**5.1.2 Architectural and design issues**

In this section, we describe the most important technical considerations about the BTMS. The main issues are those related to (1) trains positioning, (2) wireless 'train-to-earth' communications and (3) added value services. These three issues are described below.

1. **Train positioning system.** The BTMS permits a new way of train positioning which works independently of the main system operation. In order to achieve this target, a new hardware/software module is boarded on each train. This module combines the positioning data provided by some hardware devices (accelerometers, gyroscope, odometer, etc.) with the coordinates given by a GPS module. To generate the most accurate positioning information, this system parts from a railway lines different tabulation ways. In this case, the tabulation is related to lines lengths (in kilometres) and the traffic signals positions. Based on this information, and the data extracted from the hardware and software modules boarded on trains (including GPS), this system translates this information to kilometric points (Shang-Guan et al., 2009). Then this positioning information is sent to the control centre in real-time.

Besides, the BTMS communicates with an external positioning information system which permits the reception of train positioning information generated by the main CTC system.

The first one is a *Statistical Analysis Service*, which using the information stored by the positioning system on a data base, the BTMS can make statistical analysis related to the system's reliability level, GPS and GPRS coverage, and other system functionality aspects. Thus, one of the main goals of this service is to compare the received information, determining if the positioning provided by the train-side systems is according to the information generated by the primary CTC system. And the second is a *Control Message Exchanged Service*, which allows the procedural alarms transmission to the train-side systems. These kinds of alarms indicate anomalous situations to the train drivers: primary system failure, signal exceeds authorization to a certain point as a consequence of a failure of any electro-mechanical track component, etc.


**2.2. Catalysts preparation**

Gamma alumina (CK-300, Ketjen), activated carbon (NORIT RX3 Extra and CNR), and composite support, BTAl and UTAl were used as supports to prepared metal catalyst. Platinum was loaded with PtCl2 and H2PtCl6H2O by incipient wetness technique. After impregnation, catalysts were dried at 393 K in an oven for 24 h and were maintained in desiccator for further use. The catalysts were reduced in a H2 flow at 503 K for 1 h after catalytic test.

New Strategies for Obtaining Inorganic-Organic Composite Catalysts for Selective Hydrogenation http://dx.doi.org/10.5772/65959 187

**Figure 3.** Monomer precursors of the polymeric phase and polymerization starter.


*2.4. Statistical Analysis*

#### 2.4.1. Model Derivation

We used XG Boost, a scalable ML system for tree boosting [25]. We used the available open source package [26] for Python, Version 3.7.4 (Python Software Foundation, Delaware, DE, United States) [27].

The first release of the model was trained using data related to 1st April 2020 (training dataset index date), while the second and the third versions were derived using data related to 15th July 2020 and 1st November 2020, respectively. We considered all the clinics delivering services to at least one patient on the index date as well as over the week before index date.

#### 2.4.2. Model Accuracy and Feature Importance

Prediction accuracy of each release was tested every first and fifteenth day (validation dataset index dates). Therefore, development and validation datasets can include the same set of clinics/patients every two weeks.

To evaluate model performance, we measured the area under the curve (AUC) of the receiver operating characteristics (ROC) curve in the testing datasets [28] using Python, Version 3.7.4 (Python Software Foundation, Delaware, DE, United States) [27]. The AUC provides an aggregate measure of performance as the ROC curve plots the true positive rate (TPR) against the false positive rate (FPR) at all classification thresholds. Model discrimination ability over time was monitored by visual inspection of AUC trends. For illustrative purposes, we also reported the classification performance in terms of P(Outbreak|Class) (i.e., probability of outbreak (Yes/No) given the assigned risk class (L/M/H)) and P(Class|Outbreak) (i.e., probability of the assigned risk class given the outbreak) for the two action-thresholds chosen (0.015 and 0.125). In order to calculate P(Outbreak|Class) and P(Class|Outbreak) we artificially treated our problem as a binary decision for each threshold. We computed average probability values across the whole study period.

Feature importance was computed using the SHapley Additive exPlanations (SHAP) method [29]. This analysis enables intuitive model explainability via an accurate and efficient estimation of the contribution to risk of each input variable.

### 2.4.3. Descriptive Statistics

For both the training and validation datasets, we analyzed the number of active clinics, frequency and incidence of a COVID-19 outbreak, the distribution of clinics in each prediction level of risk (low, medium, high), as well as the relative risk compared to clinics in low-risk groups with Python, Version 3.7.4.

