriage return

// to send a message to destination phonenumber

; & USART\_BRGH\_HIGH , 25

and"AT+CMGS = 81199644,145"

char cm\_ini[]="AT"; // matrix type CHAR that stores AT command

USART\_TX\_INT\_OFF & USART\_RX\_INT\_OFF & OpenUSART USART\_ASYNCH\_MODE & USART\_EIGHT\_BIT & USART\_CONT\_RX

putsUSART cm\_ini ; // Send to the modem the "AT" command; while (BusyUSART()); // Wait until all data is sent.

//\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\* Send to the modem \*\*\*\*\*\*\*\*\*\*\*\*

// Send to the modem the content of two variables, minimum and maximum,

putcUSART 0x1A ; // Send to the modem thecommand CTRL+Z

Initially it was criated CHAR type matrices to store the AT commands after the USART configuration, asynchronous mode, with 8 bits of data and 9600 bps. To initialize the

fprintf stdout,"SAO2 = %d, Diastolica = %d,Sistolica = %d",sao2, minima,maxima ;

if b3 // IF button b3 is pressed

putsUSART cm\_sms ; // Send to the modem the comm

//\*\*\*\*\*\*\*\*\*\*\*\*\* Modem startup\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*

Delay1KTCYx 1 ; // Wait for 1ms

corresponding to oximeter and arterial pre

//with 8 bits and with speed of 9600bps.

while (BusyUSART());

while (BusyUSART());

while (BusyUSART());

{

}}}

while 1 {

while (BusyUSART());

while (BusyUSART());

while (BusyUSART()); putcUSART 0x0D ; Delay1KTCYx 1 ;

putcUSART 0x0D ; //

char cm\_texto[]="AT+CMGF = 1"; // matrix type CHAR that stores the command of

char cm\_sms[]="AT+CMGS =81199644,145" // matrix type CHAR that stores the command

putcUSART 0x0D ; /

putsUSART cm\_texto ; //Sends to the modem the command "AT+CMGF = 1"

// Configuration of serial communication of microcontroller to work in asynchronous mode

0D in ASCII table corresponds to CR

ssure signals.

#include <P18f4550.h > #include < delays.h > #include < usart.h >

void() {

## **Designing an Interactive Museum Guide: A Case Study for Mobile Software Development**

Dilek Karahoca1,2 and Adem Karahoca1

*1Bahçeşehir University Software Engineering Department 2Near East University Computer Technology and Instructional Design PhD Program Department Turkey*

#### **1. Introduction**

184 Advances and Applications in Mobile Computing

Li W., Luo Y., Chang Y., & HsianY. (2010). A Wireless Blood Pressure Monitoring System for

Manhattan Research, (2011). Available in http://manhattanresearch.com/News-and-

Murthy, M.V. Ramana, (2008). Mobile Based Primary Health Care System for Rural India,

Mulvaney D., Woodward B., Datta S., Harvey P., Vyas A., Thakker B., & Farooq O. (2010).

Peura R. A. (1998). Blood Pressure and Sound. *Medical Instrumentation: application and design*,

Philips Electronics North America Corporation. (2002). Understanding Pulse Oximetry SpO2

Sanderson David, Gronlund Jay, (2010). The future of mHealth – About to Explode But Key

Serigioli N., Muñoz R. R. & Rodriguez E. C. (2010). Biomedical Signals Monitoring Based in

Vital Wave Consulting. (2009). *mHealth for Development: The Opportunity of Mobile Technology*

Wukitsch, M. W.; Petterson, M.T.; Tobler D.R. & Pologe, J.A.(1988) Pulse oximetry: analysis

Mobile Computing. *32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society*, pp. 3868-3871, ISSN 978-1-4244-4124-2. Buenos Aires,

*for Health care in the Developing World. Washington*, D.C. and Berkshire, UK: UN

Events/Press-Releases/ehr-consumer-online-medical-records.

Buenos Aires, Argentina, November 2010.

mobile-healthcare-paper.pdf.

pp 287-329, ISBN 0-471-15368-0. USA.

Concepts*. Philips Medical Systems*, 2002.

Argentina, November 2010.

Challenges Remain, Global Partners Inc, Feb. 2010.

Foundation-Vodafone Foundation Partnership.

of theory, technology and practice. J Clin Monit. pp-209-301.

Madrid, Spain.

Personal Health Management, *32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society*, pp. 2196-2199, ISSN 978-1-4244-4124-2.

(2008), Available from http://www.w3.org/2008/02/MS4D\_WS/papers/cdac-

Mobile communications for Monitoring Heart Disease and Diabetes, *32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society*, pp. 2208-2210, ISSN 978-1-4244-4124-2. Buenos Aires, Argentina, November 2010. Pérez-Ferre N. & Calle-Pascual A. (2011). Overview of Telemedicine Applications in the

Follow-Up of the Diabetic Patient, In: *Advances in Telemedicine: Application in Various Medical Disciplines and Geographical Regions*, pp. 71-86, ISBN 978-953-307-161-9,

> Portable devices disseminate the information sharing without encountering the location barriers. By the great enhancements of mobile phones, it has been changed the direction of computing to the people centric computing (Lane et al. (2006), Shi et al. (2010)). Mobile phones have advantages to support mobile learning (m-learning) through the use of wireless mobile technology to allow learners to access information from anywhere and at any time (Ally, 2009). M-learning can be defined as "using mobile and interactive technologies to transfer information and knowledge with different contexts" (Sharples et al., 2007).

> Mobile phone has become a part of the personalized computing. Nowadays everyone has a strong loyalty to use these devices. Personalization helps users to identifying his intentions, and trends about a specific area. In the life-long learning activities, museums have interested to provide information from ancient history to modern ages. This viewpoint emphasizes the importance and role of the museums and their accessibility. By using mobile learning platform and its features, we can share information about the museums not only with audio guides about art objects but also the location of the museums and places located in near environment. Also, interactive tools can provide interfaces for users to access some art objects which located in a specific museum by help of the mobile solutions.

> Lifelong learning process (LLP) covers learning knowledge and gaining new skills throughout his life and focuses on the post-educational period. In LLP, individuals have to be self-motivated and voluntary to learn themselves. Lifelong learning materials have to be designed for different age groups and different levels. Visiting a museum and obtaining knowledge from its exhibition is one of the suitable areas for lifelong learning. To provide the efficiency for the museum visiting, mobile museum guide will be a good way to directed learners (Aspin and Chapman, 2000).

> The goal of this study is to develop an application that will provide information about museum guide, using smart mobile devices. This chapter presents the architecture and implementation of such a location-based application, called Mobile Museum Guide, with the mobile phone as a platform. Implemented on Android, the system is designed to provide museum guide information services; therefore, people can get museum guidance information that they need anytime and anywhere.

In the beginning of the 2000's, tourist guides or museum guides need to develop specific devices to provide information about art objects, finding a location about an art gallery, and providing detailed information about a particular art object. But now, rapidly increasing mobile phone market provides smarter phones that have abilities to access any portal by using Internet or executing specific software tools to push or pull some important information.

In this study, mobile devices are used for creating a mobile museum guide for exhibition contexts to improve the visiting experience, increase learning, and satisfying interests. In this book chapter, it will be described the design, implementation and deployment of a location-based application, named mobile museum guide for the mobile platform. This application will permit users to get museum guidance information when they need anytime and anywhere. In particular, the users could be browsed or queried through an Internet map service such as Google Maps.

This study includes following functional items:

From the functional viewpoint, following manifesto items are the aim of this project for visitors (Chou et al., 2004):

Forthcoming sections are considering consecutively about mobile technologies for museums, a mobile museum guide analysis, design, and development stages. Also at last, conclusions and future directions are given.

