**9. Conclusion**

Thus, when discussing the above problem of cardiology and cardiac surgery, it should be noted that the statistics of detection of aortic malformations have indeed changed over the past decades, in terms of frequency, which come out on the third place after coronary heart disease and hypertension. A particularly large percentage refers to aortic stenosis. Another feature is the change in the etiological factors in the development of aortic heart defects. So, if earlier the rheumatic genesis of the development of aortic malformations prevailed all over the world, now the degenerative nature of the development of aortic malformations is clearly increasing.

The third feature is the change in the social structure of aortic heart disease, i.e. with a noticeable increase in the age of the population, the number of patients over 60 years of age who undergo open correction sharply prevails. It follows from the above that open corrections are also changed. It follows from the above that the principles of diagnosing aortic heart defects have also changed, non-invasive highly informative computer technologies are increasingly being used that help to accurately determine the surgical tactics of treatment and evaluate the results of corrections of aortic heart defects.

The combination of valvular pathology and atherosclerotic lesions of the coronary vessels and the aortic wall sharply increases. Accordingly, the number of simultaneous large reconstructive operations on the valves of the aorta and root, and coronary vessels and others is increasing. It follows from the above that the principles of diagnosing aortic heart defects have also changed, non-invasive highly informative computer technologies are increasingly being used that help to accurately determine the surgical tactics of treatment and evaluate the results of corrections of aortic heart defects. The combination of valvular pathology and atherosclerotic lesions of the coronary vessels and the aortic wall sharply increases.

Accordingly, the number of simultaneous large reconstructive operations on the valves of the aorta and root, and coronary vessels and others is increasing. Open corrections are made. Accordingly, there is a need to revise the approaches to studying the issues of etiology, clinic and diagnostics, determining the tactics of treatment, performing the stages of surgical correction and evaluating the results of the latter in patients with acquired aortic heart disease at the present stage of development of cardiology and cardiac surgery.


3. CAEVs for low-carbon mobility

Mainly, due to four revolutions that include vehicular automation, vehicle electrification, vehicular connectivity, and shared mobility, CAEVs can offer great possibilities in expanding mobility and accessibility, and can play a leading role in achieving low-carbon mobility [6, 7].

These major drivers (i.e., automated driving, electric powertrains, connectivity, shared mobility) can provide compelling transition to a low-carbon future. In terms of energy use and carbon emissions, the potential synergies from combining these drivers would be significant.

The studies show that autonomous vehicles with electric powertrains have 40% lower lifetime GHG emissions than ICE-based vehicles. AV technologies along with V2V communications could smooth traffic flows and minimize braking, thus, possibly increasing fuel economy from 23 to 39% [8].

Similarly, the appealing on-demand shared mobility along with vehicular automation may offer the possibilities to expand multi-modal scenario that would reduce car travel by well over half in 2050, thus, would reduce traffic congestion as well as CO2 emissions in 2050 less than one-third of the conventional vehicles [9].

#### 3.1 Vehicular automation

As Vehicular automation involves the use of AI, and multi-agent system (MAS) to assist the vehicle operation, CAEV can be referred as a smart or intelligent.

Vehicular automation includes automated vehicle dynamics control such as adaptive cruise control (ACC) and automated powertrain operations that can improve vehicle energy efficiency and reduce carbon emissions.

Automated driving would allow to reduce unnecessary accelerations as well as decelerations so that energy efficiency can be improved. Consequently, Vehicular automation may result in optimized efficiency, increased safety, smooth traffic flow, few accidents as well as less pollution due to completely automated fuel control.

and tailpipe emissions as well as reduce vehicle miles traveled (VMT),

Connected Autonomous Electric Vehicles as Enablers for Low-Carbon Future

mental impacts due to the existing transportation systems [10–13].

Adoption of CAEVs has the possibility to play a key role in addressing environ-

Deployment of CAEVs will yield immense changes in vehicle design, traveler behavior, mobility patterns and even urban planning that will have dramatic

Widespread deployment of CAEVs will lead to a dramatic rise in vehicle miles traveled (VMT) in future. The VMT will be increased mainly due to two factors: vehicles with empty occupancy before pick-up or after drop-off, and increasing

Zero occupancy vehicle travel might contribute significantly to VMT effects of

A study also shows that personally-owned CAEVs are likely to significantly increase the total VMT and carbon emissions, as reduction in parking areas could exacerbate these increases by stimulating more zero occupancy vehicle travels.

The advancement of CAEV technology and the growth of on-demand shared mobility services may provide essential alternatives to conventional personallyowned vehicles, and have the potential to alter the way in which people move

Mobility-as-a-Service (MaaS) may be effective means to reduce VMT by combining trips that are temporally and spatially similar, and improve utilization of multi-modal transit providers as more users adopt MaaS as a main source of transit. Thus, MaaS may furnish several benefits including improved energy efficiency,

Efficient driving generally furnishes increment in fuel economy. And an increase in fuel economy shall provide reduction in energy consumption, tailpipe

traffic congestion reduction, and carbon emission reductions.

Environmental impacts of CEAVs will depend on several factors. Some of them are listed as follows: effects of the total Vehicle-miles traveled (VMT); adop-

tion of Mobility-as-a-Service (MaaS); Fuel economy; and eco-driving and

CAEVs. Increase in VMT may lead to the increase in tailpipe emissions. Adoption of CAEVs will provide independent mobility to non-drivers, including elderly people, people with disabilities, and youths. Since vehicle ownership among these groups will be very low, shared mobility services will be appealing among them. A study estimates an increment of 14% in the overall VMT as a result of wide penetration of CAEVs and mobility services provided to

and vehicle energy consumption.

environmental impacts [14, 15].

4.1 Vehicle miles traveled

non-driving group.

around cities.

4.3 Fuel economy

17

emissions and air pollution [2].

4.2 Mobility-as-a-Service

platooning.

4. Environmental impacts of CAEVs

DOI: http://dx.doi.org/10.5772/intechopen.84287

demand of ride-hailing or ride-sharing services.


*Edited by Herman J. Mosquera Cuesta*

The all-encompassing term Space Science was coined to describe all of the various fields of research in science: Physics and astronomy, aerospace engineering and spacecraft technologies, advanced computing and radio communication systems, that are concerned with the study of the Universe, and generally means either excluding the Earth or outside of the Earth's atmosphere. This special volume on Space Science was built throughout a scientifically rigorous selection process of each contributed chapter. Its structure drives the reader into a fascinating journey starting from the surface of our planet to reach a boundary where something lurks at the edge of the observable, light-emitting Universe, presenting four Sections running over a timely review on space exploration and the role being played by newcomer nations, an overview on Earth's early evolution during its long ancient ice age, a reanalysis of some aspects of satellites and planetary dynamics, to end up with intriguing discussions on recent advances in physics of cosmic microwave background radiation and cosmology.

ISBN 978-953-51-0423-0

Space Science

Photo by sakkmesterke / iStock


2.8.2. Assay of ABTS+• Radical Scavenging Activity

The method reported by Awe et al. [29] was adopted for the assay of ABTS+• radical scavenging activity. First, ABTS was dissolved in phosphate buffered saline (PBS, 0.01 M, pH 7.4) to a 7 mM concentration. The ABTS solution was then mixed with an equal volume of K2S2O8 solution (2.45 mM) in the dark at room temperature to produce ABTS+•. After 16 h, the ABTS+• solution was adjusted to a suitable absorbance (0.70 <sup>±</sup> 0.02) using water at 734 nm. To 2.0 mL of the prepared ABTS+• solution, 0.5 mL of various concentrations of extract solution (25–150 μg/mL) was added. The reaction mixture was then incubated in the dark at room temperature. A total of 10 mins later, the absorbance of the mixture was immediately recorded at 734 nm. Trolox was used as a positive control in this experiment, and the percentage of ABTS+• radical scavenging was calculated using the Equation (4).