#### **3. Results**

#### *3.1. Dialysis Clinic Characteristics*

Model version 1, 2, and 3 were trained using a dataset related to 1st April 2020, 15th July 2020, and 1st November 2020, respectively. On these dates, active clinics were 589, 597, 603, while 34 (5.77%), 44 (7.37%), and 233 (38.64%) clinics had two or more patients with COVID-19 infection in the fortnight after the index date.

The surveillance system stratifies clinics by their risk of new local outbreak within two weeks. To facilitate the interpretation of the results, we established three risk categories: (1) Low, when outbreak risk is less than or equal to 1.5%; (2) Medium, risk greater than 1.5% and less than or equal to 12.5%; (3) High, if risk is greater than 12.5%. Risk thresholds depend both on the incidence of pandemic and on the ability of any given clinic to implement containment measures. Figure 2 reports the share of active dialysis clinics in different risk classes at each testing date.

The actual outbreak incidence in the dialysis clinics during the validation period is reported in Figure 3.

**Figure 2.** Number of dialysis clinics at the validation dates. Colors denote risk categories: Red, high > 12.5%; Yellow, medium 1.5% < x ≤ 12.5%; Green, low ≤ 1.5%.

**Figure 3.** Model Performance and Incidence of Clinics with Outbreaks: the plot reports data related to the 1 year observation period.


**4. Effects of RL-TCLT on synaptic neurotransmission and synaptic plasticity**

Considering that irradiation with RL between 600 and 1200 nm produces changes at molecular, cellular, and tissue levels [50, 51] improving cognitive capacities [52], this enhancement in brain function will result in synaptic neurotransmission and synaptic plasticity potentiation after light treatment [53, 54]. Neurotransmission and synaptic plasticity represent the capacity of synaptic connections to adapt structurally and functionally in a stimulus-dependent manner [43]. Both synaptic neurotransmission and synaptic plasticity can be affected by different factors, such as mitochondrial dysfunction and increased oxidative stress, as well as physiological events including aging, stroke, brain injuries, or neurodegenerative disease, among others [22, 55, 56]. Therefore, treatments focused in maintain or promote neurotransmission and synaptic plasticity are attracting increasing attention. In this context, despite the beneficial effects showed for RL-TCLT on cognition, practically not exist electrophysiological studies using this therapy. As an approximation, we will discuss the studies using transcranial lowlevel laser light (TC-LLL).

### *Transcranial Red LED Therapy: A Promising Non-Invasive Treatment to Prevent Age-Related… DOI: http://dx.doi.org/10.5772/intechopen.100620*

Studies both *in vitro* and *in vivo* have shown that TC-LLL therapy supports neural function, this has been observed principally in reports using transgenic mouse models of Alzheimer's disease (AD) [57]. Meng et al. observed that TC-LLL therapy at 632.8 nm in primary hippocampal neurons treated with full-length Aβ1–42 peptide reduced Aβ-induced neurotoxicity. In addition, TC-LLL therapy shows neuroprotective effects decreasing Aβ-induced dendrite atrophy [57]. Also, TC-LLL treatment increased the expression of brain-derived neurotrophic factor (BDNF) in cell line and cultured neurons derived from APP/PS1 transgenic mice, suggesting that this neurotrophin will be modulating dendritic structure, promoting the survival of neurons and dendrite growth, and potentiating synaptic transmission in the CNS [57, 58]. All these results can be explained by the activation of the ERK/CREB/ BDNF pathway mediated by TC-LLL therapy [45] because this pathway is involved in the dendritic development of neurons [45, 57, 59]. Therefore, the TC-LLL therapy can induce activation of signaling transduction pathways, and gene transcription, which increases protein expression of different synaptic effectors and modulators, effects that also are potential therapeutic in treating neurodegenerative disease.