#### **2. Mobile technologies for museums**

Smart phones and tablets take place everywhere as mobile partners. These mobile devices have accessibility to Internet and wireless nets. When we look at the pros and cons of these devices, mobility and accessibility as portability seems to be advantages, but, small screen sizes, prices, heterogeneity in mobile applications, and constraints in presenting multimedia information gives a negative impression. For implementing these devices in museums,

In the beginning of the 2000's, tourist guides or museum guides need to develop specific devices to provide information about art objects, finding a location about an art gallery, and providing detailed information about a particular art object. But now, rapidly increasing mobile phone market provides smarter phones that have abilities to access any portal by using Internet or executing specific software tools to push or pull some important

In this study, mobile devices are used for creating a mobile museum guide for exhibition contexts to improve the visiting experience, increase learning, and satisfying interests. In this book chapter, it will be described the design, implementation and deployment of a location-based application, named mobile museum guide for the mobile platform. This application will permit users to get museum guidance information when they need anytime and anywhere. In particular, the users could be browsed or queried through an Internet

Get real-time directions to your next exhibit, a café, or anywhere else in the Museum by

Learn more about items from the Museum's collection, during your visit or browse

Want to learn more? Bookmark an item and receive a link to more information when

From the functional viewpoint, following manifesto items are the aim of this project for

 Mobile application should present information about any art objects or collection without requesting or searching information about it. Information should be provided

Museum exhibition should be enjoyable with accessing different activities and places by

Forthcoming sections are considering consecutively about mobile technologies for museums, a mobile museum guide analysis, design, and development stages. Also at last,

Smart phones and tablets take place everywhere as mobile partners. These mobile devices have accessibility to Internet and wireless nets. When we look at the pros and cons of these devices, mobility and accessibility as portability seems to be advantages, but, small screen sizes, prices, heterogeneity in mobile applications, and constraints in presenting multimedia information gives a negative impression. For implementing these devices in museums,

information.

map service such as Google Maps.

using the smallest route.

you get home.

visitors (Chou et al., 2004):

as location based services.

conclusions and future directions are given.

**2. Mobile technologies for museums**

mobile application.

This study includes following functional items:

exhibits from anywhere in the world.

Find your current location within the Museum and explore the map.

 Choose from Museum highlights or in-depth guided tours. Plan your own tour before you arrive or on the spot.

Share an interesting exhibit through email, Facebook, or Twitter.

Mobile application should provide deep information about art objects,

smart devices' connectivity features have to be taking into account to providing exhibition, and guide information to visitors. When the mobile devices are concern point, location based services may be taking into account. New generation smart phones have support the GPS functionality. Thus, location based services can be a trigger the visitor to getting any information near to him.

For example, portable devices such as personal digital assistants were used to provide interactive museum guidance. Chou et al. (2004) constructed a platform to support PDAs to give information about special art objects by using text, voice or video clip. Contextawareness is the popular human computer interaction discipline. A context-aware system defined as a system that "uses context to provide relevant information and/or services to the user, where relevancy depends on the user's task" (Dey and Abowd, 2000). The presentation of these services or information brings with it the issue of proactiveness – should the system initiate the presentation of information or should it wait for the user to request information (Lanir, 2011). Context-aware systems might support three categories of features: (1) presentation of information and services to the user; (2) automatic execution of a service; and (3) tagging of context to information for later retrieval (Dey and Abowd, 2000). In a related project, Ghiani et al (2009) propose a multi device, location aware museum guide which able to read RFID tags. Context-dependent information presented to the visitor via his location and historical behaviors. Proactiveness of mobile visitors' guides brings with it a possibility for better service to the user at the cost of taking control out of the user's hand (Lanir et al., 2011)

As emphasized here that, smart phones and tablets can be used to support interactive museum guidance. By using Wi-Fi, 3G or another network connection method, visitors can access to bring exhibition related information and special event notifications. Also, GPS (Global Positioning System) provides mobile user location via satellite. By using the location data, location awareness can be obtained. For instance, "special exhibitions near to you alerts" can be produced for museum visitors. Floor plans of the museum and location information of the visitor may be matched and, some adaptive guidance can be generated. As illustrated in Fig. 1, museum collections and art objects categorization data is located in DB server and business logic related issues located in application server, web server handle the requests which are coming from the web and mobile clients (Xiehao et al., 2009). By using this network topology, geospatial data can be integrated with multimedia data. This architecture can present the location based services to the museum visitors. Location based service creates location and multimedia data relations to provide information.

#### **3. A Mobile application for achieving museums: Mobile museum guide (MMG)**

Mobile museum guide development is the main idea of this study. In this section, mobile software development stages are detailed. MMG development project is organized base on the smart phone abilities. Functional and process based analysis is performed consecutively.

Software engineering encapsulates different techniques and models to support successful project management. Models and methodologies describe any system by using different notation, association semantics, and relationship between activities. Software methodology like UML covers all requirements engineering activities by using different diagraming techniques to enforce the software quality assurance. UML diagrams generate different documents and models: requirements specification documents, analysis models, designs, code, testing specifications and reports and so on. These documents and models are the inputs for the related forthcoming and planned activities that reside in software development life cycle (Jacobsen et al., 1998).

Following sub sections have details about mobile software development life cycle and its stages.

#### **3.1 Mobile software development life cycle**

Mobile software development life cycle is same as traditional software development and its life cycle. But it has some different aspects such as in duration of the software development time. Rapidly increasing mobile devices and mobile software tools enforce the minimization of the development time of the mobile software. But, common development platforms, such as iOS, Android, Black Berry, Symbian, Windows Mobile, et al., support same capabilities with different efforts.

In this study, activities of the software development life cycle are taking into account as: "Analysis", "Development ", "Testing/Training", "Documentation" and "Project go live".

At the analysis phase, system requirements analysis is performed and detailed the technical requirements of the software. This allows real time update of content, such as exhibition information of the museums, guided tours, special event notification and great flexibility of information. Also, it was decided to using Android platform which is the number one mobile platform for more easily learning and developing mobile applications.

Second work package of the analysis phase covers identifying processes for museum learning system modules. In this stage, design prototypes were prepared by Balsamiq software tool and UML diagrams drawn. At the development phase, we developed all modules for Android devices. At the testing phase, all modules were tested with Android supported device. At the last stage, review and acceptance of the study realized.

In this study, we might summarize following work packages in developing and implementing mobile museum guidance:

188 Advances and Applications in Mobile Computing

documents and models: requirements specification documents, analysis models, designs, code, testing specifications and reports and so on. These documents and models are the inputs for the related forthcoming and planned activities that reside in software

development life cycle (Jacobsen et al., 1998).

Fig. 1. Mobile users and accessible networks (Xiehao et al., 2009)

**3.1 Mobile software development life cycle**

stages.

live".

with different efforts.

Following sub sections have details about mobile software development life cycle and its

Mobile software development life cycle is same as traditional software development and its life cycle. But it has some different aspects such as in duration of the software development time. Rapidly increasing mobile devices and mobile software tools enforce the minimization of the development time of the mobile software. But, common development platforms, such as iOS, Android, Black Berry, Symbian, Windows Mobile, et al., support same capabilities

In this study, activities of the software development life cycle are taking into account as: "Analysis", "Development ", "Testing/Training", "Documentation" and "Project go

At the analysis phase, system requirements analysis is performed and detailed the technical requirements of the software. This allows real time update of content, such as exhibition information of the museums, guided tours, special event notification and great flexibility of information. Also, it was decided to using Android platform which is the number one

mobile platform for more easily learning and developing mobile applications.