**4. Discussion**

The UV-Vis absorption peaks exhibited all the ZnNPs are way below the characteristic excitonic absorption peak value of 370 nm attributed to intrinsic band-gap absorption phenomena exhibited by the bulk ZnO under valence to conduction band electron transitions [36,38]. Similar reports of blue-shifted UV vis absorption peaks have been reported for ZnNPs synthesized through sol-gel [25,39], microemulsion route [37], and green synthesis approach [40]. Moreover, a similar UV-Vis peak at 347 nm has been observed for the starch (CA)-ZnNPs on laser ablation synthesis in an aqueous starch solution for 15 to 20 min which could be ascribed to the formation of a layered nanocomposite comprised of starch-β-Zn(OH)2 sheets [41]. The occurrence of a single sharp peak indicates the formation of monodispersed ZnNPs and therefore, the narrow size distribution pattern of the synthesized ZnNPs [42]. The dual or triple peaks indicate the polydisperse nature of the aqueous nano-sol probably due to the formation of larger agglomerates by coalescence during or post-nucleation process [43]. The albumin (CA) ZnNMs showed a sharp peak at 220 nm, and multiple stout peaks at 400, 620, 700, and 760 nm wavelength which showcased the highly polydisperse nature of the albumin (CA) ZnNMs aqueous sol. However, the small area under these multiple peaks is also indicative of the presence of variable larger-sized ZnNPs in low amounts.

On the estimation of the bandgap energy of different ZnNMs according to Einstein equation; Energy = hC/λ, where h = Plank's constant (6.626 × 10 −34 Joules sec), C = Velocity of light (3 × 10 8 m s −1 ) and λ = wavelength (nm), the calculated values were 5.90, 5.64, 3.76 and 3.50 eV for the sharp and distinct absorption peak wavelengths of 210, 220, 330 and 350 nm respectively. These values are quite high compared to the moderate band-gap energy (3.35 eV) of ZnO which corresponds to UV and deep blue region indicating O2p to Zn3d electron transitions [44]. These blue shifts in the band-gap energy may be ascribed to the inadvertent occurrence of other metal atom impurities that may have altered the electronic movement across the valence and conduction bands [45]. Similar blueshifts have been observed on doping of zinc with IIIa group metal atoms [46] and 3d transitions metals [45].

The particle size of less than 10 nm have been reported for NaOH-ethanol reaction mix [43] while ZnNPs generated from NaOH-isopropanol reaction mixture formed particles with the mean size dimensions of 189.0 ± 6.0 and 447.0 ± 22.0 nm by sonochemical and hydrothermal techniques respectively [47]. However, unlike Chen et al. [30] the ZnNPs size was larger for the albumin (CA) ZnNPs while similar size distribution and average size have been observed for thiourea (RA) [26], cellulose (CA) [29], and starch (CA) ZnNPs [27].

The use of a high concentration of ZnCl2 can lead to the formation of hexagonal layered plate-like crystals of simonkolleite [18]. However, the XRD spectra for NaOH and thiourea reducing agents exhibited the presence of only hexagonal zincite phase crystals. Similar diffraction peaks have been reported for ZnNPs obtained by green synthesis from fruit parts of *Citrullus colosynthis* [14] and cotton linter pulp [11]. The XRD spectra of the albumin (CA) ZnNMs showed a very broad peak spanning over 30 to 50 20◦ diffraction region with several substantially indistinct peaks within this range indicating the occurrence of mixed crystal phase. As Bovine serum albumin (BSA) exhibits affinity to adsorb or form protein corona structure on the surface of the ZnNPs [19,20], it may have restrained the coalescence of the smaller-sized particles to form larger aggregates during the nucleation process. However, the transmission electron micrograph indicated the formation of platelike hexagonal ZnNPs (Figure 2).

The DPPH FRSA of the ZnNMs may be ascribed to the transfer of electron density from oxygen atom in ZnNMs to N-atom odd electron in DPPH compound [48]. The antioxidant properties further depict the efficiency of the redox-catalysis reactions, electronic configuration, surface-interface effect, and biocompatibility of the ZnNMs. From the results obtained for the FRSA potential, it appears that the NPs of a particular size is critical beyond which any further reduction in the size of the NPs does not contribute significantly to the antioxidant behavior. These observations are in line with the anticipated size-dependent phenomena and larger surface area to volume ratio to be involved in improved neutralization/deactivation of the hydroxy (•OH) radicals [49]. Similar to observations of this study, the report on electron spin resonance (ESR) spectroscopy study of 4 to 15 nm AuNPs, showcased maximum antioxidant activity by 9 nm NPs and not by NPs with a size smaller than 9 nm [49]. Therefore, it can be argued that the dose [50,51] or concentration [52], specific surface, and crystallinity status [49] of the ZnNMs are relatively more critical features for altering the efficiency with which the ZnNMs interact with the DPPH radicals.

The fungal radial growth inhibition in the agar-well diffusion assay for the three test fungi indicated superior antimycotic activity of NaOH (RA) derived ZnNPs. Similar radial growth inhibition has also been reported for ZnO NPs by He et al. [11] for *Penicillium expansum* and *Botrytis cinerea*. The hyphal thinning effects can be observed for most of the ZnNMs evaluated in the study which appears to be a characteristic feature of any fungal tissue in response to nanoparticle challenge [53,54]. However, the morphological manifestations such as breakage of the cell wall and leakage of the cytoplasmic contents, and appearance of swollen hyphal cells indicate the alteration in the osmotic conditions and formation of physical pores in the cell wall/membrane of the treated fungal cells [19,23]. Coherent to the results of the mycelial inhibition in agar well assay as described in the previous Section 3.3.1, maximum impact on the hyphal morphology was recorded for the *Fusarium oxysporum*. However, the hyphal fragmentation can be observed in *Macrophomina phaseolina*, unlike the agar well assay which indicates the subtle cytological changes that occur in the fungal hyphae on treatment with ZnNMs. A substantially low concentrations of the ZnNMs have been evaluated in this study unlike the studies performed on *Pythyium* [23], *F. graminearum, A. flavus*, and *P. citrinum* [19], and *Aspergillus flavus* [14].

The genomic DNA fragmentation may be attributed to the physical and chemical properties of the NPs [55] including the size, concentration [56,57], chemistry [56–58], and surface functionalization [59]. Though within a fungal cell the predominant mechanism of degradation of the cellular DNA by ZnNPs is through formation of reactive oxygen species (ROS) which causes extensive DNA scissoring and fragmentation [7]. However, direct interactions of ZnNPs with DNA molecules involve the binding of DNA with ZnO nanoparticles to nucleobases [60]. The specific conjugation of the DNA on ZnO NPs surface has been further identified by Das et al. [61] evidenced through varied conductivity and mobility under electric field on an agarose gel. The results of this study effectively demonstrate that the ZnNPs can intercalate with the DNA and exhibit non-photocatalytic DNA

degradation unlike the sunlight-induced fragmentation of *Leishmania* DNA on incubation with Ag-doped ZnO NPs [62].

#### **5. Conclusions**

This study provides evidence on variation in the anti-mycotic, anti-oxidant, and DNA degrading effect of the ZnO nanoparticles generated through wet chemical synthesis methods by using different reducing or complexing/capping agents. Both the anti-oxidant and anti-mycotic potentials were not observed to follow strict nanoparticle size-dependence. Thus, the NaOH (RA) derived ZnNPs had zincite crystal phase and the particle size distribution ranging from 10 to 90 nm and possessed the highest antioxidant and antimycotic activities. This study also illustrates the subtle hyphal morphological changes occurring on the use of lower concentration of the ZnNPs which may span over a variety of manifestations ranging from thinning, fragmentation, swelling, and lysis of the fungal hyphae. These alterations in the hyphal morphology could be attributed to variability in the functionalizations or chemical functional groups present on the surface of the ZnNPs. Further, the fungal DNA-NaOH (RA) ZnNPs incubation study enunciated the DNA degradation and exacerbation of the total genomic DNA of the test pathogenic fungi in a concentration and time-dependent manner with complete degradation of the *Macrophomina phaseolina* genomic DNA after 24 h of incubation. It could also be inferred from the study that the DNA damaging effect of the ZnNPs is also fungal genera/species-specific and may vary according to the fungal culture being studied. Therefore, this work establishes the impact of the appropriate nano-crystallite size dimensions and surface functionalizations as the key factors that vary the anti-mycotic, anti-oxidant, and DNA degrading potentials of ZnNPs.