Interestingly, the NIR-LED light treatment at 670nm in the Tg2576 mice model of AD, which progressively accumulated Aβ in their brain [60], indicate that NIR-LED therapy decreased the levels of Aβ1–42 at the synapses and Aβ oligomer-induced reduction in long-term potentiation (LTP), relevant processes of neuroplasticity that correlates with memory formation [61]. Therefore, NIR-LED light therapy recovered crucial processes related to synaptic function, necessary to the preservation of cognition abilities [62]. Additionally, studies with photobiomodulation transcranial therapy with wavelengths of 635nm in a mouse model of depression showed that this treatment reduces glutamate levels and neurotoxicity, improving the depressant behavior. These beneficial effects can be explained by the activation of the PKA pathway and the increased levels of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors. In addition, reduce the expression of GluA1, decreasing the glutamatergic neurotransmission. Thus this therapy could rescue excitatory synaptic transmission, improve synaptic plasticity, and have also a potential anti-depressive effect [63].

Thus, several pieces of evidence suggest that the application of transcranial therapy light could be used to improve cellular components associated with the synaptic function [42], which is essential in the maintenance and preservation of cognition, including learning and memory [43]. In addition to the therapeutic effects at the molecular level, it is proposed the generation of changes at the behavioral level, such as cognitive improvement, antidepressant effects, and sleep improvement [42]. Furthermore, this therapy can stimulate neuronal organization or reorganization, therefore it could be extremely promising as a method of stabilization and/ or improvement of various brain disorders or nervous system, and neurodegenerative diseases [30, 42]. However, more extensive studies are necessary to evidence all the cellular and molecular mechanisms involved in these encouraging results. This last especially considering that the evidence summarized here consider severe differences in the device and light type used, the protocol of administration, and the study model. Is imperative to advance understanding the multiple targets of red light in the synaptic structure and function.

## **5. RL-TCLT mechanisms: improving mitochondrial function**

Considering the multiple reports revised previously, now known that Red Light therapy, including Red and InfraRed LED light and Red Laser light, have favorable effects on brain structure and function, and especially in the hippocampus

improving cognitive functions [9, 25, 42]. This enhances in cognitive capacity could be explained by the activation of neurogenesis and synaptogenesis [9, 42], as well as by the stimulation of processes related to synaptic plasticity such as LTP [62]. However, any of these events reveal a potential mechanism by which Red light treatment results therapeutic to different affections such as aging, neurodegenerative disease, stroke, and depression among others [42].

Interestingly, all pathological conditions mentioned previously involve, almost in part, dysfunction of hippocampal neurons attributable to mitochondrial defects [64, 65]. For example, mitochondrial dysfunction is considered a hallmark of aging and could be considered one of the factors leading to neurodegeneration [4, 22, 66]. Studies in humans and animal models showed that decreased memory correlates with reduced cerebral energetics metabolism and more specifically to mitochondrial bioenergetics deficits [4, 22, 66]. Therefore, the mitochondrial focus of aging and neurodegenerative diseases is of great interest for the development of a potent and ideally non-invasive anti-aging intervention to improve or attenuate cognitive impairment in the elderly.

Notably, enhanced metabolic functioning is one of the most identifiable properties of irradiate neuronal cells with RL or NIR light, resulting in increased intracellular ATP production [8]. Thus, mitochondrial ATP production is one of the most strongly suggested mechanisms of action of RL therapy [5, 8]; for example, studies using RL-TCLT at 660 nm for 15 sec daily for 2 weeks in aged 18 mo mice improved ATP concentration [16]. More specifically, studies in vitro with RL and NRL LED radiations with a wavelength between 600 and 850 nm have shown that the effects of this treatment are principally attributed to photon absorption by complex IV of the mitochondrial respiratory chain [5]. This mitochondrial complex corresponds to the cytochrome c oxidase (COX) enzyme [22] and it seems that RL increases the activity of this enzymatic complex, leading to enhancement of oxygen consumption and ultimately to mitochondrial respiration [5]. COX is a photo acceptor of RL and NIR light, which generates a redox change in the enzyme [5, 8]. In turn, this causes a transient change in mitochondrial membrane potential (mψ) and increases ATP production [5, 16]. Thus, wavelengths corresponding or near to red will be improving the mitochondrial production of ATP, potentiating the synaptic and cognitive function [5, 42]. Nevertheless, is important to highlight that other works report that RL could inhibit the COX enzyme. In particular, NIR wavelengths of 750nm and 950nm reduced the activity of the COX complex. This results in decreased mitochondrial respiration and a loss of mitochondrial membrane potential (ΔΨm) [67]. Is surprising to note that the attenuation of mitochondrial function and the concomitant production of superoxide radical reduce neuronal death exposed to oxygen–glucose deprivation and in a mice model of ischemia, an effect that is not observed after other NIR wavelengths that activate COX [67]. Altogether, these contradictory results question the real effect of RL on COX responsible for the beneficial effects of this therapy (**Figure 2**).