Deploying the application.

Fig. 2. Example Work Breakdown Structure (WBS)

#### **3.2 Model driven analysis activities for developing MMG**

Analysis activities start with investigating end users' (visitors) expectations from the MMG. As described by Richardson et al. (1998), interface development stage can be supported with task analysis and designers need to work detailed specification of requirements for the interface. For satisfying, functional and non-functional behavior of the proposed system, software specification has to be organized based on the hierarchical task analysis (HTA). Stanton (2006) proposes that HTA provides an opportunity to the analyst to plan tasks not only the designer but also end user viewpoint. Annett (2006) emphasize that "*HTA encourages the analyst to consider not only what should happen, but also what does actually happen and how this can go wrong. He suggests that these questions will arise naturally as the analyst seeks*  *to discover the indicators for success and failure of each of the sub-goals*" (Stanton, 2006). In investigating user requirements, task analysis, specially HTA might be a useful tool for system analysts and system designers (Stanton, 2004).

In interactive system design, task analysis has to take into account the end user profile and system usage. Ormerod and Shepherd (2004) proposes sub goal template to obtain requirements specifications by a task analysis.

Here, HTA analysis is performed to obtain the main skeleton of the application. As illustrated in Fig. 3, language selection, museum searching, creating favorites list, and items info are the main tasks for the MMG application. "Search Museum" function is the main task of the application and it has criteria for filtering museums by map, city, type, culture, term, era, or a year.

Fig. 3. Hierarchical task analysis and menu items

Use case driven approach is well known and used in the requirements elicitation phase of software development life cycle (Jacobson, 1992).

Hsu et al. (2012) propose a touristic trip forecasting and intelligent recommendation system. Also, they use Google Maps API to allow the user to adjust the geographic data according to personal needs. Google Maps API well accepted for connecting geospatial data with business data. Also in this study, transportation to the museum and directions are provided by using Google Maps API. After the selection process of the museum, extra information and features are presented to the visitors.

In Table 1, fourteen different use cases are listed to show all details of the MMG. In use case model 1, following steps are followed;

User runs the application,

190 Advances and Applications in Mobile Computing

*to discover the indicators for success and failure of each of the sub-goals*" (Stanton, 2006). In investigating user requirements, task analysis, specially HTA might be a useful tool for

In interactive system design, task analysis has to take into account the end user profile and system usage. Ormerod and Shepherd (2004) proposes sub goal template to obtain

Here, HTA analysis is performed to obtain the main skeleton of the application. As illustrated in Fig. 3, language selection, museum searching, creating favorites list, and items info are the main tasks for the MMG application. "Search Museum" function is the main task of the application and it has criteria for filtering museums by map, city, type, culture,

Use case driven approach is well known and used in the requirements elicitation phase of

Hsu et al. (2012) propose a touristic trip forecasting and intelligent recommendation system. Also, they use Google Maps API to allow the user to adjust the geographic data according to personal needs. Google Maps API well accepted for connecting geospatial data with business data. Also in this study, transportation to the museum and directions are provided by using Google Maps API. After the selection process of the museum, extra information

system analysts and system designers (Stanton, 2004).

requirements specifications by a task analysis.

Fig. 3. Hierarchical task analysis and menu items

software development life cycle (Jacobson, 1992).

and features are presented to the visitors.

term, era, or a year.

Second use case includes museum searching steps:

Main concept of the designed system listed in the second use case model and illustrated in Fig.4.

Fig. 4. Use Case Modeling diagram for searching museums

Functional requirements can be best obtained by using use case models (Jacobsen et al., 1992; Jacobsen et al., 1998). Business analysts and system analysts prefer to prepare the use case models which include more practical business rules and descriptive statements of natural language. As mentioned before, use case modeling helps analysts to develop functional requirements and task models such as HTA or sub goal templates (SGT) are feeding user interface design stage. In the specification of use cases, the described activities are performed by entity when interacting with an actor (here end user or visitor).

Use cases, introduced by Jacobsen et al. (1992), versus task descriptions to identify sequences of actions, considering variants to generate a response from system to an actor (Booch, 1999). The definition emphasized that actions performed by system not user. The UML diagram reflects this, showing use cases as bubbles inside the system. Task descriptions don't cover

Quality requirements such as response time and usability, but they point out where quality is crucial (Lauesen, 2003). As Lauesen (2003) declared that, a UML use case diagram, which deals only with the computer system's actions and separate human and computer actions; but the task descriptions, which do not separate human and computer actions. This feature supports the requirements engineering activities based on the object oriented analysis and design side.

Table 1. Use cases for MMG

One of the most important components of UML is the class diagram, that model the information on the domain of interest in terms of objects organized in classes and relationships between them. UML class diagrams allow for modeling, in a declarative way, the static structure of an application domain, in terms of concepts and relations between them (Fowler and Scott, 1997).

A UML class diagram represents a set of objects with common features. A class has a rectangle and top the shape consist the name of the class. The attributes of the class diagram are denoted in the second cell of the rectangle. Third cell consists of the operations that associated to the object of the class (Berardi et al., 2005). N-ary association shows more than three connections between classes. Figure 5 illustrates the class diagram for MMG study.

Fig. 5. Class diagram for MMG

192 Advances and Applications in Mobile Computing

case models which include more practical business rules and descriptive statements of natural language. As mentioned before, use case modeling helps analysts to develop functional requirements and task models such as HTA or sub goal templates (SGT) are feeding user interface design stage. In the specification of use cases, the described activities

Use cases, introduced by Jacobsen et al. (1992), versus task descriptions to identify sequences of actions, considering variants to generate a response from system to an actor (Booch, 1999). The definition emphasized that actions performed by system not user. The UML diagram reflects this, showing use cases as bubbles inside the system. Task

Quality requirements such as response time and usability, but they point out where quality is crucial (Lauesen, 2003). As Lauesen (2003) declared that, a UML use case diagram, which deals only with the computer system's actions and separate human and computer actions; but the task descriptions, which do not separate human and computer actions. This feature supports the requirements engineering activities based on the object oriented analysis and design side.

User get real time direction to museum

User get transportation option (taxi, bus, on foot) Use Case 3 User search museums with some criteria (this criteria: by map, by city, museum type, culture, term, era, year)

Use Case 5 User display museum search result with some criteria (display all, A

Use Case 11 User display museum calendar (what's happening today, tomorrow

Use Case 12 User gets detailed information about selected museum (address,

Use Case 13 User searches alternative restaurant, café, shops near the selected

Use Case 14 User share museum opinion with social network (Facebook, Twitter

One of the most important components of UML is the class diagram, that model the information on the domain of interest in terms of objects organized in classes and relationships between them. UML class diagrams allow for modeling, in a declarative way,

User get distance to museum

to-Z, area, distance, popularity) Use Case 6 User display overview about selected museum and rate it.

Use Case 4 User adds favorite museums to favorite page.

Use Case 7 User display selected museum's exhibitions. Use Case 8 User display selected museum's floor plan. Use Case 9 User downloads selected museum's application. Use Case 10 User display selected museum's multimedia tours.

and this weekend)

museum.

etc.)

Table 1. Use cases for MMG

phone, web site address etc.)

Find your current location and Museum location, explore the map.

are performed by entity when interacting with an actor (here end user or visitor).

descriptions don't cover

Use Case 2

Use Cases Description

Use Case 1 User changes language

Dynamic behavior of a system can be modeled in UML based sequence diagrams and proposed that at least a sequence diagram have to be drawn for per Use Case to describe the behavior of the sub system more efficiently (Jacobsen et al., 1992). By using the Use Case's description, first version of the sequence diagram can be prepared. In the design process, the Use Case diagram or the Sequence diagram might be updated till fine and tuning satisfied (Larman, 1998). A sequence diagram has two parts, the vertical side represents time, and the horizontal side represents the different classes. Time proceeds down the page and, there is no significance to the horizontal ordering of the object types (Jacobsen et al., 1992). In figure 6, sequence diagram of museum search is depicted.