**Author Contributions:** Conceptualization, A.K.; methodology, A.K.; validation, A.K., P.M., J.K.; formal analysis, J.K., M.T., P.B., P.M.; investigation, A.K., P.M.; resources, A.K., M.A.A.; data curation, A.K.; writing—original draft preparation, A.S., A.K.; writing—review and editing, K.A.A.-E., M.A.A.; visualization, A.K.; supervision, A.K.; project administration, A.K.; funding acquisition, K.A.A.-E., A.S., M.A.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Rashtriya Krishi Vikas Yojana (RKVY), Government of India, Grant ID (RKVY-12, PC-1320-32) to Anu Kalia. Moreover, the current research was partly supported by the Science and Technology Development Fund (STDF), Joint Egypt (STDF)-South Africa (NRF) Scientific Cooperation, Grant ID. 27837 to Kamel Abd-Elsalam.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data is contained within the article.

**Acknowledgments:** The authors thank the Director of Research for the allocation of funds in the RKVY scheme.

**Conflicts of Interest:** The authors declare no conflict of interest.


**BIOLOGICAL CONTROL OF PEST AND VECTOR INSECTS**

Edited by **Vonnie D.C. Shields**

#### **Biological Control of Pest and Vector Insects**

http://dx.doi.org/10.5772/63274 Edited by Vonnie D.C. Shields

#### **Contributors**

Nabil El-Wakeil, Nawal Gaafar, Mahmoud Saleh, Huda Elbehery, László Nowinszky, János Puskás, Solange Maria De França, Douglas Rafael Silva Barbosa, Mariana Breda, Alice Maria Nascimento Araújo, Carolina Arruda Guedes, Yu-Shin Nai, Yu-Feng Huang, Tzu-Han Chen, Kuo-Ping Chiu, Chung-Hsiung Wang, Grzegorz Wegrzyn, Kinga Lukasiewicz, Marek Sanak, Hamadttu Abdel Farag El-Shafie, Darine Slama, Hamouda Babba, Emna Chaker, Anna Kourti, Luc Swevers, Dimitrios Kontogiannatos, Iwona Skrzecz, Flavia Dos Santos, Thais Chouin-Carneiro, Gary Chiang, Jennifer Chiang, Christian Ogaugwu, Ravi Durvasula, Juan Cibrian, Eliningaya Kweka, Epiphania Kimaro, Esther Gwae, Yakobo Petro, Imna Malele, Marc Kouam, Joseph Kamgno

#### **© The Editor(s) and the Author(s) 2017**

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Violations are liable to prosecution under the governing Copyright Law.

Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution 3.0 Unported License which permits commercial use, distribution and reproduction of the individual chapters, provided the original author(s) and source publication are appropriately acknowledged. If so indicated, certain images may not be included under the Creative Commons license. In such cases users will need to obtain permission from the license holder to reproduce the material. More details and guidelines concerning content reuse and adaptation can be foundat http://www.intechopen.com/copyright-policy.html.

#### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in Croatia, 2017 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

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**7. Psychological pre/post-surgical intervention for bariatric candidates**

Bariatric surgery is the most effective intervention for weight loss in the field of obesity management. Unfortunately, patients often do not achieve optimal results in term of weight loss maintenance over time and meet various difficulties related to eating behaviors and psychological functioning. Given the significant variation in weight long-term outcomes after surgery that could be partially attributed to a number of risk factors, including psychological factors, psychological pre-operative or post-operative interventions are increasingly being recommended for patients seeking or undergoing bariatric surgery [23].

The most delivered psychological interventions included Cognitive Behavioral Therapy (CBT) but promising evidence suggests the effectiveness of intervention based on Acceptance and Commitment Therapy (ACT) and Dialectical Behavioral Therapy (DBT).

Cognitive behavioral interventions typically include psychoeducation, goal setting, self-monitoring, stimulus control, problem solving, and reinforcement, for the promotion of lifestyle-related behavior change. Through the psychological intervention, patients are encouraged to recognize their problematic eating behaviors, learn strategies to cope with life stressors, and engage themselves in healthy eating behaviors and physical activity. Recently, David and colleagues [5] conducted a systematic review aimed to examine the effectiveness of cognitive behavioral interventions. Results showed that the most common techniques employed during the interventions were psychoeducation, self-monitoring, goal setting, stimulus control, cognitive restructuring, problem solving and reinforcement, as well as addressing ambivalence, improving self-care and prevention of relapse. Most of the interventions included in the study were delivered in person, or in combination with telephone or web. With respect to intervention outcomes, the main findings of the review revealed that 32% of included studies showed a significant impact on weight loss. Specifically, it seems that the higher impact on weight loss was found in post-operative intervention, than the benefits of pre-operative interventions which were not maintained at follow-up. Other interventions outcomes were lifestyle behaviors, eating pathology, and psychological functioning. Results showed that psychological interventions seem to have an inconsistent influence on change in dietary habits and physical activity, but a limited number of studies examined this outcome. With respect to eating pathology, psychological interventions included in the study revealed a significant impact on reducing binge eating and emotional eating. Similarly, interventions had a positive impact on psychological functioning. Both pre-operative and post-operative interventions improved quality of life and reduced depression and anxiety symptoms.

Recently, other forms of psychological interventions were applied to bariatric patients. In a pilot RCT, Weineland and colleagues [24] compared an intervention based on ACT (two face to face session in combination with internet-based support) to usual treatment. Results showed that patients in the ACT condition significantly improved in eating disorders, body dissatisfaction, quality of life, and acceptance of thoughts and feelings related to weight.

Promising results were also obtained in an observational study [25] in which a pre-operative intervention based on DBT skills training combined with treatment as usual compared to a treatment as usual group. The intervention of DBT was focused on regulating emotions with emotion regulation, mindfulness, distress tolerance, and interpersonal effectiveness training. The results offered preliminary evidence in support of the effectiveness of a brief DBT skills training intervention in combination with treatment as usual in reducing eating pathology (binge eating and emotional eating).


6. Conclusion

based on the results shown in Figures 19 and 20, where pulse duration decreases with increasing energy within the limits of absorption saturation. Theoretically, shorter pulse durations can be achieved using an output coupler with less than 10% reflectivity. Practically, the threshold energy and the slope efficiency limit the choice of reflectivity. As Figure 22 shows, higher pump energies are needed to achieve lasing in a low-Q laser resonator. If the reflectivity of the output coupler is 10%, for instance, the lasing threshold for obtaining a 31.5-ps laser pulse is 80 μJ and the slope efficiency is 8%. However, increasing the output coupler reflectivity to

154 Numerical Simulations in Engineering and Science

30% decreases the threshold energy to 40 μJ and increases the slope efficiency to 10%.

Figure 23. Spectro-temporal evolution of the broadband, short-pulse Ce:LiCAF laser emission from an optimized low-Q (R2 = 30%), short cavity (L = 2 mm) oscillator. A short laser pulse with about 31.5-ps pulse duration, broadband emission centered at 288.5-nm wavelength, and 10 μJ output energy can be obtained practically from a 1-mm long, 1 mol% Ce3+ doped LiCAF crystal when pumped by a 266-nm, 75-ps pump pulse with 140 μJ pump energy. A slope efficiency of about 10% is also feasible with pump energies that are far from the crystal's absorption saturation and damage threshold.

Figure 24. Spectral profile of the broadband, short-pulse Ce:LiCAF laser emission from an optimized low-Q (R2 = 30%),

short cavity (L = 2 mm) oscillator. Temporal dynamics is shown in Figure 21b.