On the other hand, several reports showed that RL-LED modulates the levels of reactive oxygen species (ROS) [30, 40].  This could be explained by an increment in the activity of antioxidant enzymes such as catalase or also could be a consequence of increased mitochondrial function with reduced electron leak [5, 16]; more studies are necessary to evaluate these possibilities. Besides, in this study, the authors showed that RL-LED absorption activates transcription factors that regulate long-lasting effects on gene expression [16], therefore this suggests that Red Light therapy could be a more complex mechanism, at a long time, and not only a transient activation of several enzymes. Other results also showed that Red 635 nm irradiation inhibits the expression of COX *Transcranial Red LED Therapy: A Promising Non-Invasive Treatment to Prevent Age-Related… DOI: http://dx.doi.org/10.5772/intechopen.100620*

### **Figure 2.**

*Mechanism of action proposed to red and near-infrared light and its cellular effects. Transcranial therapy using red and near-infrared light has been proposed to photoactivate the cytochrome c oxidase (COX) enzyme, the complex IV of the electron transporter chain of the mitochondria. However other reports propose that several wavelengths inhibit COX enzyme; modulating ROS and ATP production, calcium homeostasis, and inflammatory processes.*

enzyme, reducing ROS levels and mRNA of cytosolic phospholipase A2 (cPLA2) and secretary phospholipase A2 (sPLA2) [68]. This also consequently inhibits the release of PGE2, suggesting an additional anti-inflammatory effect (**Figure 2**).

Additional mechanisms that will be involved in the positive effects of RL-LED implicate Ca2+ ions modulation [8, 40]. RL and NIR LED are recognized by water groups formed in the heat/light-gated Ca+2 channel. This induces vibrational water energy, which in turn disorganizes the protein structure of the Ca+2 channel. This conformational change finally leads to channel opening; modulating intracellular Ca+2 levels [5]. This possible mechanism is relevant in neurons, considering that intracellular Ca+2 levels are critical to trigger survival or death pathways related to synaptic activity [69].

In summary, despite various mechanisms that could be mentioned such as the potential molecular target of RL and NRL, still is necessary additional research in the field to understand the events that result in synaptic and cognitive function. Possibly these improvements are the result of diverse events occurring simultaneously.

## **6. Future perspectives of transcranial Red630-light-transcranial LED therapy preventing age-related memory loss: Our advances**

Despite diverse studies shown possible molecular targets of RL-LED therapy [5, 8], the precise mechanism underlying the neuroprotective actions of RL-TCLT is not completely understood. Therefore, more studies are required to determine the

biological events that lead to neuroprotection or neuronal repair in both aging and neurodegenerative diseases. Possibly, the main problem related to the incapacity of determining a detailed mechanism is based on the variability of wavelengths, times of treatment, and models used [15, 16, 40, 63, 70]. Therefore, this highlights the need for complete studies using the same mice model, LED dispositive, and therapy protocol, to understand and describe the mechanism(s) underlying the benefits of RL-TCLT.

Interestingly, RL-TCLT at 630 nm in patients with traumatic brain injuries, using a helmet that emits radiation for 30 min, three times per week, for six weeks showed a great reduction in post-traumatic stress symptoms, insomnia, and depression, suggesting improved cognitive function [71]. More importantly, the same RL-TCLT used in aged patients with mild cognitive impairment improves memory in these aged humans. For this reason, and to study the complete effects and mechanisms of RL-TCLT in aging, we designed a unique RL-TCLT device to emit homogeneous light at a wavelength of 630 nm, with 100 J of energy, a power density of 0,35 w/ cm2 , and an energy density of 43.5 J/cm2 in the brain of mice, specifically in the hippocampus.

We applied RL-TCLT to the hippocampus of 7.5mo SAMP8 mice, a mice model of accelerated aging, with an irradiation time of 125 s daily (excluding weekends) for 5 weeks. This protocol is equivalent to the applied to patients with mild cognitive impairment described previously, and the mouse lifespan. We started the RL-TCLT in SAMP8 at 7.5mo because we and other authors showed that the non-transgenic SAMP8 mice present age-related hippocampal memory loss since 6mo and is more evident from 7mo onwards [72]. Interestingly, our results reveal that 7.5mo SAMP8 mice treated with RL-TCLT at the hippocampus improves spatial learning and memory of aged SAMP8 mice. This cognitive improvement will be due to a possible remodeling of the synaptic structure toward more active synapses reducing the risk of excitotoxic events. This is suggested by i) an increase in presynaptic proteins such as synaptophysin (SYP) and Synapsin (SYN) that increase the neurotransmitter release [73], ii) a decrease in the NMDAR subunit NR2B, whose protein levels are related to excitotoxicity [74] and iii) higher Arc protein levels, a marker of synaptic plasticity [75] (Jara et al., manuscript in preparation).

Considering that both memory formation and synaptic activity are highly dependent on energy [76], that mitochondria are the main ATP producer of the cell [22], and that the suggested mechanisms by RL-TCLT target the mitochondria [5], we evaluated different mitochondrial functions in the hippocampus of treated SAMP8 mice with RL-TCLT. Relevantly, we observed increased ATP production, higher activity of the OXPHOS complex II-III, and IV (COX enzyme); suggesting that RL-TCLT directly stimulates mitochondrial bioenergetics function enhancing the activity of other OXPHOS complexes in addition to COX (Jara et al., manuscript in preparation). Similarly, we observed decreased levels of the mitochondrial calcium uniporter (MCU), suggesting that it will result in reduced mitochondrial Ca+2 overload and swelling, enhancing mitochondrial Ca+2 buffering. In fact, this last was validated in Ca+2 overload assays in hippocampal mitochondria from RL-TCLT SAMP8 mice (Jara et al., manuscript in preparation), indicating that RL-TCLT also improves the calcium buffering capacity of the aged hippocampal mitochondria. Whether bioenergetics and calcium buffering enhancing are directly related or are independent mechanisms requires future analysis.

Thus, our results indicated RL-LED-mediated mitochondrial stimulation, which could be transient or permanent. But it is difficult to think that only transient activation of mitochondrial function could explain the improved cognitive effects produced by RL-TCLT treatment. Although mitochondrial ATP production is vital for synaptic communication, it is probably not solely sufficient to

*Transcranial Red LED Therapy: A Promising Non-Invasive Treatment to Prevent Age-Related… DOI: http://dx.doi.org/10.5772/intechopen.100620*

result in improved hippocampal memory. Therefore, is highly probable that other mechanisms are involved in the beneficial effects of RL-TCLT, which result in gene transcription and the consequent cellular remodeling. In concordance with the anterior, we also observed higher PGC-1α protein levels, a transcriptional coactivator considered the main inducer of mitochondrial biogenesis that also regulates mitochondrial function [77]. This suggests that RL-TCLT will stimulate the generation of new mitochondria or the activation of gene-dependent mitochondrial reparation pathways that result in increased mitochondrial function. However, this requires a robust study.


**4.4 Orientation update using magnetic sensors**

Huang et al (2010) update orientation using magnetic field signals from magnetic sensors, i.e., magnetometers.

In order to determine the orientation of the sensor module for rotation representation and transformation between the l-frame and the b-frame, the Euler angles must be determined. In the swing phases, the Euler angles are calculated through integration of the angular velocity over time assuming that the original orientation of the sensor module is known. However, the original orientation is usually unknown in reality. In addition, the gyroscope signal summed during integration is perturbed by gyroscope bias. Thus the accuracy of the

The Application of Magnetic Sensors in Self-Contained Local Positioning 77

x (North)

hy

No matter whether a person is standing or walking, the sensor module attached to the human body is not confined to a flat and level plane. This makes it more difficult to determine the heading direction, because the tilt angles of the x-axis and the y-axis are always changing and the sensor module cannot stay horizontal to the earth's surface. Errors

To compensate the tilt of the sensor module when it is in an arbitrary position, the l-frame is transformed into the b-frame through three rotations with Euler angles ψ, θ and

sequence. The DCMs of the rotations are denoted as **R**1(*ψ*, z), **R**2(*θ*, y) and **R**3(*ϕ*, x) respectively. These Euler angles are related to the tilt but they are not all the tilts of the

> b T T sin sin cos cos cos *l* **g Rg**

2 2

atan 2 , *ggg xyz* (14)

in

(13)

y (East)

Magnetic Field

Heading

Fig. 6. Heading defined in the horizontal x-y plane

z (Down)

hz

hx

caused by tilt angles can be large if not compensated.

sensor module. The algorithm (Huang et al 2010) is as follows.

where **g**b = (gx gy gz]T and **g**l = (0 0 1]T. Therefore, we have

Rotations of the gravity vector from the l-frame to the b-frame are given by

orientation will decay overtime. Therefore, Huang et al (2010) use magnetometers to detect the magnetic field of the earth. Using data from magnetometers, together with gyroscopes and accelerometers, the orientation in the beginning of each human walk can be found. Similarly, the orientation of the sensor module in stance phase, when the sensor module is in a near-static status, can be updated in each gait. With the updates of orientation in stance phases, the integral drifts of rotated angles, which increase linearly over time, are greatly reduced.

To find the near-static orientation, the Euler angles *ψ*, *θ* and *φ* must be calculated. Because gyroscopes cannot be used in a near-static status, signals from accelerometers and magnetometers have to be used to calculate the orientations of the sensor module. However, these angles cannot be obtained directly. To resolve this problem, the tilts and heading of the sensor module need to be investigated.

Both x-tiltαand y-tilt β of the sensor module, or the sensor on it, as shown in Fig. 5, can be found simultaneously using acceleration outputs of all three axes as below:

$$\alpha = \operatorname{atan2}\left(a\_{x'}\sqrt{a\_y^2 + a\_z^2}\right) \tag{10}$$

$$\mathcal{B} = \operatorname{atan2}\left(a\_{y'} \sqrt{a\_x^2 + a\_z^2}\right) \tag{11}$$

where the two-argument function, atan2, is a variation of the arctangent function. For any real arguments *x* and *y*, which are not both equal to zero, atan2(y, x) is the angle in radians between the positive x-axis of a plane and the point given by the coordinates (x, y) on it.

The heading of a pedestrian, thus that of the sensor module on him/her, can be calculated using the x-axis and y-axis magnetic field outputs if the sensor module is level in a horizontal plane. As shown in Fig. 6, only the *x* and *y* components of the Earth's magnetic field, are used when determining the heading of sensor module. The headingγ is defined by:

$$\gamma = \operatorname{atan2} \{ h\_{\mathbf{y}}, h\_{\mathbf{x}} \} \tag{12}$$

where *h*x, *h*y and *h*z are components of magnetic field on x, y, z axis respectively.

Fig. 5. x-tilt and y-tilt assignments relative to ground

orientation will decay overtime. Therefore, Huang et al (2010) use magnetometers to detect the magnetic field of the earth. Using data from magnetometers, together with gyroscopes and accelerometers, the orientation in the beginning of each human walk can be found. Similarly, the orientation of the sensor module in stance phase, when the sensor module is in a near-static status, can be updated in each gait. With the updates of orientation in stance phases, the integral drifts of rotated angles, which increase linearly over time, are

To find the near-static orientation, the Euler angles *ψ*, *θ* and *φ* must be calculated. Because gyroscopes cannot be used in a near-static status, signals from accelerometers and magnetometers have to be used to calculate the orientations of the sensor module. However, these angles cannot be obtained directly. To resolve this problem, the tilts and heading of

Both x-tiltαand y-tilt β of the sensor module, or the sensor on it, as shown in Fig. 5, can be

2 2 atan 2 , *x yz*

2 2 atan 2 , *y x z*

where the two-argument function, atan2, is a variation of the arctangent function. For any real arguments *x* and *y*, which are not both equal to zero, atan2(y, x) is the angle in radians between the positive x-axis of a plane and the point given by the coordinates (x, y) on it.

The heading of a pedestrian, thus that of the sensor module on him/her, can be calculated using the x-axis and y-axis magnetic field outputs if the sensor module is level in a horizontal plane. As shown in Fig. 6, only the *x* and *y* components of the Earth's magnetic field, are used

y x

*a aa* (10)

*a aa* (11)

atan 2( , ) *h h* (12)

x

β

α

y

found simultaneously using acceleration outputs of all three axes as below:

when determining the heading of sensor module. The headingγ is defined by:

where *h*x, *h*y and *h*z are components of magnetic field on x, y, z axis respectively.

Sensor

Horizontal Plane Gravity Vector

Fig. 5. x-tilt and y-tilt assignments relative to ground

greatly reduced.

the sensor module need to be investigated.

Fig. 6. Heading defined in the horizontal x-y plane

No matter whether a person is standing or walking, the sensor module attached to the human body is not confined to a flat and level plane. This makes it more difficult to determine the heading direction, because the tilt angles of the x-axis and the y-axis are always changing and the sensor module cannot stay horizontal to the earth's surface. Errors caused by tilt angles can be large if not compensated.

To compensate the tilt of the sensor module when it is in an arbitrary position, the l-frame is transformed into the b-frame through three rotations with Euler angles ψ, θ and in sequence. The DCMs of the rotations are denoted as **R**1(*ψ*, z), **R**2(*θ*, y) and **R**3(*ϕ*, x) respectively. These Euler angles are related to the tilt but they are not all the tilts of the sensor module. The algorithm (Huang et al 2010) is as follows.

Rotations of the gravity vector from the l-frame to the b-frame are given by

$$\mathbf{g}^{\text{b}} = \mathbf{R}^{\text{T}} \cdot \mathbf{g}^{\text{l}} = \begin{bmatrix} -\sin\theta & \sin\phi\cos\theta & \cos\phi\cos\theta \end{bmatrix}^{\text{T}} \tag{13}$$

where **g**b = (gx gy gz]T and **g**l = (0 0 1]T. Therefore, we have

$$\theta = \operatorname{atan2}\left(\operatorname{g}\_{"\prime} \sqrt{\left\|\operatorname{g}^{\prime} + \operatorname{g}^{\prime}\right\|}\right) \tag{14}$$

The Application of Magnetic Sensors in Self-Contained Local Positioning 79

Many factors, including size, cost, dynamic range, sampling rate and bias, have to be considered in choosing an appropriate sensor module. Firstly, the size of the sensor module should be small enough to be installed unobtrusively on footwear. Consequently, all 3D gyroscopes, 3D accelerometers and 3D magnetometers should be integrated into, actually orthogonally mounted in, this inertial/magnetic sensor module. Secondly, the price of this sensor module should be low enough to meet budget constraints for the targeted applications. Thirdly, the dynamic range of the sensor module should meet the requirements of the applications. Fourthly, the sampling rate should be high enough to avoid sampling errors. The sampling rate of the sensors should be at least 100 Hz. Finally, because of the sensitivity of sensors to temperature, each sensor in a module should be

With all these considerations an inertial measurement unit (IMU) was selected to build the prototype for testing. The IMU provides serial digital outputs of 3D acceleration, 3D angular

In the experiments, the sensor module is mounted on the arch, i.e., the upper of closed footwear, as shown in Fig 7. The installation of the sensor should ensure that the relative

Using the methods introduced in Section 4 and the sensor module selected in Section 5.1, several experiments have been conducted at different paces in different places and different

times. Most of these experiments are two-dimensional (2D) tests.

individually compensated for bias, which is a function of temperature.

movement between the sensor module and the foot-ware is minimized.

**5.1 Sensor module selection**

velocity, and 3D magnetic field.

**5.2 Sensor module mounting**

Fig. 7. Sensor Module Mounting

**5.3 Field experiment**

This equation is actually the same as equation (10) if the sensor module is not moving. Using equation (13) and (14), we obtain

$$\phi = \operatorname{atan2}\left(g\_y \operatorname{sign}(\cos \theta), g\_z \operatorname{sign}(\cos \theta)\right) \tag{15}$$

This equation is different from equation (11). Hence the angle of roll is not the y-tilt angle *β*. y-tilt should not be used as roll for orientation calculation and magnetometer compensation.

After both pitch *θ*and roll are found from acceleration data, the attitude, or yaw*ψ*, can be found from magnetic field data by solving the following simultaneous equation:

$$\mathbf{m}^{\mathsf{b}} = \mathbf{R}^{\mathsf{T}} \cdot \mathbf{m}^{\mathsf{l}} \tag{16}$$

where **m**b = [*m*x *m*y *m*z]T, **m**l = [*a 0 b*]T (*a* and *b* are the horizontal and vertical components of the magnetic field of the earth vector) and **R**T=**R**l T(*ψ*,z)**R**2T(*θ*,y)**R**3 T(,x). Hence we get

$$\mathbf{R}\_1^\mathrm{T} \left( \boldsymbol{\psi}, \mathbf{z} \right) \cdot \mathbf{m}^\mathrm{l} = \mathbf{R}\_2^\mathrm{T} \left( \boldsymbol{\theta}, \mathbf{y} \right) \cdot \mathbf{R}\_3^\mathrm{T} \left( \boldsymbol{\phi}, \mathbf{x} \right) \cdot \mathbf{m}^\mathrm{b} \tag{17}$$