Fig. 6. Sequence Diagram - User searching museum by city name

#### **3.3 User interface designing activities for MMG**

Usability of mobile device depends on the size. Shneiderman's "Golden Rules for interface design" covers eight interface design guidelines (Shneiderman, 1998). But four of them can directly be applied to the mobile devices;

Gong and Taraswewich (2004) propose modifications on other four design guidelines.

Following mock-up designs are constructed by considering the modified Golden Rules for interface design.

Fig. 6. Sequence Diagram - User searching museum by city name

Usability of mobile device depends on the size. Shneiderman's "Golden Rules for interface design" covers eight interface design guidelines (Shneiderman, 1998). But four of them can

Gong and Taraswewich (2004) propose modifications on other four design guidelines. Consistency (create input/output methodologies which are device independent), Reversal of actions (mobile applications should rely network connectivity as little as

Reduce short-term memory load (recognition of function choices instead of

Following mock-up designs are constructed by considering the modified Golden Rules for

**3.3 User interface designing activities for MMG**

Error prevention and simple error handling,

memorization of commands),

directly be applied to the mobile devices; Enable frequent users to use shortcuts,

 Offer informative feedback, Design dialogs to yield closure, Support internal locus of control.

possible),

interface design.

#### **3.4 Usability analysis based on the ISO9241-11**

The development of mobile applications has some important limitations; such as, size of the screen, memory and processing capacities of the smart phones. For these reasons, different design decision for the tasks has to be taking into account (Rauch, 2011). Nielsen (2011) reports that the mobile user experience improving slightly 59% to 62% and these success rates obtained by computing correctly versus incorrectly done tasks. Also, shown that, acceptance of mobile applications are better than mobile site. Even web site is good; success rate of mobile application usage is 76% and 64% for web site success rate.

These feedbacks show that mobile applications may suffer from the usability and design issues. To handle design issues effectively, mobile interface analysis and design stages have to be supported with mock-up usability tests. As Nielsen (2009) reported that, since WAP phones (2000), there is not progress in task performance. He conducted two simple tasks to users and expected that current phones have improvement from usability perspective. But, results show that feature phones (regular cellular phone) have worst user experience when accessing web sites. Smart phones and touch phones have average success rates 55% and 75% respectively when accessing web sites. Bigger screen of the mobile phones provides better user experience (Nielsen, 2009).

Table 2. ISO9241-11 Usability results

From these viewpoints, here, usability analysis conducted with five expert users with "search museums nearby here" scenario. In ISO9241 usability standard, twenty-one criteria can be used to measuring the usability and the possible ways to set the worst/best case and planned/now-level targets. These measurements are named as usability metrics. We now describe the development and evaluation of usability trails for MMG with five HCI experts to access on smart phones by performing ISO 9241-11 usability standard. Parameters and average trail results of the interfaces are listed in Table 2 for five experts.

#### **4. Conclusion**

As a result, best way to provide information to people, seems to be mobile phones. But these devices have some miserable aspects such as screen size. To provide information efficiently and more rapid way, design aspects have to be consider for different sized mobile phones. Even mobile applications have usability advantages when compared with web sites; but also, they have also limitations, because of the design shortcomings. This chapter shows that mobile application development stages can be modeled by model driven software development in software development life cycle. Mobile applications development platforms and programming languages can be different. But, main design idea is same; keep it simple, accessible and minimal. Complex and confused design strategies might be hazardous for mobile applications and web sites.

#### **5. Acknowledgment**

The authors would like to thank to Miss Hafize Arabul for designing the prototypes of the MMG.

#### **6. References**

From these viewpoints, here, usability analysis conducted with five expert users with "search museums nearby here" scenario. In ISO9241 usability standard, twenty-one criteria can be used to measuring the usability and the possible ways to set the worst/best case and planned/now-level targets. These measurements are named as usability metrics. We now describe the development and evaluation of usability trails for MMG with five HCI experts to access on smart phones by performing ISO 9241-11 usability standard. Parameters and

As a result, best way to provide information to people, seems to be mobile phones. But these devices have some miserable aspects such as screen size. To provide information efficiently and more rapid way, design aspects have to be consider for different sized mobile phones. Even mobile applications have usability advantages when compared with web sites; but also, they have also limitations, because of the design shortcomings. This chapter shows that mobile application development stages can be modeled by model driven software development in software development life cycle. Mobile applications development platforms and programming languages can be different. But, main design idea is same; keep it simple, accessible and minimal. Complex and confused design strategies might be

The authors would like to thank to Miss Hafize Arabul for designing the prototypes of the

Ally, M. (2009). Mobile Learning: Transforming the Delivery of Education and Training, pp.

Annett, J.(1996). Recent developments in hierarchical task analysis, S.A. Robertson (Ed.), Contemporary Ergonomics, Taylor & Francis, London, pp. 263–268. Aspin, D. N.; Chapman, J.D.(2000). Lifelong learning: concepts and conceptions, International Journal of Lifelong Education, vol. 19, no. 1, pp. 2-19, 2000. Berardi, D.; Calvanese,D.; Giacomo,G.D.(2005). Reasoning on UML class diagrams,

Booch, G.; Rumbaugh, J.; Jacobson, I. (1999). The Unified Modeling Language User Guide,

Chou, L.-D. Lee, C.-C. Lee, M.-Y., Chang,, C.-Y. (2004). A Tour Guide System for Mobile

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Fowler, M.; Scott, K. (1997). UML Distilled—Applying the Standard Object Modeling

Artificial Intelligence, Vol.168, No:1–2, pp.70-118, ISSN 0004-3702,

Learning in Museums, The 2nd IEEE International Workshop on Wireless and

awareness, CHI 2000 workshop on the what, who, where, when, and how of

average trail results of the interfaces are listed in Table 2 for five experts.

hazardous for mobile applications and web sites.

1, ISSN 1919-4390, Edmonton, Canada.

Mobile Technologies in Education (WMTE'04).

Laguage, Addison-Wesley, Reading, MA, 1997.

10.1016/j.artint.2005.05.003.

Addison-Wesley, Boston.

context-awareness, pp. 1–6.

**4. Conclusion**

**5. Acknowledgment**

MMG.

**6. References**

http://www.useit.com/alertbox/mobile-usability-study-1.html

## **CARS-AD Project: Context-Aware Recommender System for Authentication Decision in Pervasive and Mobile Environments**

João Carlos Damasceno Lima1,2, Cristiano Cortez da Rocha3, Iara Augustin2 and Mário Antônio Ribeiro Dantas1 <sup>1</sup>*Federal University of Santa Catarina* <sup>2</sup>*Federal University of Santa Maria* <sup>3</sup>*MobilEasy Technologies Ltda*

*Brazil*

#### **1. Introduction**

200 Advances and Applications in Mobile Computing

Stanton, N.A.(2004). The psychology of task analysis today D. Diaper, N.A. Stanton (Eds.),

Stanton,N.A.(2006). Hierarchical task analysis: Developments, applications, and extensions,

Xiehao, B.; Hongxia, B.; Mingxiao, W.(2009). Integration of multimedia and location-based

Erlbaum Associates, London, pp. 569–584

Nov. 2009, doi: 10.1109/ICNIDC.2009.5360805

10.1016/j.apergo.2005.06.003.