In summary, the transient cavity method is extended to a solid-state gain medium. Numerical simulations show that the same principles used to generate ultrashort laser pulses in dye lasers using this technique can be applied to solid-state gain media to generate ultrashort broadband pulses in the UV region. The laser gain medium was represented as a system of two homogeneous broadened singlet states and the numerical simulations solved the laser rate equations for broadband emission. The spectral and temporal evolution of the resulting laser emission was investigated in order to find the optimal cavity length and output coupler reflectivity that will give rise to the formation of resonator transients in the laser oscillator cavity. The calculations reveal that a laser oscillator with a short cavity and a low Q is ideal for the formation of resonator transients, which then lead to ultrashort (ps) laser emission. Specifically, a 2-mm cavity length and a 10% output coupler reflectivity can be used to generate a single 31.5 ps pulse using a 1-mm long Ce:LiCAF crystal with 1 mol% Ce3+ ion doping concentration. Although this work used Ce: LiCAF crystal as the laser gain medium, the transient cavity method can also be applied to generate ultrashort laser pulses using other rare earth-doped fluoride crystals.


**2.3. Embeddedness and sustainability**

Embeddeness is a concept widely used in food system research. Borrowed from economic sociology, the concept of embeddedness is widely used to characterize the different types of food systems: the dis-embedded globalized system and the more embedded localized system [23]. The embeddedness concept can be used to enrich the understanding of the broader sets of values tied to consumer motivations to buy at farmers markets [7]. As a matter of fact, local markets, where people exchange goods and services, are embedded in a network of social relations and meaningful systems of norms and rules [16]. Central to local food systems is the face-to-face connections and interactions between producers and consumers. Global markets are shaped less by social networks and norms, and impose instead compliance with their own norms of efficiency, rationality, optimizing and 'time saving' behavior. This global food system is experiencing societal, cultural, and moral disembedding. Ethical embeddedness is also being criticized resulting in a growing sense of distrust. Perhaps the development of more local systems is a result of social theory. This theory suggests that in situations of increasing distrust, alternative movements will emerge as consumers get organized to overcome their sense of insecurity [16]. As a result, alternative food schemes such as farmers' markets and community supported agriculture have emerged giving consumers access to food they perceive to be less harmful to the environment, society, and health.

The long term stability and sustainability of food systems is an interesting area of research. Academic literature on food research indicates a renewed interest in sustainable agriculture and food systems [4]. Sustainability has been described as having three conceptual pillars: social, environmental and economic factors. A sustainable food and agriculture system

1*local = place, vore = to consume*

requires economically viable farms and productive farmland to provide quality food [4]. Locally grown foods and farmers' markets contribute to the sustainability of the food system in many ways. Significant job and income impacts resulting from increased consumption of locally grown products are natural by-products of such systems. Ancillary benefits including increased access to healthy foods, business opportunities and drawing customers to down‐ town areas are also new trends.


*2.1. Synthesis of the NH4CN Polymers*

All of the NH4CN polymers synthetized in this work were produced as was previously described in [13] using the reaction times and temperatures indicated in Table 1. The initial equimolar concentrations of NaCN and NH4Cl always were 1 M. Figure 1 shows some experimental details of the synthesis process.


*Article* **A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor**

**Robert Hudec, Slavomír Matúška, Patrik Kamencay \* and Miroslav Benco**

Faculty of Electrical Engineering and Information Technology, University of Zilina, 01026 Zilina, Slovakia<EMAIL_ADDRESS>(R.H<EMAIL_ADDRESS>(S.M<EMAIL_ADDRESS>(M.B.) **\*** Correspondence<EMAIL_ADDRESS>Tel.: +421-41-513-2225

**Abstract:** Bedsores are one of the severe problems which could affect a long-term lying subject in the hospitals or the hospice. To prevent lying bedsores, we present a smart Internet of Things (IoT) system for detecting the position of a lying person using novel textile pressure sensors. To build such a system, it is necessary to use different technologies and techniques. We used sixtyfour of our novel textile pressure sensors based on electrically conductive yarn and the Velostat to collect the information about the pressure distribution of the lying person. Using Message Queuing Telemetry Transport (MQTT) protocol and Arduino-based hardware, we send measured data to the server. On the server side, there is a Node-RED application responsible for data collection, evaluation, and provisioning. We are using a neural network to classify the subject lying posture on the separate device because of the computation complexity. We created the challenging dataset from the observation of twenty-one people in four lying positions. We achieved a best classification precision of 92% for fourth class (right side posture type). On the other hand, the best recall (91%) for first class (supine posture type) was obtained. The best F1 score (84%) was achieved for first class (supine posture type). After the classification, we send the information to the staff desktop application. The application reminds employees when it is necessary to change the lying position of individual subjects and thus prevent bedsores.

**Citation:** Hudec, R.; Matúška, S.; Kamencay, P.; Benco, M. A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor. *Sensors* **2021**, *21*, 206 . https://doi.org/10.3390/s21010206

Received: 4 December 2020 Accepted: 27 December 2020 Published: 31 December 2020

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Keywords:** smart sensor; IoT system; Velostat; pressure sensor; convolutional neural network; data classification; position detection

## **1. Introduction**

The expected population development shows that the process of population aging will intensify in the coming years and will deepen long into the future. Therefore, one of the state authorities' priorities is the aging of the population and the quality of their life. This priority is focusing on the health insurance of older fellow citizens, including assistance in the field of social security. A lot of elderly people rely on the help of either their family members or the hospice employees. Many of them are long-term lying subjects for health reasons. There is a significant risk of bedsores in long-term lying subjects. This risk is also present in patients hospitalized for a long time. The problems described above are the primary motivation for this article. To prevent lying bedsores, we have designed a smart IoT system for detecting the position of a lying person using novel textile pressure sensors. The proposed system is intended to help hospital and hospice staff, but also family members who take care of a person in the prevention of lying bedsores. Sixty-four novel textile pressure sensors placed on the mattress toppers provide us with information about the position of the lying subject. We transfer this information to the cloud, evaluate it, and then display it on a desktop application. The application reminds employees when it is necessary to change the lying position of individual subjects and thus prevent bedsores.

There are several papers published with a similar topic to our proposed complex solution. Authors in [1] presented a complex system based on an artificial neural network for in-bed posture classification. Unlike our solution, they used the commercially available pressure sensor mattress. The mattress consists of a 2.5 mm-thick bedsheet containing a matrix of 64 × 27 textile made piezo-resistive pressure sensors. They carried out the experiments on 12 healthy adults using histogram of gradients and local binary patterns feature extraction and feed-forward artificial neural network as a classification method. The method classifies the lying posture into one of the four postures (supine, prone, left, and right lateral side). They achieved accuracy at 97.9%. As the authors stated in the conclusion, their presented work did not offer a comprehensive solution to the problem. It presents a potential technique to make a fundamental step towards prevention.

Authors in [2–4] proposed the solution based on their own version of the bedsheetsensing element. Hong [2], in his research article, presents a smart care bed for elderly patients. The smart bed features several pressure sensors FSR 408 and FSR 406. Their primary motivation was to prevent falls and bedsores. As the final implementation, he presented a real-time pressure-sensing algorithm capable of deciding on the possibilities of bedsores and falling accidents by considering both the intensity and the duration of the pressure of specific body parts. Abdelmoghith et al. [3] provide a solution that reduces the possibility of developing bedsores for long-term patients. They created an IoT-based healthcare monitoring system based on their prototype of a mattress. They used FSR 406 pressure sensors for body pressure distribution and DHT11 for temperature measurement. The proposed system did not classify the lying person's posture but only monitored the subject's mobility. The system provides crucial information using a mobile application based on the collected data. Authors in [4] also use the sensors from the FSR family to determine the pressure distribution of the lying person. They demonstrated the concept of a pressure sensing/monitoring system for pressure ulcer prevention. They equipped the bedsheet with 16 sensors and measured their resistance using an Arduino-based board. However, they did not solve the problem of laying person posture classification and provide only the concept of smart bedsheet.