As illustrated in Fig. 6, *h*x, *h*y and *h*z are the projections of magnetic field on the horizontal plane and the *z*-axis (pointing downward) respectively, i.e., **h**b = [*h*x *h*y *h*z]T =(acos*ψ*, -asin*ψ*]T. Then the compensated magnetic field can be obtained as below:

$$h\_x = m\_x \cos \theta + m\_y \sin \theta \sin \phi + m\_z \sin \theta \cos \phi \tag{18}$$

$$h\_y = m\_y \cos \phi - m\_z \sin \phi \tag{19}$$

$$h\_z = m\_x \sin \theta + m\_y \cos \theta \sin \phi + m\_z \cos \theta \cos \phi \tag{20}$$

Accordingly, the heading on the horizontal plane, or the Euler angle yaw, can be defined as

$$\boldsymbol{\nu} = \operatorname{atan2}\left[ -\boldsymbol{h}\_{\mathbf{y}}, \boldsymbol{h}\_{\mathbf{x}} \right] \tag{21}$$

The angle *ψ* calculated in equation (21) is actually a compass reading, i.e., the heading relative to magnetic north. To get the true heading, denoted as*ψ*T here, the magnetic declination *D* must be deduced from the compass reading*ψ*:

$$
\psi\_T = \psi - D \tag{22}
$$

where *D* is the difference between magnetic north and true north. This difference is caused by the tilt of the earth's magnetic field generator relative to the earth's spin axis.

### **5. Experimental study**

To implement the methods introduced in the Section 4,. A commercial sensor module shall be carefully selected And then mounted to on a person rigidly. The result of the on-site pedestrian tracking is promising.


*2.4. Media for Growing Yeast*

The following media were used for growing yeast. YPAD: 1% (*w*/*v*) yeast extract, 2% (*w*/*v*) bacto peptone, 40 mg/L adenine and 2% (*v*/*v*) glucose; YPAGly: 1% (*w*/*v*) yeast extract, 2% (*w*/*v*) bacto peptone, 40 mg/L adenine and 2% (*v*/*v*) glycerol; YPGALA: 1% (*w*/*v*) yeast extract, 2% (*w*/*v*) bacto peptone, 40 mg/L adenine and 2% (*v*/*v*) galactose. Solid media contained 2% (*w*/*v*) agar.


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**Chapter 8**

**Current Use of Thalamic Vim**

Naoki Tani, Ryoma Morigaki, Ryuji Kaji and

Additional information is available at the end of the chapter

Recent advances in neuroimaging and neurosurgical techniques provide a growing body of evidence suggesting that deep brain stimulation (DBS) is a powerful and safe therapeu‐ tic option for medically intractable Parkinson's disease (PD). For more than half a centu‐ ry, the thalamic ventrolateral (VL) nucleus has been an anatomical target for stereotaxy in treating movement disorders that include PD. It plays a pivotal role in the basal gangliathalamo-cortical circuit that is associated with motor brain functions. The entire output of the basal ganglia is directed to the motor cortex via the VL nucleus where the cerebellar and pallidal afferents terminate predominantly in the ventralis intermedius (Vim) nucleus and ventralis oralis (Vo) nucleus, respectively. In accordance with the general concept that the cerebellothalamic fiber connections participate in tremor genesis, thalamic Vim DBS is now used in the treatment of a wide variety of tremor subtypes with different etiologies. Indeed, thalamic Vim DBS can exert a striking therapeutic impact on tremor-dominant PD that exhibits better clinical prognoses and slower disease progression with less cognitive decline as compared to akinesia/rigidity-dominant PD. In patients with tremor-dominant PD, tremor suppression can be achieved irrespective of age, disease duration, or baseline disease severity. Based on recent advances in the understanding of the pathophysiology of tremor-dominant PD, this review introduces the current use of thalamic Vim stimulation

> © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Stimulation in Treating**

**Parkinson's Disease**

http://dx.doi.org/10.5772/57105

in treating patients with PD.

Satoshi Goto

**1. Introduction**

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**Chapter 8**