The Handbook of Task Analysis in Human–Computer Interaction, Lawrence

Applied Ergonomics, Vol.37, No.1, January 2006, pp.55-79, ISSN 0003-6870,

services on mobile phone tour guide system," Network Infrastructure and Digital Content, 2009. IC-NIDC 2009. IEEE International Conference on , pp.642-646, 6-8

> Recommender Systems (RS) emerged as an independent research area in the mid 90s, and they have been proved in the recent years to be a valuable approach for coping with the information overload problem (Ricci, 2011). In general, recommendation systems manage the overload of information by helping a user to choose among an overwhelming number of possibilities. In another point of view, we argue that the underlying concepts of Context-Aware Recommendation Systems are also useful to build supporting software (infra-structure) for mobile, pervasive and ubiquitous environments, which have requirements such as invisibility of computing, follow-me semantics (Augustin et al., 2006), context-awareness, adaptation, user-centric personalization, everyday human activities.

> A pervasive environment is characterized by a richness of contexts in which users, devices and agents are mobile in several places and with several entities, including services, applications and resources. These elements can be available or not in a particular time and space. In fact, information regarding this time-space scenario can be used to develop a behavioral pattern of an entity in several contexts (Munoz-Organero et al., 2010).

> To achieve ubiquity, the support system, which runs the environment management, must be designed to find the right balance between the user individuality and the system proactivity. In mobile and pervasive computing, we consider that the user's tasks must be personalized, while functionalities associated with security must be designed proactively. In mobile environment, the user moves constantly and wants to maintain access and to continue with the activity that he was performing (follow-me semantics). While traveling, the user can be served by different context (networks, systems, resources), which require authentication services periodically. Thus, proactive support system must know when to perform the authentication so implicitly or explicitly, seeking to reduce the user interference. We propose adopting a Context-Aware Recommender System (CARS) to help in this decision making process.

On the other hand, it is known that mobile devices can be easily lost, stolen, or used by several users, enabling sensitive information to become inappropriately available and could be stolen or broadcasted without permission. To avoid this situation, there is the need to adopt an authentication process, given that standard security policies allow that a second form of user validation can be used to complement the traditional alphanumeric passwords.

In pervasive computing, in which the majority of mobile devices has a reduced size, users face difficulties on providing (safe) passwords with too many characters. Considering that the pervasive space is rich in additional information, captured by sensors such as location, time, temperature, security camera images, which compose the environmental context. Therefore, it is possible to use this information, associated with the user behavior as a component of the authentication process. The challenge of this solution lies on the complexity of modeling the individual behavior, as there is the need to develop models to deal with different user behavior.

To address this issue, we present a user behavioral model based on activities, environmental contexts, and user profile. These three elements form a tri-dimensional matrix of a CARS to the authentication in a pervasive system. We also will discuss the processes of (i) content-based filtering, (ii) collaborative filtering and (iii) hybrid filtering. A relevant factor of the behavioral model is the spatial-temporal modeling, which allows us to compute the conditional probability of the occurrence of a given activity. This factor allows to consolidate user implicit authentications in the pervasive space or to launch a challenge question to determine the user authenticity.

#### **2. Implicit authentication**

#### **2.1 Motivation and proposal**

With the popularization of mobile devices (smartphones and tablets), there is a gradual user migration to the use of pervasive computing environments. The most common use of such devices is the storage and the retrieval of information that is relevant to the users, such as financial services, social networks, electronic messages, and e-commerce.

However mobile devices add disadvantages regarding the safety and privacy of information that is stored. Now new security mechanisms should be concerned about the loss of the devices and where they are being used, since it can be easily lost, stolen or used by multiple users, allowing confidential information may be used inappropriately.

The standard security policies allow the use of a second alternative for user validation that can be used to complement the traditional alphanumeric passwords and minimize the disadvantages of loss of information in mobile devices. The password-based authentication has the advantage of being simple to implement and use few resources (no additional hardware is usually needed). However, usual password discovery methods (phishing, keyloggers, and social engineering) has been successfully applied in breaking many passwords.

The use of an additional validation as part of the authentication provides a more secure process. There are several services (e.g. google authenticator) that already implement it, but they still have problems regarding usability and cost. The choice of enterprises for the use of SecureID tokens that are displayed by auxiliary devices and have characteristics related 2 Will-be-set-by-IN-TECH

On the other hand, it is known that mobile devices can be easily lost, stolen, or used by several users, enabling sensitive information to become inappropriately available and could be stolen or broadcasted without permission. To avoid this situation, there is the need to adopt an authentication process, given that standard security policies allow that a second form of user

In pervasive computing, in which the majority of mobile devices has a reduced size, users face difficulties on providing (safe) passwords with too many characters. Considering that the pervasive space is rich in additional information, captured by sensors such as location, time, temperature, security camera images, which compose the environmental context. Therefore, it is possible to use this information, associated with the user behavior as a component of the authentication process. The challenge of this solution lies on the complexity of modeling the individual behavior, as there is the need to develop models to deal with different user

To address this issue, we present a user behavioral model based on activities, environmental contexts, and user profile. These three elements form a tri-dimensional matrix of a CARS to the authentication in a pervasive system. We also will discuss the processes of (i) content-based filtering, (ii) collaborative filtering and (iii) hybrid filtering. A relevant factor of the behavioral model is the spatial-temporal modeling, which allows us to compute the conditional probability of the occurrence of a given activity. This factor allows to consolidate user implicit authentications in the pervasive space or to launch a challenge question to

With the popularization of mobile devices (smartphones and tablets), there is a gradual user migration to the use of pervasive computing environments. The most common use of such devices is the storage and the retrieval of information that is relevant to the users, such as

However mobile devices add disadvantages regarding the safety and privacy of information that is stored. Now new security mechanisms should be concerned about the loss of the devices and where they are being used, since it can be easily lost, stolen or used by multiple

The standard security policies allow the use of a second alternative for user validation that can be used to complement the traditional alphanumeric passwords and minimize the disadvantages of loss of information in mobile devices. The password-based authentication has the advantage of being simple to implement and use few resources (no additional hardware is usually needed). However, usual password discovery methods (phishing, keyloggers, and social engineering) has been successfully applied in breaking many

The use of an additional validation as part of the authentication provides a more secure process. There are several services (e.g. google authenticator) that already implement it, but they still have problems regarding usability and cost. The choice of enterprises for the use of SecureID tokens that are displayed by auxiliary devices and have characteristics related

financial services, social networks, electronic messages, and e-commerce.

users, allowing confidential information may be used inappropriately.

validation can be used to complement the traditional alphanumeric passwords.

behavior.

passwords.

determine the user authenticity.

**2. Implicit authentication**

**2.1 Motivation and proposal**

to desktop-based computing, where information is restricted to safe environments (business and home) are very different from the pervasive environment where mobile devices not have these limiting safety.

The implicit authentication is an approach that uses observations of user behavior for authentication. Since people are creatures of habits - a person goes to work in the morning, perhaps with a stop at the coffee shop, but almost always using the same route. Once at work, she might remain in the general vicinity of her office building until lunch time. In the afternoon, perhaps she calls home and picks up her child from school. In the evening, she goes home. Throughout the day, she checks her various email accounts. Perhaps she also uses online banking and sometimes relies on her smartphone for access away from home. Weekly visits to the grocery store, regular calls to family members, etc. are all rich information that could be gathered and recorded almost entirely using smartphones. (Shi et al., 2011).

With the migration of users to pervasive computing, mobile devices begin to collect the information produced by daily user activity: user schedule, calls users, messages, and add additional data (eg time and location) in the information produced. This set of data and information that is collected daily defines a profile that woks as a reliable criterion for authenticating users.