The findings presented in [5] offer another approach to preventing bedsores. They used wearable computing and a deep learning approach to pressure ulcer prevention. To get the information about the lying person, they used a non-invasive system of wearable sensors based on inertial sensor. They can estimate the positions of the patients, and send an alert signal when the subject remains in the same position for too long a period. The disadvantage of this solution is that the lying person has to wear the same piece of electronic equipment. Authors in [6] propose a similar solution. They used a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. They evaluated nine different accelerometer positions on the human body and they achieved the best F1 scores at 97.8% in lying postures detection.

In the papers mentioned above, authors use commercially available pressure sensors in their prototypes or accelerometer to get information about the lying person. On the other hand, the works presented in [7–9] propose the solution for pressure distribution measurement based on their own sensors. For instance, Saenz-Cogollo et al. [7] present a pressure mapping mat for tele-home care applications. They developed a mat-like pressure mapping system based on a single layer textile sensor. The sensor is fabricated by embroidering silver-coated yarns on a piece of light cotton fabric and creating pressuresensitive resistive elements by stamping the conductive polymer at the crossing points of conductive stitches. The mat consists of an array of 32 × 32 sensing elements. They demonstrated the functionality and performance by comparing the proposed mat with the commercial pressure platform. Li et al. [8] present a low-cost textile smart map for step pressure sensing based on multiple layer structure with polyester textile, conductive copper taffeta, and Velostat. The matrix of sensors consists of 8 × 10 sensing points. They used an Arduino-based board for transferring the measured data into the personal computer for evaluation and visualization. The study presented in [9] describes a low-cost smart portable sitting mat that can measure the sitting plantar pressure distribution in real-time. The smart mat consists of 576 pressure-sensing elements made by a low-cost E-Textile. The

presented solution uses voltage feedback non-scanning electrode to eliminate the cross-talk effect between the sensing elements. The primary goal of the presented work is the creation of a low-cost and effective sensing pressure mat.

Our system relies on novel textile pressure sensors based on electrically conductive yarn and the Velostat. Suprapto et al., in their study [10], also rely on the Velostat-based pressure sensors. They presented a characteristic of a 32 × 32 sensor matrix for foot pressure distribution measurements. The single proposed sensor is constructed from dual electrodes placed on the top and underneath Velostat as the sensing material with a size of 7 × 7 mm. Based on their findings, the proposed sensor shows a relatively linear force–conductance response. A similar sensor design is presented by the authors in [11]. Their sensor is an easy-to-build textile pressure sensor created from low-cost conventional anti-static sheets and conductive woven fabrics. The sensor consists of three layers, the top conductive layer, the middle low-density polyethylene with a carbon sheet, and the bottom conductive layer. The size of the sensor is approximately 23 × 23 mm. From the evaluated characteristics, they concluded that the proposed design is suitable for didactic, healthcare, and lifestyle applications. Authors in [12,13] also described Velostat-based pressure sensors; moreover, the sensor design pattern is the same. The sensor consists of three layers, where the Velostat is in the middle. Tihak and Boskovic [14] carried out the experiments demonstrating the development of pressure sensor using Velostat. They presented typical problems and also discussed the causes and possible remedies. Lin and Seet [15] present an improved design of a textile pressure sensor. The sensor is constructed of conductive yarns and a dual-layer piezoresistive polymer (Velostat) as the sensing material. The sensor size is 10 × 10 mm. The proposed sensor can detect the load up to 1000 kPa with a relatively linear response.

The structure of our paper is as follows. The next chapter describes the IoT system proposal in detail, the novel textile pressure sensors development, hardware development, and whole topper production. Software descriptions follow next. We will give a view of how communications work between the system parts and will describe the Node-RED and desktop applications. Then, we will present the neural network used for lying posture classification and achieved results from our experiments. The discussion and conclusion section are at the end of our paper.

#### **2. IoT System Proposal**

Our primary goal was to create a smart IoT-based system for the detection of the position of a laying person in bed. The system should help staff who take care of long-term lying persons. The main task is to warn the staff that the person is lying in one position for a long time and it is necessary to change the lying person's position. In this way, it is possible to prevent unwanted bedsores. Figure 1 shows the proposal for the system concept.

The system consists of one or more Arduino-based hardware solutions with attached pressure sensors on the mattress topper, the server, computation development board, and software application on the personal computer (PC). For every bed, 64 novel sensors detect the pressure distribution. Because the bed is usually placed in the same place, the hardware solution runs from the electrical network and does not need a battery power source. The network-attached storage from QNAP holds the server solution. It features the Message Queuing Telemetry Transport (MQTT) broker for communication, Node-RED for the logic, and Mongo database for data storage. For the position classification, we use the neural network. The neural network needs a lot of computation power. Because of this requirement, computation runs on a separate device.

In our case, it is the Nvidia Jetson TX2 development board. On the PC runs a Qt-based desktop application where the user interacts with the system. The communication flow is as follows:

We will provide detailed information about the individual parts of our solution in the next sections.

**Figure 1.** A smart IoT-based system concept proposal.

#### *2.1. Sensor Design*

The overall design of an intelligent topper should meet several requirements that are critical to its conventional use. One of the main requirements for long-term use is the application of an electrically conductive thread, which is not significantly damaged by abrasion and common detergents. Other relevant parameters are simple industrial production, low consumption of electrically conductive textile materials, the comfort of a person lying on a bed, or resistance of the conductive paths of the mattress to mechanical damage or tearing. In our designs and experiments, we tested three electroconductive yarns. Figure 2a shows two 50 mm copper wires with a silver coating blended with polyester yarns. Figure 2b illustrates the Elitex yarn based on polyamide yarns by a chemical silver coating. Figure 2c shows the third yarn, 3 ltex yarn based on two blended multifilaments of polyamide yarns by chemical silver coating with antioxidant treatment.

Based on our experiences with sewing electroconductive patterns, we chose the 3 eltex yarn with antioxidant treatment. Its core consists of polyamide coated by pure silver with a fineness of 278 dtex and surface electrical resistance of 260 Ω/m. The yarns based on the two blended 50 μm Cu/Ag wire with polyester yarns are uncomfortable for humans. Similarly, the Elitex yarn is not suitable for long-time use. The reason is the peeling of the silver layer from the surface of the fiber during washing and subsequent mechanical stress. The selected yarn is gentle on the human skin, it is more resistant to the oxidation process, and it has better embroidery properties. Our motivation was to choose a yarn that will be suitable for sewing with embroidery machines, and the design pattern will be strong enough and reliable even in cases of an extreme load, and also resistant to ordinary.

The primary sensor design was based on the change of resistance or capacitance principle. Moreover, electrodes will be embroidered in one step, fully textile, and fabricated on one or two textile layers. Following these conditions, we have developed and fabricated more than 30 designs. We focused on determining the influence of various factors that affect the production process or the overall parameters of the sensor. These include, for example, the stitch type and the number of yarn cross-connections, the type, and construction of electroconductive yarn, embroidering speed, and continuity of seeding with minimizing the number of yarn cuts, time and temperature of ironing. Figure 3 shows relevant milestone designs. The sensor abbreviation is explained in the following Table 1.

**Figure 2.** Electroconductive yarns with chemical analysis. (**a**) Two 50 mm Cu/Ag wires blended with polyester yarns; (**b**) Elitex yarn; (**c**) 3 ltex yarn with antioxidant treatment.

The sensing place at the topper covers approximately 0.7 m2. This is an area where a person lies with an average figure and a weight of 90 kg. The sensing surface on which a person lies is defined by his/her shoulders and buttocks. Moreover, this part of a person's body accounts for about 60 percent of a person's total weight with an average load of approximately 7 g/cm2. In experiments, we used a load weight of 11.5 kg to simulate the pressure of the human body. The load is placed on the sensor through a dielectric plate with a size of 40 × 40 mm which reaches a pressure equivalent to 100 times the human load (718 g/cm2). It simulates extreme human strain and sensor resistance when a person is moving on a mattress. The goal is to achieve a change in measured values by at least two orders of magnitude with the lowest possible consumption of electrically conductive yarn. Table 2 presents the sensor's details and measured values. We used Velostat as the middle layer between the top and bottom electrodes in the sensor design. Velostat is a piezo-resistive material with high volume resistivity (<500/cm) and a thickness of only 200 microns. The Velostat is pressure sensitive, so squeezing it will reduce the resistance.

