Bioinspired Designs and Biomimetic Applications of Triboelectric Nanogenerators

The emerging novel power generation technology of triboelectric nanogenerators (TENGs) is attracting increasing attention due to its unlimited prospects in energy harvesting and self-powered sensing applications. The most important factors that determine TENGs’ electrical and mechanical performance include the device structure, surface morphology and the type of triboelectric material employed, all of which have been investigated in the past to optimize and enhance the performance of TENG devices. Amongst them, bioinspired designs, which mimic structures, surface morphologies, material properties and sensing/power generation mechanisms from nature, have largely benefited in terms of enhanced performance of TENGs. In addition, a variety of biomimetic applications based on TENGs have been explored due to the simple structure, self-powered property and tunable output of TENGs. In this review article, we present a comprehensive review of various researches within the specific focus of bioinspired TENGs and TENG enabled biomimetic applications. The review begins with a summary of the various bioinspired TENGs developed in the past with a comparative analysis of the various device structures, surface morphologies and materials inspired from nature and the resultant improvement in the TENG performance. Various ubiquitous sensing principles and power generation mechanisms in use in nature and their analogous artificial TENG designs are corroborated. TENG-enabled biomimetic applications in artificial electronic skins and neuromorphic devices are discussed. The paper concludes by providing a perspective towards promising directions for future research in this burgeoning field of study

Smart Devices and Systems for Wearable Applications

Wearable technologies need a smooth and unobtrusive integration of electronics and smart materials into textiles. The integration of sensors, actuators and computing technologies able to sense, react and adapt to external stimuli, is the expression of a new generation of wearable devices. The vision of wearable computing describes a system made by embedded, low power and wireless electronics coupled with smart and reliable sensors - as an integrated part of textile structure or directly in contact with the human body. Therefore, such system must maintain its sensing capabilities under the demand of normal clothing or textile substrate, which can impose severe mechanical deformation to the underlying garment/substrate. The objective of this thesis is to introduce a novel technological contribution for the next generation of wearable devices adopting a multidisciplinary approach in which knowledge of circuit design with Ultra-Wide Band and Bluetooth Low Energy technology, realization of smart piezoresistive / piezocapacitive and electro-active material, electro-mechanical characterization, design of read-out circuits and system integration find a fundamental and necessary synergy. The context and the results presented in this thesis follow an “applications driven” method in terms of wearable technology. A proof of concept has been designed and developed for each addressed issue. The solutions proposed are aimed to demonstrate the integration of a touch/pressure sensor into a fabric for space debris detection (CApture DEorbiting Target project), the effectiveness of the Ultra-Wide Band technology as an ultra-low power data transmission option compared with well known Bluetooth (IR-UWB data transmission project) and to solve issues concerning human proximity estimation (IR-UWB Face-to-Face Interaction and Proximity Sensor), wearable actuator for medical applications (EAPtics project) and aerospace physiology countermeasure (Gravity Loading Countermeasure Skinsuit project)

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

Psychophysiological analysis of a pedagogical agent and robotic peer for individuals with autism spectrum disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, dependable, and less complicated environment, where the interaction complexity can be adjusted so as to account for these individuals’ needs. The first phase of this dissertation presents an artificial-intelligence-based tutoring system which uses an interactive computer character as a pedagogical agent (PA) that simulates a human tutor teaching sight word reading to individuals with ASD. This phase examines the efficacy of an instructional package comprised of an autonomous pedagogical agent, automatic speech recognition, and an evidence-based instructional procedure referred to as constant time delay (CTD). A concurrent multiple-baseline across-participants design is used to evaluate the efficacy of intervention. Additionally, post-treatment probes are conducted to assess maintenance and generalization. The results suggest that all three participants acquired and maintained new sight words and demonstrated generalized responding. The second phase of this dissertation describes the augmentation of the tutoring system developed in the first phase with an autonomous humanoid robot which serves the instructional role of a peer for the student. In this tutoring paradigm, the robot adopts a peer metaphor, where its function is to act as a peer. With the introduction of the robotic peer (RP), the traditional dyadic interaction in tutoring systems is augmented to a novel triadic interaction in order to enhance the social richness of the tutoring system, and to facilitate learning through peer observation. This phase evaluates the feasibility and effects of using PA-delivered sight word instruction, based on a CTD procedure, within a small-group arrangement including a student with ASD and the robotic peer. A multiple-probe design across word sets, replicated across three participants, is used to evaluate the efficacy of intervention. The findings illustrate that all three participants acquired, maintained, and generalized all the words targeted for instruction. Furthermore, they learned a high percentage (94.44% on average) of the non-target words exclusively instructed to the RP. The data show that not only did the participants learn nontargeted words by observing the instruction to the RP but they also acquired their target words more efficiently and with less errors by the addition of an observational component to the direct instruction. The third and fourth phases of this dissertation focus on physiology-based modeling of the participants’ affective experiences during naturalistic interaction with the developed tutoring system. While computers and robots have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition offer great potential for addressing this issue. The third phase presents a Bluetooth-enabled eyewear – EmotiGO – for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users’ usual activities. This phase presents the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement. The fourth and final phase attempts to model the students’ engagement levels using their physiological signals collected with EmotiGO during naturalistic interaction with the tutoring system developed in the second phase. Several physiological indices are extracted from each of the signals. The students’ engagement levels during the interaction with the tutoring system are rated by two trained coders using the video recordings of the instructional sessions. Supervised pattern recognition algorithms are subsequently used to map the physiological indices to the engagement scores. The results indicate that the trained models are successful at classifying participants’ engagement levels with the mean classification accuracy of 86.50%. These models are an important step toward an intelligent tutoring system that can dynamically adapt its pedagogical strategies to the affective needs of learners with ASD