According to Shi et al. (2011) the implicit authentication is able to: i) act as a second factor for authentication passwords and supplements for greater assurance of a profitable and easy to use, ii) work as the primary authentication method to completely replace passwords, and iii) provide additional security for financial transactions such as credit card purchases, acting as an indicator of fraud. We note that in the latter scenario, the act of making the transaction does not require any user action on the device.

#### **2.2 Research in authentication for pervasive environment**

We present a set of related approaches for context-aware authentication with the goal of comparing this work with the state-of-art in this area:

system (i) is able to interfere and to analyze user behavior using cognitive (intelligent) agents, and (ii) can determine the security level needed, predicting, thus, the cost associated with the authentication process delay, given the application of cryptography algorithms. TBAS uses two kinds of cognitive agents: a mobile cognitive agent (MCA) and a static cognitive agent (SCA). In this approach, the SCA creates the MCA and sends this mobile cognitive agent to the mobile device. This procedure is done while the user is being authenticated.

#### **2.2.1 Analysis of the approaches**

In this section, we conduct an analysis of the previously presented approaches considering the essential requirements to the authentication on pervasive environments. As shown on Table 1, the majority of the approaches present a weak contextual modeling, as they consider only aspects regarding the characteristics of the devices used by the user and his spatial context. In this way, such systems have an incomplete vision of the scenario, hurting the decision making process. The proposal 1 works with a limited spatial-temporal model, because time is modeled though a simplification that does not allow the inference of weekly or biweekly activities (for example, on every Saturday the user plays tennis with new adversaries and needs to contact them to schedule the game).

A small part of these analyzed approaches presents some mechanism to dynamically analyze and model user behavior. The proposals presented on 3 and on 4 are static, i.e. they do not offer mechanisms that the system can use to dynamically aggregate the knowledge and the skills acquired by the user during his interactions with the system. In 3, despite the proposition of an activity recognition mechanism, the user must explicitly inform the activity that he is executing. In 4, although a proposal for using profiles to determine the user 4 Will-be-set-by-IN-TECH

3. **Authentication based on Activity Recognition** - Hassan et al. (2008) propose an activity-based security mechanism that aims to help in the user activities in ubiquitous environments. Such mechanism is composed of an authentication system based on the human identification of images (Jameel et al., 2006) of an activity oriented access control module. The proposed model supports several kinds of devices, including mobile devices (PDAs), laptops, and desktops. In the mechanism, the activity recognition manager (ARM) provides information regarding user activity to the authorization system, through low level data collection related to the activity and the production of high-level contextual information. This way, the ARM can conduct the process of reasoning about user actions. 4. **Context-based Authentication** - Corradi et al. (2004) propose a security middleware, named UbiCOSM (Ubiquitous Context-Based Security Middleware). Such approach adopts the context as a basic concept to the specification and the execution of security policies. Therefore, the permissions are associated directly to the contexts, instead of being associated with the identities and with the user roles. The information about contexts and resources is provided by the CARMEN middleware (Bellavista et al., 2003). The UbiCOSM access control manager works with two context classifications: physical and logical context. Physical contexts identify physical spaces delimited by specific geographical coordinates. This way, a user operates in a given physical context, depending on his current location; thus, the user can only belong to a physical context. Additionally, the physical contexts define specific boundaries to the access control manager, as each

physical context has references to the resources which must be protected.

In this section, we conduct an analysis of the previously presented approaches considering the essential requirements to the authentication on pervasive environments. As shown on Table 1, the majority of the approaches present a weak contextual modeling, as they consider only aspects regarding the characteristics of the devices used by the user and his spatial context. In this way, such systems have an incomplete vision of the scenario, hurting the decision making process. The proposal 1 works with a limited spatial-temporal model, because time is modeled though a simplification that does not allow the inference of weekly or biweekly activities (for example, on every Saturday the user plays tennis with new adversaries and needs to contact

A small part of these analyzed approaches presents some mechanism to dynamically analyze and model user behavior. The proposals presented on 3 and on 4 are static, i.e. they do not offer mechanisms that the system can use to dynamically aggregate the knowledge and the skills acquired by the user during his interactions with the system. In 3, despite the proposition of an activity recognition mechanism, the user must explicitly inform the activity that he is executing. In 4, although a proposal for using profiles to determine the user

authenticated.

**2.2.1 Analysis of the approaches**

them to schedule the game).

system (i) is able to interfere and to analyze user behavior using cognitive (intelligent) agents, and (ii) can determine the security level needed, predicting, thus, the cost associated with the authentication process delay, given the application of cryptography algorithms. TBAS uses two kinds of cognitive agents: a mobile cognitive agent (MCA) and a static cognitive agent (SCA). In this approach, the SCA creates the MCA and sends this mobile cognitive agent to the mobile device. This procedure is done while the user is being

Table 1. Approaches for Context-Aware Authentication (AuthZ)

permissions is presented, the user must explicitly determine which activities he intends to perform on the system.

As the battery life time is a major usability concern in mobile devices (Rahmati & Zhong, 2009), it is desirable that the authentication mechanisms take into account aspects related to the consumption of computational resources in an intelligent way, when they execute their security procedures. Thus, the validation of the proposal in 2 is based on the categorization of mobile transactions, considering the security level needed to perform such operations. Although there is this cost categorization associated with the authentication process, such categorization is only used to determine the impact of the delay associated to the cryptography algorithm used, and to define which will be the authentication methods (challenges) used. On the other hand, in 1 and in 3, despite the authors cite that the proposed architecture is lightweight, no experiments or additional details are presented to describe how such architecture deals with the mobile devices energy constraints. The architectures having the authentication control in the server do not interfere in the applications and in the mobile device operational system, making its dissemination easier and reducing battery consumption.

The possibility of managing the security policies by the users has become steadily more important in the design of security solutions that aim to align the usability to the capacity of maintaining acceptable levels of integrity, reliability and availability in mobile applications (Toninelli et al., 2009). However, security and usability have been rarely integrated in a satisfactory way in the design and in the development of mobile systems (Hong et al., 2007).

Therefore, we aimed to analyze the proposed approaches in terms of privacy and flexibility of authentication mechanisms and policies. In 2 and 4 it is presented another authentication mechanism. In 2, different challenges are provided to the user in order to ensure his identity, depending on the security level needed by the requested transaction and the user behavioral anomaly level. On the other hand, in 4, the user defines its preferences through profiles, which are used by the system in the user authentication and authorization processes. In 1 it is not presented a validation mechanism nor the generation of challenges, the authors inform that this can be performed by the applications, in different levels. In 3, the proposed architecture provides only one authentication way for the user, which is the process of identifying images proposed before by the same authors in Jameel et al. (2006).

#### **3. Context-aware recommendation systems**

We adopt Burke's taxonomy (Burke, 2007) to provide an overview of the different types of RS, which has become a classical way of distinguishing between recommender systems and referring to them. He distinguishes six different classes of recommendation approaches:

Many resources in ubiquitous environments can be available and the users want to share them. However, the situations and the preferences of users are different, even if the users are in the same environment. Therefore, we want to have a proper RS for sharing the available resources. These and other possible models for recommendation systems may be adequate for current uses, but perhaps not for future ubiquitous computing.

Ubiquitous computing systems with knowledge of more than locations - say, the tools a person is using - could greatly benefit that person by recommending others who have expertise with those tools. Although it may be impossible to optimally anticipate the needs of each user at any place at any time, ubiquitous computing will enable such systems to help people to cope with an expanding array of choices (McDonald, 2003).

Therefore, the recommendation approach, which proved to be successful to PC users, cannot be straightforwardly applied for mobile users due to the obstacles that are typically present in mobile usage environments, such as: limitation of mobile devices and wireless networks, impact from external environment, and behavior characteristics of mobile users (Ricci et al., 2011).

#### **3.1 Mobile recommender systems**

Mobile recommendation systems are systems that provide assistance to the users as they face decisions "on the go", or, in other words, as they move into new, unknown environments. 6 Will-be-set-by-IN-TECH

provides only one authentication way for the user, which is the process of identifying images

We adopt Burke's taxonomy (Burke, 2007) to provide an overview of the different types of RS, which has become a classical way of distinguishing between recommender systems and referring to them. He distinguishes six different classes of recommendation approaches:

• Content-based filtering: recommends items that are similar to the ones that the user liked

• Collaborative filtering: recommends to the active user the items that other users with

• Demographic: recommends items based on the user demographic profile. The assumption is that different recommendations should be generated for different demographic niches; • Knowledge-based: recommends items based on specific domain knowledge about how certain item features meet users needs and preferences and, ultimately, how the item is useful for the user. Notable knowledge-based recommender systems are case-based or

• Community-based: recommends items based on the preferences of the user's friends. This technique follows the epigram "Tell me who your friends are, and I will tell you who you

• Hybrid recommender systems: based on the combination of the above mentioned techniques. A hybrid system combining techniques A and B tries to use the advantages

Many resources in ubiquitous environments can be available and the users want to share them. However, the situations and the preferences of users are different, even if the users are in the same environment. Therefore, we want to have a proper RS for sharing the available resources. These and other possible models for recommendation systems may be adequate

Ubiquitous computing systems with knowledge of more than locations - say, the tools a person is using - could greatly benefit that person by recommending others who have expertise with those tools. Although it may be impossible to optimally anticipate the needs of each user at any place at any time, ubiquitous computing will enable such systems to help people to cope

Therefore, the recommendation approach, which proved to be successful to PC users, cannot be straightforwardly applied for mobile users due to the obstacles that are typically present in mobile usage environments, such as: limitation of mobile devices and wireless networks, impact from external environment, and behavior characteristics of mobile users (Ricci et al.,

Mobile recommendation systems are systems that provide assistance to the users as they face decisions "on the go", or, in other words, as they move into new, unknown environments.

for current uses, but perhaps not for future ubiquitous computing.

with an expanding array of choices (McDonald, 2003).

proposed before by the same authors in Jameel et al. (2006).

**3. Context-aware recommendation systems**

similar tastes liked in the past;

constraint-based systems;

of A to fix the disadvantages of B.

**3.1 Mobile recommender systems**

in the past;

are".

2011).

Examples include consumers making purchasing decisions in retail stores, or students having ad hoc meetings to decide on assignment workload van der Heijden et al. (2005).

As mobile devices are popular and are becoming a primary platform for information access and business applications, recommendation techniques can increase the usability of mobile and pervasive applications, providing more focused content and tailoring the information to the needs of the user. Ricci et al. (2011) reviews the major techniques that have been proposed in the last years and illustrates the supported functions by Mobile Recommender Systems (MRS).

As we have observed, in the most mobile recommender systems, the recommendation target is Internet content, multimedia (videos, music, films, books), product promotions, tourist experiences and traffic information. The data sources are users and Internet transactions. For those MRS that add context-awareness, the more commonly context information is the user location (location-aware applications).

Context-based applications pro-actively retrieve content of interest based on the user current task or his profile. Roberts et al. (2008) describe the implementation of a mobile recommender system for leisure activities, named Magitti, which was built for commercial deployment under stringent scalability requirements. Ricci & Nguyen (2007) exploit the recommendation by proposal and critiquing techniques in the MobyRek system, that has been designed to run on a mobile phone with limited user input. It searches functionality, lets the user to formulate both must and wish conditions, and returns a ranked product list. The result of an empirical evaluation of the system shows that it can effectively support product selection processes in a user friendly manner.

Although the systems target different needs, they share a common design: the system collects different types of (contextual) information characterizing the user (such as preferences, activities, location, device) and uses it to filter and to rank relevant content items and trying to anticipate the needs or the products in which the user may be interested, while he is moving himself.

#### **3.2 Pervasive security and recommender systems**

Security problems in RSs are addressed with two goals in mind: (i) to control malicious users (Ray & Mahanti, 2009), and (ii) to ensure user privacy (Zhan et al., 2010). Thus, we are interested in recommender systems for security in mobile and pervasive environment.

The goal of research in Mobile, Pervasive and Ubiquitous Security is to understand and to analyze the new security and privacy issues arising from the high mobility, context-awareness and invisibility of these systems, that begin to be available, and to propose solutions to safely deploy applications, services and appliances anywhere, anytime, on any device, on any network, running on background.

According to Johnson (2009), pervasive environments need context-aware security mechanisms because the context changes allow these mechanisms to be adjusted on the basis of the current situation. Therefore, the mechanisms using this approach are able to effectively deal with traditional security systems limitations that are designed for static environments. Nevertheless, most of the research regarding the development of context-aware authentication systems is limited or vague (Ricci & Nguyen, 2007). Usually, these systems only consider traditional aspects, e.g. the user location. As a result, they provide an abstract and weak view of a certain situation. Thus, the decisions made within these systems are poor, because they are based on an incomplete scenario.

Few works address issues about pervasive security and context-aware recommender systems. Kim et al. (2009) propose a new authenticated key management method, called 3DE\_sec, to minimize the load of the authenticator, even with mobile nodes or the insertion or deletion of nodes. The proposed system generates personalized profiles (using inference of data captured from RFID systems) containing user preference and various lifestyles and uses of recommendation service, it updates based on past history and currently available services. The system architecture is composed by profile collector, profile aggregator, collector resolver, and service manager. The evaluation results indicate that the method can set a shared key faster and more securely using a multiple-key ring assigned to each node before deployment secure and efficient recommendation service in RFID.

Romero-Mariona et al. (2008) propose a recommendation system for helping developers to choose among security requirements approaches. As part of their ongoing research, they identified ten key characteristics (elicitation support, level of customer involvement, unambiguity level, completeness level, clarity level, traceability level, overall security level, update difficult of the security specifications, automation support level, scalability level) that are used to recommend which of the 12 approaches surveyed better suits a specific project. In a different direction of these approaches, we aim to recognize the suitable moment to renew the user authentication automatically or to ask the user for information to explicitly authenticate him.

#### **4. CARS-AD architecture**

The pervasive space exists in an environment where situations or contexts surround the user. In particular, these contexts are relevant for the adaptive process of services and information offered to the user through context-aware applications (Dey, 2001). Therefore, the situations that a user can experienced in a pervasive environment are personal, complicating the representation of the user's context and its parameters.

We analyzed the resources offered by mobile devices to identify contexts and properties relevant to the users and their behaviors in pervasive environments. Consequently, the following authentication-relevant contexts were identified (Uden, 2007):

The temporal property was integrated with the context model to improve the decision-making process (Uden, 2007). Specifically, this model considers historical data, such as the user learning capacity, including the acquisition of skills and knowledge, and the evolution of activities and behaviors (see Figure 1).