**Figure 3.** Sensing node designs in Wilcom DecoStudio and their fabrications. (**a**) PC2D; (**b**) PC3H; (**c**) PC3I; (**d**) PC3J; (**e**) PC3K; (**f**) PR4A; (**g**) PR4B; (**h**) PR4C.

**Table 1.** The definition of the sensor's abbreviations.

**Table 2.** Description of sensing node design based on the resistivity.

As it can be seen in Figure 4a, the design marked as PR4C achieved the highest change in resistance. Figure 4b illustrates the top and transversal view of the best design.

**Figure 4.** Measuring node realized by PR4C design. (**a**) Top view; (**b**) transversal view: (1) polyurethane foam, (2) electrically conductive yarn, (3) nonconductive yarn, (4) Velostat foil, (5) base fabric, (6) fixation with nonconductive yarn.

The developed primary sensor or sensing node of the intelligent topper should have the simplest design possible because it is a challenge to embroider a big sensor area. It will be used as a part of a sensing matrix of eight-by-eight sensing nodes, which should be sufficient to classify the person's sleep posture and to detect his/her activity during the night.

#### *2.2. Hardware Design*

We described the pressure sensor in previous part. Now it is necessary to design hardware solutions to measure, collect, and send the data to the server. In our design, we used a NodeMcu microcontroller. The NodeMcu is an Arduino-based board where the central processor is ESP8266. The communication via Wi-Fi with the server is also provided by this processor. The main advantage of this board is that it can be directly connected to the Wi-Fi and processing the data from sensors at the same time from one source code. The board features 16 general-purpose input–output pins and one analog input. In the hardware design, we followed the findings presented in [10–12]. We developed an electronic setup, which can measure each sensor pressure in the matrix of sensors using multiplexers. Figure 5 shows the proposed schematic.

Each sensor in the matrix of sensors is represented by its resistance Rs in parallel with a parasitic capacitance Cp. Rtop and Rbottom represent the yarn resistance. While our proposed novel sensor decreases the resistance with the applied pressure, we adjust the sensor sensing ability by adding R2 resistance in the front of trans-impedance amplifiers. NodeMCU manages the Mux1 and Mux2 to select a particular sensor sequentially, where Mux1 is responsible for selecting the column in the matrix and Mux2 for the row. We use the resistance Rdrain to reduce the effect of Cp by referring the non-active columns to the ground. To minimalize the cross-talk between rows of the matrix, we added the transimpedance amplifiers for each row. Usage of the trans-impedance amplifiers allows us to measure voltage Vsignal that is inversely proportional to the measuring sensor resistance.

**Figure 5.** Schematic of the hardware solution based on multiplexers, the trans-impedance amplifiers, and NodeMCU board.

The Figure 6 illustrates the source code flowchart diagram. The diagram starts with the initialization of all the necessary components. Then, the general input/output pins and communication peripheries enter the default state. Using provided Wi-Fi credentials, the NodeMcu connects to the Wi-Fi network. If the connection to the network fails, the system waits for 5 s and then repeats this operation until the successful login. The next step is a connection to the MQTT broker using pre-defined credentials. The MQTT protocol communication is provided by an external library—Adafruit MQTT Library ESP8266. To establish the MQTT connection, we need the client instance of the class Adafruit\_MQTT\_Client. The client connects to the MQTT broker. We have to create an additional object for receiving the responses from the broker. The instance of the class Adafruit\_MQTT\_Subscribe provides such an interface. An Adafruit\_MQTT\_Publish class provides the application programming interface (API) for sending data. After a successful connection, the object from Adafruit\_MQTT\_Subscribe class fetches the data from the MQTT broker using the channel identifier for reading commands. On the server side, the MQTT broker resends the commands from the desktop application on the PC. The valid commands are to start and stop the measurements. The measurement is performed in cycles every 1 s. We are using two analog multiplexers to select the particular sensor in the matrix of sensors on the output and input side.

The first multiplexer sets the output voltage on a specific output and similarly, the second multiplexer sets the specific input, which transfers the signal to the NodeMcu. On the NodeMcu, we are measuring the signal amplitude using an analog input pin. The signal amplitude increases with the dropping resistance. After finishing the measurement of the signals, NodeMcu initiates sequential data sending to the server. It is necessary to send data sequentially because of the processor ram limitation. The data are sent in JavaScript Object Notation (JSON) format using Adafruit\_MQTT\_Publish object.

**Figure 6.** NodeMcu source code flowchart for pressure data acquisition and sending to the server.

#### *2.3. Topper Design and Production*

Based on realized experiments mentioned in Section 2.1, we decided to use the pressure sensor with Velostat foil. The overall topper's design consists of a partial pressure sensor creating a structure of eight by eight sensing nodes. We set the sensor diameter to 40 mm with 100 mm distances in horizontal and vertical axes. Figure 7 shows the sensor design for both layers.

The fabrication of the sensor starts by drawing a mesh of eight by eight dots on the base fabric. They mark the center of each sensing point. It is necessary because the overall sensor is too big to fabricate at one step and the material of the base fabric is a little bit elastic. Moreover, the precise final composition of both layers relative to one sensor at all 64 nodes is difficult to manufacture. We embroidered the partial sensors in the marked positions. In our prototype, we used the nonconductive viscose-based yarn for upper embroidery and silver-coated polyamide yarn as bottom embroidering filaments. After that, the rest of the connecting lines and connectors were embroidered too. It is a fact, that topper will be placed on the standard PolyURethane (PUR) foam mattress. From this point of view, the sensor prototype should be massive with a robust design able to resist a crackdown of the electroconductive yarns when a human is moving over the bed. For this reason, we secured the connections between sensor nodes by triple stitching. Figure 8 illustrates the fabrication of the sensor's layers.

**Figure 7.** Final topper's pressure matrix design with eight by eight sensing nodes. (**a**) Top layer; (**b**) bottom layer.

**Figure 8.** Embroidered patterns. (**a**) Top layer; (**b**) bottom layer.

As the next step, we inserted circle Velostat foils between the fabricated layers in the positions of the sensors. Using the embroidery machine, we fixed both layers of the sensors. Finally, we stabilized the sensor matrix and applied it to the polyurethane foam. Figure 9 illustrates the final smart topper with our novel sensors.

**Figure 9.** Final topper production applied on 40 mm polyurethane foam.

The topper was created as a liner of polyurethane foam for a standard mattress of 2000 mm by 900 mm dimension. We chose to experimentally position it on the topper's base fabric. The upper limit of the sensor location was determined by the shoulder position and its value was 550 mm from the edge of the topper. Likewise, the lower limit is determined by the buttocks at a distance of 1250 mm. The place between the shoulders and buttocks represents the dominant body footprint of a person.

Overall topper design was composed by Wilcom DecoStudio software, that produces a digital embroidery pattern of stitches. Both patterns have been stored on a PC hard drive as a file with EMB (Embroidery format) extension and DST (Data Stitch Tajima) for a programmable embroidery machine. Further, they were embroidered by Barudan BEXT-S1501CII separately.

#### *2.4. IoT System Design*

The central unit of our IoT system is the NAS from QNAP [16,17]. This unit runs programs and server services that provide connectivity, management, data storage, and communication between the system peripheries. Two primary services are running on the server:

The QIoT suite is an application, which could be installed directly from the application center on NAS. QIoT suite is a complete and practical IoT private cloud platform for building and managing IoT applications. It integrates different services, which are necessary to provide a complex solution in the IoT world, into one application. The QIoT suite leverages popular tools like the MQTT broker [17,20], Node-RED, Freeboard, and supports multiple protocols and dashboards. MongoDB is a popular, general-purpose, document-based, distributed database, which is common in a cloud solution and IoT world. We use MongoDB for data storage and provision. Figure 10 shows the flowchart of the system data flow.

**Figure 10.** The system communication data flow between system components.

The primary data flow is as follows:

#### 2.4.1. Node-RED Application

The Node-RED application implements the logic part of the IoT system for detecting the position of a lying person using the private cloud platform. In our solution, we used two primary flows with the deployed application:

• A data processing flow.

• A command processing flow.

Figure 11 shows the command processing flow. HTTP request–response communication model implements the command processing in our solution. The flow starts with the input HTTP node. There is one compulsory argument in the HTTP request—command type.

**Figure 11.** The Node-RED command processing flow for things based on HTTP request–response communication model.

There are two valid values for command type, controlSending, and manageStorage. Use controlSending type to start or stop sending data from the topper. In this scenario, function Start measurement and Stop measurement prepares the command message and Qbroker out node sends the message to the topper. The arguments from the HTTP request appear in Node-RED as a JSON object. An example of such a JSON command is:

{ "type":"controlSending", "cmd":"1" }

The manageStorage type serves as an API point during the experimental measurements and data acquisition for training and testing neural networks. We will describe this command type later in the section Data Acquisition. The Figure 12 illustrates the data processing flow.

**Figure 12.** The Node-RED data processing flow for data collection, evaluation, propagation, and storage.

The flow starts with Qbroker in node. This input node listens on the topic "pressure-Data." The purpose of this flow is to collect, evaluate, and propagate the topper pressure data. The example of receiving data on the node is:

```
"payload": {
"row":4,
"data": ["52.00","52.00","86.00","122.00","132.00","113.00","32.00","38.00"]
}
```
Because of the NodeMCU ram limitation, we have to send the measured data in sequences. Therefore we need eight messages to send data from one measurement. One message contains the pressure data from one row in the matrix of sensors. The row tag indicates the row position. The function Accumulate data serves as a buffer function for one measurement. The IP network could cause different delays and therefore function task is to check the row order, sort the data if necessary, and prepare data from one measurement for later evaluation. The next two switch blocks inspect the data format and integrity. The next block is the Prepare data function. The function checks the topper state and prepares the data for the evaluation in the neural network. Afterward, the switch block validates the topper state. HTTP request–response block sends the data for evaluation in JSON format as a parameter in the HTTP request to the Jetson module. Data looks like:

```
payload: {
timestamp:<PHONE_NUMBER>,
subjectID: "subjet12",
notes: "",
data: [
[7, 8, 12, 12, 20, 123, 0, 0],
[8, 10, 27, 21, 28, 26, 0, 0],
[2, 9, 6, 8, 12, 8, 0, 0],
[6, 11, 11, 16, 22, 13, 3, 0],
[19, 32, 39, 95, 103, 66, 8, 0],
[13, 16, 16, 28, 51, 24, 1, 0],
[18, 20, 24, 47, 82, 40, 6, 0],
[0, 0, 4, 10, 12, 6, 0, 0],
],
request: "evaluate"
}
```
After the evaluation in the Jetson module, the next function processes the HTTP response and sends information to the desktop application using web-socket and store all data to the database.

#### 2.4.2. Data Provisioning in Neural Network Training

There is one more flow in our application. This flow serves as an API point for neural network training purposes. The Figure 13 shows this flow.

**Figure 13.** The Node-RED flow providing the application programming interface (API) for neural network based on HTTP request–response communication model.

The flow starts with HTTP in node, where there are two compulsory parameters in the HTTP request, the pose identification, and request type. Based on the request tag, the function block Prepare DB query prepares the query for getting the data from the database. There are two valid request types, training, and testing. For training purposes, 70% of the data are used for one pose. The rest (30%) use the neural network for testing. The HTTP out node response contains all valid data from databases in JSON format.

#### 2.4.3. Desktop Application

We created a desktop application for the operating staff using the Qt framework. Figure 14 illustrates a primary application screen.

**Figure 14.** Desktop application for informing the staff about the subject identification, current state, current position, duration of the current position, and time to the next position change.

The application is using HTTP request–response model to send commands to start or stop measurements. Actual data like subject identification, current state, current position, duration of the current position, and time to the next position change give the staff the information about the subject lying on the smart topper. There is also a graphical representation of the current state of the lying subject. The red color represents the maximal pressure, while the blue color represents minimal or non-pressure on a particular sensor in the matrix of sensors. The displayed value represents the pressure in numerical form. This number goes from 0 to 140. The 0 value describes the state when there is no pressure applied to the sensor. The value of 140 represents the maximum load, which the sensor is capable of measuring.

#### **3. Experimental Results**

We will present the obtained experimental results in this section. First, we will provide the results from the single sensor resistive response measurements. Then, we will describe the data acquisition for training purposes. In the end, we will provide the results for lying posture detection using a Convolutional Neural Network (CNN) [21,22].

#### *3.1. Single Sensor Resistive Response Measurements*

We performed the resistive response of a single-sensor element to a load of 10 N over 200 cycles. We measured the sensor resistance every 100 ms. Figure 15a illustrates

the obtained response. There is a little variability in zero load resistance and also with the applied load. The minimum measured resistance during the response measurements was 1.5 kΩ, but with more applied force, the resistance goes down to 300 Ω, and this value is stable. The zero-load variability makes no difference in our system. The proposed hardware solution is most sensitive in the range of 10 kΩ, more precisely from 22 to 32 kΩ. Therefore, small variation in a range more than 32 kΩ does not affect the system. This was the reason why we added a 22 kΩ resistor in the front of trans-impedance amplifiers.

**Figure 15.** (**a**) The resistive response of a single-sensor element to a load of 10 N over 200 cycles. (**b**) The value of output signal as a function of the changing resistance.

We determined the value of the resistor based on the performed analysis of the output value change in the response of changing the resistance with a potentiometer. Figure 15b describes this observation.

#### *3.2. Data Acquisition*

We had to create a dataset based on our proposed smart topper for neural network learning. Together, we collected data from 21 subjects, of which 3 were women and 18 men. The subjects' weights varied from 45 to 125 Kg. The database was divided into training and a test set randomly. In our case, we used data collected from 15 subjects for training. The data collected from the remaining six subjects serve to test the performance of the trained model. In our work, we determined four different postures for laying person classification, namely supine, the left side, prone, and the right side. Figure 16 shows samples of pressure images acquired from different subjects lying in one of the four postures. As mentioned above, the subject's weight varies a lot. Based on the wide weight variance, posture classification is a very challenging task. For example, when we compared the pressure distribution of one posture for the subject with the lowest weight and the heaviest subject, the difference was enormous.

We created an automated graphical tool for data collection and annotated storage in the MongoDB on the server and used the hardware described in the previous section on hardware design. The measurements repeat every second. The measuring range is from zero to 140. Zero means minimal or no pressure applied on the sensor, while 140 represents the maximal pressure on the sensors. Each subject spent 30 s in each posture. After ten seconds of measurement, we asked the subject to stand up and lay down in the same posture but in a different position. The subject could lay in any desired position for the given posture that feels comfortable. In the end, we had 120 samples for each subject, 30 samples per posture. For training purposes, 450 samples were used for one pose. The remaining 180 samples use neural network for testing (see Table 3).

**Figure 16.** Pressure distribution example for lying postures, **Supine:** (**a**) subject 1, (**b**) subject 2, **Left side:** (**c**) subject 1, (**d**) subject 2, **Prone:** (**e**) subject 1, (**f**) subject 2, **Right side:** (**g**) subject 1, (**h**) subject 2.

**Table 3.** Dataset (four different postures).

#### *3.3. Proposed Methodology Using Modified CNN*

This section describes the structure of the proposed CNN (Figure 17). The convolutional neural network classifies an input image into categories: supine, the left side, prone, and the right side. We performed the experiments using modified CNN and the precision, recall, and F1 parameter represents the achieved results. The model of the modified CNN consists of these primary layers:

keras.Input(shape=input\_shape), layers.Conv2D(16, kernel\_size=(3, 3), activation="relu"), layers.Conv2D(32, kernel\_size=(3, 3), activation="relu"), layers.Conv2D(32, kernel\_size=(3, 3), activation="relu"), layers.Flatten(), layers.Dropout(0.25),

layers.Dense(num\_classes, activation="softmax"),

**Figure 17.** The block diagram of the modified convolutional neural network (CNN).

These basic operations (layers) form the core of almost every convolutional neural network. The task of the first layers is to extract the necessary samples from the input data. These first three layers are usually repeated several times. As you can see in Table 4, the layer consists of several functional maps. The main task of the functional map is the extraction of selected features using a convolution filter. The samples are then combined into feature map within the fully interconnected layers and perform a final operation called classification [23–25]. This modified CNN divides into nine main blocks (see Figure 17):

**Table 4.** The structure of the modified CNN (layers description).

In the other words, our modified CNN is trying to learn more and more abstract features. In the initial layers this network encodes low-level features such as edge detectors. In the following layers, the features for shapes such as multicolor gradients are described. In the last layers, there are features for individual objects or very complex shapes. Firstly, the input for CNN is data (8 × 8) which passes through the convolutional layer. These convolutional layers consist of a set of filters that are used to extract local image features. The activation function in the neural network determines the values of the outputs of the individual neurons based on their internal potential (the internal potential is calculated by multiplying the weights with the input). In our case, the Rectified Linear Unit (ReLu) as an activation function was used (this is a nonlinear function). Next, CNN applies the Max Pooling layer (sub-sampling) layer. In the Pooling operation, the feature map is divided into several sub-windows. Only the maximum value of each window is then left. The outputs from the individual windows are combined to create a new scaled-down feature map. When we apply this to all maps, a new set of feature maps is created, which then forms as an input to the next convolution layer, where the whole process is repeated. After the Max Pooling operation, the convolutional layer output is flattened through a fully connected layer. Finally, there is a SoftMax output layer for image classification. The SoftMax function assigns to each class the probability that the input image belongs to the

appropriate class. The sum of all probabilities is equal to 1. The output from last layer (fully connected layer) can be used as a descriptor for other machine learning algorithms [24–26].

The confusion matrix (Table 5) holds the results from each tested method. This matrix summarizes the result of the classification and has rows indexed by output variable classes (reality) and columns by classes that the model predicted (estimation/prediction). The rows of the confusion matrix represent the actual class (supine, the left side, prone, and the right side). In our case, 180 samples in each row for testing were used. On the other hand, the column of confusion matrix represents the predicted class (supine, the left side, prone, and the right side).

**Table 5.** The achieved results using modified CNN for data classification (confusion matrix).

Based on the experiments carried out, the supine posture classification achieved the best results at 0.84 in the term of the F1 parameter (see Table 6). On the other hand, the prone posture classification gets the lowest value at 0.79 from all postures (see Table 5). The resulting accuracy is obtained as the sum of correctly predicted samples to the sums of all samples (0.82).

**Table 6.** The evaluation criterion of the modified CNN.

The parameters (P, R, F1) were calculated from the obtained values (see Table 6). The precision is the ratio between True Positive (TP) and the sum of positive data (True Positive (TP) + False Positive (FP)). The recall is the ratio between True Positive (TP) and the sum of data from the actual class (True Positive (TP) + False Negative (FN)) as shown in Figure 18. The F1 score is a combination of precision and recall (weighted average value of precision and recall). The TP value indicates the number of correctly classified patterns of the class true, the value FP indicates the number of incorrectly classified patterns of the class true. The value TN indicates the number of incorrectly classified patterns of class false, and the value FN represents the number of incorrectly classified patterns of class false.

**Figure 18.** The evaluation metrices (precision, recall, F1 score).

#### **4. Discussion**

We have presented a smart IoT system for detecting the position of a lying person using novel textile pressure sensors. We introduced our novel textile pressure sensor based on the electrically conductive yarn and the Velostat. Our sensors showed a relatively stable resistive response. When we compare it with the pressure sensor presented in [7], which is also fabricated by sewing, their sensor provides a more stable resistive response to ours and is easier to manufacture. On the other hand, our sensor design is more robust and tear-resistant. Other works [10–12,15] use Velostat as the sensing element in their pressure sensors, but they do not use the sewing machine. In the comparison with other presented sensors, our sensor showed the biggest difference in measuring the resistance with the no-load and maximal load. For example, the resistance changes from 6 kΩ to approximately 700 Ω for the sensor presented in [10], respectively, from 2.5 kΩ to 400 Ω for the sensor proposed in [11]. The advantage of our sensor is also that it is based on materials which are gentle on the human skin and can be in direct contact with it.

To obtain the pressure distribution of the lying subject, we used sixty-four sensors. We created a matrix of sensors with eight rows and fabricated it on the base fabric. The final topper consisted of 40 mm thin polyurethane foam for a standard mattress of 2000 by 900 mm where the matrix of sensors is on the top side. Authors in [1] rely on the commercially available mattress for getting the laying person pressure distribution. The mattress has significantly more sensing points over our solution (1728). On the other hand, the works presented in [2–4] uses their version of the bedsheet sensing element and the number of the sensing elements used is 45 [2], 12 [3], 16 [4], respectively. They used commercially available sensors from the FSR family and only attach these sensors to the bed or sheet. The work presented in [1–4], and also our solution, use a non-invasive method and without electronics attached to the subject for the prevention of bedsores. In comparison, the works presented in [5,6] are also non-invasive, but they use an electronic component that needs to be attached to the subject. This can cause some level of discomfort for long-term patients.

In the end, we presented a complex solution for bedsores prevention based on our novel pressure sensor and deep learning. We determined four different postures for laying subject classification, namely supine, the left side, prone, and the right side. We created a challenging dataset and collect the data from 21 subjects. Three of them were women, and 18 were men. The dataset is challenging because the subject weight varies from 45 to 125 kg and the subject could choose the comfortable lying position in each posture. In

terms of classification accuracy comparison, the work presented in [1], is the closest to our solution. These authors also used four different postures for classification, and their dataset consists of data from the observation of 12 healthy adults. The big disadvantage shown by classification accuracy comparison is that they have a more precise pressure distribution over our solution. They achieved a high testing prediction accuracy of 97.9% while our solution's accuracy is 82.22% with the best F1 score of 84%. On the other hand, the solution presented in [2] achieved the lying posture recognition 87.3% and they have only three different postures, left lateral, supine, and right lateral. The solution based on a deep learning approach and an electronic component attached to the subject [5] achieved a high accuracy of 99.56% with F1 1.00%. They used six different postures for classification, where they added sitting and movement posture over our solution. The accelerometerbased solution presented in [6] also achieved a high F1 score that ranges from 95.2% to 97.8%. Overall, the accelerometer-based solution gets better classification results over the sheet-based solution, but there is a disadvantage that the subject has to wear a piece of electronic equipment.

#### **5. Conclusions**

In this paper, we presented a complex IoT-based solution for detecting the position of a lying person.  The novel sensor is based on the electrically conductive yarn and the Velostat. The performed experiments indicate a stable resistive response. We demonstrated the functionality of the whole solution and presented the application to the operating staff using the challenging dataset, which consists of data from 21 subjects. The modified CNN network classifies the collected data into one of the four lying postures. Our modified CNN achieved an overall accuracy of 82.22% and with the best F1 score of 84%.

**Author Contributions:** Conceptualization, S.M., R.H. and P.K.; methodology, R.H., P.K. and S.M.; software, M.B. and S.M.; validation, S.M., P.K. and R.H.; formal analysis, S.M. and R.H.; visualization, P.K.; supervision, R.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was funded by the Slovak Research and Development Agency under the project APVV-16-0190—Research of integration of textile functional system for biodata monitoring aimed at achievement synergy of human health, comfort and safety (BIO-IN-TEX) and APVV-18-0167—Smart clothing for E-health applications (E-clothing).

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** We would like to thanks to Peter Palˇcek for REM analyses and VÚTCH— CHEMITEX for providing the electrically conductive yarns. This work has been supported by the Slovak Research and Development Agency under the project No. PP-COVID-20-0100: DOLORES.AI: The pandemic guard system.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**