A Framework for Profiling based on Music and Physiological State

The IoT (Internet of Things) is an emergent technological area with distinct chal-lenges, which has been addressed by the world research community. This disser-tation proposes the use of a knowledge-based framework capable of supporting the representation and handling of devices along with some autonomous inter-action with the human being, for creating added value and opportunities in IoT. With usability in mind, the objective lays in an attempt to characterize users’ physiological status mainly through music in a profiling approach. The idea is to produce a solution able to customize the environment by musical suggestions to the actual scenarios or mood that the users lie in. Such system can be trained to understand different physiological data to then infer musical suggestions to the users. One of the adopted methods in this work explores that thought, on whether the usage of a person’s physiological state can wield adequate sensorial stimulation to be usefully used thereafter. Another question considered in this work is whether it is possible to use such collected data to build user’s musical playlists and profile that tries to use the user’s physiological state to predict his or her emotional state with the objective to reach a well-being situation

Graphene Biosensors for Diabetic Foot Ulcer Monitoring

The prevalence of Diabetes Mellitus (DM) in the twenty-first century has increased drastically, consequently, the incidence of DM-related complications has increased as well. According to the International Diabetes Federation (IDF) in 2021, globally one in every ten adults aged from 20 to 79 years had DM. Approximately 15-34% of individuals with DM are likely to develop a Diabetic Foot Ulcer (DFU) throughout their lifetime. Unmonitored and in- fected DFU can lead to non-traumatic lower extremity amputation and worst-case cause morbidity. Therefore, it is of great importance to develop effective, rapid production, bio- compatible, low-cost, flexible, wearable, sustainable sensors to monitor objectively the ulcer healing state. This dissertation aims to meet this need through the development of tempera- ture and pH laser-induced graphene (LIG) sensors on paper, that could be included in smart bandages and medical wound dressings. During this dissertation, LIG on paper fabrication parameters were studied to obtain the most reproducible, durable, and good electrical per- formance. The production condition of the LIG used for the development of the sensors had an average sheet resistance value of 24.9Ω/ with 1.2 Ω/ of standard deviation. The ther- moresistive sensor developed is characterized by a negative temperature coefficient with a highly linear response, and a sensitivity of 0.71 %℃−1 from 26℃ to 40℃, a suitable interval for its application. The electrochemical cell produced works as a potentiometric pH sensor. Its working electrode (WE) was electropolymerized with polyaniline (PANI) a pH-sensitive bio- compatible electrolyte. The sensor demonstrated a Nernstian behavior with a sensitivity of 53.0 / and 2.3 / of standard deviation on the interval from 2 pH to 9 pH.A prevalência da Diabetes Mellitus (DM) no século XXI aumentou drasticamente, con- sequentemente, a incidência de complicações relacionadas com a DM também aumentou. Segundo a Federação Internacional de Diabetes em 2021, globalmente um em cada dez adultos com idades compreendidas entre os 20 e os 79 anos tem DM. Aproximadamente 15- 34% dos indivíduos com DM são suscetíveis de desenvolver uma úlcera do pé diabético (DFU) durante toda a sua vida. A DFU não monitorizada e infetada pode levar a uma amputa- ção não traumática das extremidades inferiores e causar morbilidade no pior dos casos. Por conseguinte, é de grande importância desenvolver sensores eficazes, de produção rápida, biocompatíveis, de baixo custo, flexíveis, viáveis e sustentáveis para monitorizar objetivamen- te o estado de cicatrização da úlcera. Esta tese visa responder a esta necessidade através do desenvolvimento de sensores de temperatura e pH induzidos por laser (LIG) em papel, que poderiam ser incluídos em ligaduras inteligentes e curativos médicos de feridas. Durante esta dissertação, foram estudados parâmetros de fabrico de LIG em papel para obter o mais re- produtível, durável, e bom desempenho elétrico. O valor da resistência da folha média da condição de produção utilizada para o desenvolvimento foi de 24.9 Ω/ com um desvio padrão de 1.2 Ω/. O sensor termoresistivo desenvolvido é caracterizado por um coeficiente de temperatura negativa com uma resposta altamente linear, e uma sensibilidade de 0.71 %℃−1 entre os 26℃ e 40℃, um intervalo adequado para a sua aplicação. A célula ele- troquímica produzida funciona como um sensor de pH potenciométrico. O seu elétrodo de trabalho (WE) foi electropolimerizado com polianilina (PANI) um eletrólito biocompatível sensível ao pH. O sensor demonstrou um comportamento Nernstiano com uma sensibilidade de 53.0 / e desvio padrão de 2.3/ no intervalo de 2 a 9 pH