Fig. 1. Contextual Modeling adapted from Cassens & Kofod-Petersen (2006)

#### **4.1 Context model**

8 Will-be-set-by-IN-TECH

of a certain situation. Thus, the decisions made within these systems are poor, because they

Few works address issues about pervasive security and context-aware recommender systems. Kim et al. (2009) propose a new authenticated key management method, called 3DE\_sec, to minimize the load of the authenticator, even with mobile nodes or the insertion or deletion of nodes. The proposed system generates personalized profiles (using inference of data captured from RFID systems) containing user preference and various lifestyles and uses of recommendation service, it updates based on past history and currently available services. The system architecture is composed by profile collector, profile aggregator, collector resolver, and service manager. The evaluation results indicate that the method can set a shared key faster and more securely using a multiple-key ring assigned to each node before deployment

Romero-Mariona et al. (2008) propose a recommendation system for helping developers to choose among security requirements approaches. As part of their ongoing research, they identified ten key characteristics (elicitation support, level of customer involvement, unambiguity level, completeness level, clarity level, traceability level, overall security level, update difficult of the security specifications, automation support level, scalability level) that are used to recommend which of the 12 approaches surveyed better suits a specific project. In a different direction of these approaches, we aim to recognize the suitable moment to renew the user authentication automatically or to ask the user for information to explicitly authenticate

The pervasive space exists in an environment where situations or contexts surround the user. In particular, these contexts are relevant for the adaptive process of services and information offered to the user through context-aware applications (Dey, 2001). Therefore, the situations that a user can experienced in a pervasive environment are personal, complicating

We analyzed the resources offered by mobile devices to identify contexts and properties relevant to the users and their behaviors in pervasive environments. Consequently, the

• Interpersonal context: describes the social aspects of the user, such as the relationships and

• Environmental context: captures the situations surrounding the user, such as services,

The temporal property was integrated with the context model to improve the decision-making process (Uden, 2007). Specifically, this model considers historical data, such as the user learning capacity, including the acquisition of skills and knowledge, and the evolution of

are based on an incomplete scenario.

him.

**4. CARS-AD architecture**

secure and efficient recommendation service in RFID.

the representation of the user's context and its parameters.

people and information accessed by the user.

activities and behaviors (see Figure 1).

following authentication-relevant contexts were identified (Uden, 2007):

communication channels between the user and his community; • Spatial context: considers attributes regarding the use's location;

• Operational context: describes the goals, tasks and activities of the user;

The proposed context-aware authentication architecture attempts to use resources that are commonly found in mobile devices, such as smartphones, to monitor the behavior of the user in different situations and events where the user is immersed in the pervasive space. These devices are considered as special artefacts that are commonly used by the users to perform tasks and activities, and consequently, to achieve their goals in mobile environments (Uden, 2007). Specifically, these devices offer access to resources such as (Lima et al., 2010):

• Sensors: can provide information regarding the environment, visual authentication and other information that define the environment in which the user is interacting with the authentication system.

The architecture has additional modules that allow the recommendation system implementation, described as follows:

The context-aware authentication architecture is illustrated in Figure 2. The context subsystem, or, user context, is responsible for capturing all the situations that determine the occurrence of a new event through the resources previously described. Thus, this subsystem sends the event description *ei* to the belief analyzer subsystem.

Fig. 2. Proposed Architecture

#### **4.2 Behavioral model**

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• Sensors: can provide information regarding the environment, visual authentication and other information that define the environment in which the user is interacting with the

The architecture has additional modules that allow the recommendation system

• Group profile: a pre-defined profile which considers the standard characteristics of the agents (users, applications, use sessions and environment) which interact with the system. • Explicit profile: created during the first interaction of the system with the user through an interactive interface; contains the events made explicit by the user and extracted from their contacts and the personal agenda stored in the mobile device. This profile can be

• Implicit profile: created through the processing of user events and his explicit profile; it has the relevant information about frequent events, the actions taken by the users, and their

• Recommendation filter: an adaptive filter according to the approach (based in content, collaborative, or hybrid) which uses the vector space model to compute the information relevance, and uses a formal treatment through the vectors to compute the similarity

The context-aware authentication architecture is illustrated in Figure 2. The context subsystem, or, user context, is responsible for capturing all the situations that determine the occurrence of a new event through the resources previously described. Thus, this subsystem

**Belief Analyzer:** The Belief Analyzer is responsible for the definition of behaviors or beliefs, as well as for the classification of events and the inference of behaviors through the activities, the stored profiles, and the perceived and registered events. The behaviors are analyzed by similarity or probabilistically in order to define new occurrences and to determine the attitude to be adopted by the system, as well as the actions that must be taken in a new occurrence. The beliefs database works with a knowledge repository

**Recommendation Filter:** The Recommendation Filter aims to determine the new implicit profiles, i.e. for each combination of an event with the explicit profile, a new orthogonal vector is determined. This vector is used to compute the similarity: if the similarity degree is higher than a defined threshold, the profile is considered relevant and, then, it will be stored in the system as a new implicit profile, with a weight equals to the similarity degree. **Probability Analyzer:** The subsystem which analyzes probabilities (Probability Analyzer) is responsible for the user categorization, based on the conditional probabilities of his behavior. This classification is divided in three categories: normal, suspect and abnormal. **Challenger:** The subsystem determines how the user will be questioned in order to prove his identity on the system, based on the categorization made by the Probability Analyzer and on the authentication level needed for the desired operation. The response to the challenge

authentication system.

implementation, described as follows:

spatial-temporal characteristics.

between the profiles being analyzed.

(storing beliefs and profiles).

sends the event description *ei* to the belief analyzer subsystem.

proposed to the user is, then, stored for future queries.

customized and synchronized at any time;

Given the habitual tendencies of individuals, time correlations are important for the determination of successive events (Rocha, 2010). Hence, the prediction of events defines the actions and behaviors that the user will adopt. This work uses the following definitions to formally define the concepts of event and behavior:

In the first moment we used the cosines of vectors to calculate the similarity between contexts and activities. However due to the diversity of domains that exist between the elements of each of these vectors was necessary normalization of values, similar case was reported in Su & Khoshgoftaar (2009). To solve this problem was adopted Pearson's correlation.

The similarity between two different contexts is established by the equivalence of events and it is determined by the possibility that activities in these contexts may occur again. The similarity between context is defined as

$$Sim(\mathbb{C}\_i, \mathbb{C}\_j) = \frac{\mathbb{C}\_i \times \mathbb{C}\_j}{|\mathbb{C}\_i| \times |\mathbb{C}\_j|} \tag{1}$$

When applying the normalization values through Pearson's Correlation Coefficient (PCC) in equation 1. The new similarity between contexts will be

$$PCCc\_{i,j} = \frac{\sum\_{u \in U} (c\_{u,i} - \overline{c\_i})(c\_{u,j} - \overline{c\_j})}{\sqrt{\sum\_{u \in U} (c\_{u,i} - \overline{c\_i})^2} \sqrt{\sum\_{u \in U} (c\_{u,j} - \overline{c\_j})^2}} \tag{2}$$

The equivalence between two activities can be determined by reusing actions or by the similarity between the activities. Can be defined as

$$\text{Sim}(A\_{i\cdot}A\_{j}) = \frac{A\_{i} \times A\_{j}}{|A\_{i}| \times |A\_{j}|} \tag{3}$$

When applying the normalization values through Pearson's Correlation Coefficient in equation 3. The new similarity between activities will be

$$PCCa\_{i,j} = \frac{\sum\_{u \in \mathcal{U}} (a\_{u,i} - \overline{a\_i})(a\_{u,j} - \overline{a\_j})}{\sqrt{\sum\_{u \in \mathcal{U}} (a\_{u,i} - \overline{a\_i})^2} \sqrt{\sum\_{u \in \mathcal{U}} (a\_{u,j} - \overline{a\_j})^2}} \tag{4}$$

The presented definitions represent the behavioral modeling and the way to compute the similarity between contexts and between activities. Using these definitions the processes of Recommendations System filtering are defined and characterized.

#### **4.3 Spatio-temporal permutation model**