Remote Assessment of the Cardiovascular Function Using Camera-Based Photoplethysmography

Camera-based photoplethysmography (cbPPG) is a novel measurement technique that allows the continuous monitoring of vital signs by using common video cameras. In the last decade, the technology has attracted a lot of attention as it is easy to set up, operates remotely, and offers new diagnostic opportunities. Despite the growing interest, cbPPG is not completely established yet and is still primarily the object of research. There are a variety of reasons for this lack of development including that reliable and autonomous hardware setups are missing, that robust processing algorithms are needed, that application fields are still limited, and that it is not completely understood which physiological factors impact the captured signal. In this thesis, these issues will be addressed. A new and innovative measuring system for cbPPG was developed. In the course of three large studies conducted in clinical and non-clinical environments, the system’s great flexibility, autonomy, user-friendliness, and integrability could be successfully proven. Furthermore, it was investigated what value optical polarization filtration adds to cbPPG. The results show that a perpendicular filter setting can significantly enhance the signal quality. In addition, the performed analyses were used to draw conclusions about the origin of cbPPG signals: Blood volume changes are most likely the defining element for the signal's modulation. Besides the hardware-related topics, the software topic was addressed. A new method for the selection of regions of interest (ROIs) in cbPPG videos was developed. Choosing valid ROIs is one of the most important steps in the processing chain of cbPPG software. The new method has the advantage of being fully automated, more independent, and universally applicable. Moreover, it suppresses ballistocardiographic artifacts by utilizing a level-set-based approach. The suitability of the ROI selection method was demonstrated on a large and challenging data set. In the last part of the work, a potentially new application field for cbPPG was explored. It was investigated how cbPPG can be used to assess autonomic reactions of the nervous system at the cutaneous vasculature. The results show that changes in the vasomotor tone, i.e. vasodilation and vasoconstriction, reflect in the pulsation strength of cbPPG signals. These characteristics also shed more light on the origin problem. Similar to the polarization analyses, they support the classic blood volume theory. In conclusion, this thesis tackles relevant issues regarding the application of cbPPG. The proposed solutions pave the way for cbPPG to become an established and widely accepted technology

The Application of Physiological Metrics in Validating User Experience Evaluation on Automotive Human Machine Interface Systems

Automotive in-vehicle information systems have seen an era of continuous development within the industry and are recognised as a key differentiator for prospective customers. This presents a significant challenge for designers and engineers in producing effective next generation systems which are helpful, novel, exciting, safe and easy to use. The usability of any new human machine interface (HMI) has an implicit cost in terms of the perceived aesthetic perception and associated user experience. Achieving the next engaging automotive interface, not only has to address the user requirements but also has to incorporate established safety standards whilst considering new interaction technologies. An automotive (HMI) evaluation may combine a triad of physiological, subjective and performance-based measurements which are employed to provide relevant and valuable data for product evaluation. However, there is also a growing interest and appreciation that determining real-time quantitative metrics to drivers’ affective responses provide valuable user affective feedback. The aim of this research was to explore to what extent physiological metrics such as heart rate variability could be used to quantify or validate subjective testing of automotive HMIs. This research employed both objective and subjective metrics to assess user engagement during interactions with an automotive infotainment system. The mapping of both physiological and self-report scales was examined over a series of studies in order to provide a greater understanding of users’ responses. By analysing the data collected it may provide guidance within the early stages of in-vehicle design evaluation in terms of usability and user satisfaction. This research explored these metrics as an objective, quantitative, diagnostic measure of affective response, in the assessment of HMIs. Development of a robust methodology was constructed for the application and understanding of these metrics. Findings from the three studies point towards the value of using a combination of methods when examining user interaction with an in-car HMI. For the next generation of interface systems, physiological measures, such as heart rate variability may offer an additional dimension of validity when examining the complexities of the driving task that drivers perform every day. There appears to be no boundaries on technology advancements and with this, comes extra pressure for car manufacturers to produce similar interactive and connective devices to those that are already in use in homes. A successful in-car HMI system will be intuitive to use, aesthetically pleasing and possess an element of pleasure however, the design components that are needed for a highly usable HMI have to be considered within the context of the constraints of the manufacturing process and the risks associated with interacting with an in-car HMI whilst driving. The findings from the studies conducted in this research are discussed in relation to the usability and benefits of incorporating physiological measures that can assist in our understanding of driver interaction with different automotive HMIs

Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective