Review on Smart Electro-Clothing Systems (SeCSs)

This review paper presents an overview of the smart electro-clothing systems (SeCSs) targeted at health monitoring, sports benefits, fitness tracking, and social activities. Technical features of the available SeCSs, covering both textile and electronic components, are thoroughly discussed and their applications in the industry and research purposes are highlighted. In addition, it also presents the developments in the associated areas of wearable sensor systems and textile-based dry sensors. As became evident during the literature research, such a review on SeCSs covering all relevant issues has not been presented before. This paper will be particularly helpful for new generation researchers who are and will be investigating the design, development, function, and comforts of the sensor integrated clothing materials

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

Open Source Quantitative Stress Prediction Leveraging Wearable Sensing and Machine Learning Methods

The ability to monitor physiological parameters in an individual is paramount for the evaluation of physical health and the detection of many ailments. Wearable technologies are being introduced on a widening scale to address the absence of low-cost and non-invasive health monitoring as compared to medical grade equipment and technologies. By leveraging wearable technologies to supplement or replace traditional gold-standard measurement techniques, the research community can develop a deeper multifaceted understanding of the relationship between specific physiological parameters and particular health conditions. One particular research area in which wearable technologies are beginning to see application is the quantification of physical and mental stress levels in individuals through brainwave and physiological feature monitoring. At present, these methods are time consuming, invasive, expensive, or some combination of the three. This thesis chronicles the development and application of a novel open source wearable sensing platform to the field of stress and fatigue estimation and quantization. More specifically, the garment in its current configuration monitors heart rate, blood oxygen saturation, skin temperature, respiration rate, and skin conductivity parameters to explore the relationship between these parameters and various self-reported stress measures. Utilizing machine-learning methods, subject-specific models were generated in an n=1 study which predicts the self-perceived stress level of the subject with an accuracy of between 92% and 100%. The garment was developed with a modular interface and open source code base to allow and encourage reconfiguration and customization of the sensor array for other research applications. The dataset generated in this effort spans the early stages of the COVID-19 pandemic as the subject experienced increasing levels of isolation and tracks physiological parameters across two months via daily measurements

Engineering Novel High-Resolution Bioelectronic Interfaces From Mxene Nanomaterials

At the interface between Man and Machine are electrode technologies. Using recording electrodes, it is possible to observe and monitor the activity of neurons or nervous tissue, affording us with an understanding of the basic dynamics underlying behavior and disease. By interacting with the nervous system through stimulating electrodes, it is possible to impact brain function, or evoke muscle activation and coordination, paving the way for treatments to severe neurological and neuromuscular disorders. However, despite the exciting promises of electrode technologies, current state-of-the-art platforms feature stiff and high-impedance materials, which are incompatible with soft biological tissue. Additionally, many current technologies suffer from shorter lifetimes than may be desirable for a truly chronic implant or wearable health monitoring device. Recently, there has been a shift in focus towards two-dimensional nanocarbons as alternative materials for superior electrode technologies. This comes as a result of the enhanced flexibility, biocompatibility, and electronic and electrochemical properties that most carbon-based nanomaterials exhibit. In particular, the 2D nanomaterial titanium carbide MXene (Ti3C2Tx) has very recently shown great promise for a variety of biomedical applications. However, the long-term stability of Ti3C2Tx has not been fully explored, and it is still unknown whether Ti3C2Tx can be used for chronic bioelectronic applications. Accordingly, in this thesis, I address and explore the key advantages of Ti3C2Tx for biopotential sensing, with a particular emphasis on validating this unique material for chronic recording studies. First, I demonstrate the superior advantages of Ti3C2Tx for direct recording of biopotential signals at the skin level in humans. Second, I define the long-term stability of Ti3C2Tx MXene in dried film form, and explore modifications in synthesis and film assembly to improve the material’s lifetime. Third, I fabricate and validate Ti3C2Tx-based epidermal sensors that exhibit comparable recording capabilities to state-of-the-art clinical electrodes, firmly establishing Ti3C2Tx electrode technologies for future, chronic experiments. The processing and fabrication methods developed herein translate into mature technologies with unique properties that are comparable to state-of-the-art designs, thereby offering a novel bioelectronic platform with the potential to benefit a variety of fields in both the research and clinical settings

Методе за оцену електричне активности глатких мишића

Recording of the smooth stomach muscles' electrical activity can be performed by means of Electrogastrography (EGG), a non-invasive technique for acquisition that can provide valuable information regarding the functionality of the gut. While this method had been introduced for over nine decades, it still did not reach its full potential. The main reason for this is the lack of standardization that subsequently led to the limited reproducibility and comparability between different investigations. Additionally, variability between many proposed recording approaches could make EGG unappealing for broader application. The aim was to provide an evaluation of a simplified recording protocol that could be obtained by using only one bipolar channel for a relatively short duration (20 minutes) in a static environment with limited subject movements. Insights into the most suitable surface electrode placement for EGG recording was also presented. Subsequently, different processing methods, including Fractional Order Calculus and Video-based approach for the cancelation of motion artifacts – one of the main pitfalls in the EGG technique, was examined. For EGG, it is common to apply long-term protocols in a static environment. Our second goal was to introduce and investigate the opposite approach – short-term recording in a dynamic environment. Research in the field of EGG-based assessment of gut activity in relation to motion sickness symptoms induced by Virtual Reality and Driving Simulation was performed. Furthermore, three novel features for the description of EGG signal (Root Mean Square, Median Frequency, and Crest Factor) were proposed and its applicability for the assessment of gastric response during virtual and simulated experiences was evaluated. In conclusion, in a static environment, the EGG protocol can be simplified, and its duration can be reduced. In contrast, in a dynamic environment, it is possible to acquire a reliable EGG signal with appropriate recommendations stated in this Doctoral dissertation. With the application of novel processing techniques and features, EGG could be a useful tool for the assessment of cybersickness and simulator sickness.Снимање електричне активности глатких мишића желуца може се реализовати употребом електрогастрографије (ЕГГ), неинвазивне методе која пружа значајне информације везане за функционисање органа за варење. Упркост чињеници да је откривена пре више од девет деценија, ова техника још увек није остварила свој пун потенцијал. Основни разлог за то је недостатак стандардизације који условљава ограничења у смислу поновљивости и упоредивости између различитих истраживања. Додатно, варијабилност која је присутна у примени различитих препоручених поступака снимања, може смањити интерес за употребу ЕГГ-а код широког опсега потенцијалних корисника. Наш циљ је био да пружимо евалуацију поједностављене методе мерења тј. протокола који укључује само један канал током релативно кратког временског периода (20 минута) у статичким условима са ограниченим кретањем субјекта тј. у мировању. Такође, приказали смо наше ставове у вези најприкладније позиције површинских електрода за ЕГГ снимање. Презентовали смо и резултате испитивања метода, на бази обраде видео снимка као и фракционог диференцијалног рачуна, за отклањање артефаката помераја – једног од највећих изазова са којима је суочена ЕГГ метода. За ЕГГ је уобичајено да се користе дуготрајни протоколи у статичким условима. Наш други циљ био је да представимо и оценимо употребљивост супротног приступа – краткотрајних снимања у динамичким условима. Реализовали смо истраживање на пољу оцене активности желуца током појаве симптома мучнине изазване виртуелном реалношћу и симулацијом вожње. За потребе методе за оцену електричне активности желуца, предложили смо три нова параметра за квантификацију ЕГГ сигнала (ефективну вредност амплитуде, медијану и крест фактор) и извршили процену њихове прикладности за оцену гастроинтестиналног тракта током коришћења виртуелне реалности и симулатора вожње. Закључак је да ЕГГ протокол у статичким условима може бити упрошћен и његово трајање може бити редуковано, док је у динамичким условима могуће снимити одговарајући ЕГГ сигнал, али уз праћење препорука наведених у овој тези. Употребом нових техника за процесирање сигнала и прорачун одговарајућих параметара, ЕГГ може бити корисна техника за оцену мучнине изазване коришћењем симулатора и производа виртуелне реалност

Abstracting information on body area networks

Healthcare is changing, correction...healthcare is in need of change. The population ageing, the increase in chronic and heart diseases and just the increase in population size will overwhelm the current hospital-centric healthcare. There is a growing interest by individuals to monitor their own physiology. Not only for sport activities, but also to control their own diseases. They are changing from the passive healthcare receiver to a proactive self-healthcare taker. The focus is shifting from hospital centred treatment to a patient-centric healthcare monitoring. Continuous, everyday, wearable monitoring and actuating is part of this change. In this setting, sensors that monitor the heart, blood pressure, movement, brain activity, dopamine levels, and actuators that pump insulin, “pump” the heart, deliver drugs to specific organs, stimulate the brain are needed as pervasive components in and on the body. They will tend for people’s need of self-monitoring and facilitate healthcare delivery. These components around a human body that communicate to sense and act in a coordinated fashion make a Body Area Network (BAN). In most cases, and in our view, a central, more powerful component will act as the coordinator of this network. These networks aim to augment the power to monitor the human body and react to problems discovered with this observation. One key advantage of this system is their overarching view of the whole network. That is, the central component can have an understanding of all the monitored signals and correlate them to better evaluate and react to problems. This is the focus of our thesis. In this document we argue that this multi-parameter correlation of the heterogeneous sensed information is not being handled in BANs. The current view depends exclusively on the applica- tion that is using the network and its understanding of the parameters. This means that every application will oversee the BAN’s heterogeneous resources managing them directly without taking into consideration other applications, their needs and knowledge. There are several physiological correlations already known by the medical field. Correlating blood pressure and cross sectional area of blood vessels to calculate blood velocity, estimating oxygen delivery from cardiac output and oxygen saturation, are such examples. This knowledge should be available in a BAN and shared by the several applications that make use of the network. This architecture implies a central component that manages the knowledge and the resources. And this is, in our view, missing in BANs. Our proposal is a middleware layer that abstracts the underlying BAN’s resources to the applica- tion, providing instead an information model to be queried. The model describes the correlations for producing new information that the middleware knows about. Naturally, the raw sensed data is also part of the model. The middleware hides the specificities of the nodes that constitute the BAN, by making available their sensed production. Applications are able to query for information attaching requirements to these requests. The middleware is then responsible for satisfying the requests while optimising the resource usage of the BAN. Our architecture proposal is divided in two corresponding layers, one that abstracts the nodes’ hardware (hiding node’s particularities) and the information layer that describes information available and how it is correlated. A prototype implementation of the architecture was done to illustrate the concept.This work was partially supported by PhD scholarship SFRH/BD/28843/2006 from Fundação da Ciência e Tecnologia from Portugal

From hospital to home. The application of e-health solutions for monitoring and management of people with epilepsy

Background. In the last 10 years, there has been an explosion in the development of mobile and wearable technologies. Recent events such as Covid 19 emergency, showed the world how clinicians need to focus more on the application of these technologies to monitor and manage their patients. Despite this, the use of innovative technologies is not now a common practice in epilepsy. This thesis aims to demonstrate how people with epilepsy (PWE) are ready to use these mobile and wearable technologies and how data collected from these solutions can have a direct impact on PWE’s life. Methods. A systematic literature search was performed to provide an accurate overview of new non- invasive EEGs and their applications in epilepsy health care and an online survey was performed to fill the literature gap on this topic. To accurately study the PWE’s experience using wearable sensors, and the value of physiological and non-physiological data collected from wearable sensors, we used EEG data collected from the hospital (RADAR-CNS), and we collected original data from an at-home study (EEG@HOME). The data can be divided into two main categories: qualitative data (online survey, semi-structured interviews), and quantitative data analysis (questionnaires, EEG, and additional non-invasive physiological variables). Results. The systematic review showed us how non-invasive portable EEGs could provide valuable data for clinical purposes in epilepsy and become useful tools in different settings (i.e., rural areas, Hospitals, and homes). These are well accepted and tolerated by PWE and health care providers, especially for the easy application, cost, and comfort. The information obtained on the acceptability of repeated long-term non-invasive measures at home (EEG@HOME) showed that the use of the portable EEG cap was in general well tolerated over the 6 months but, the use of a smartwatch and the e-seizure diary was usually preferred. The level of compliance was good in most of the individuals and any barriers or issues which affected their experience or quality of the data were highlighted (i.e., life events, issues with equipment, and hairstyle of patients). Semi-structured interviews showed that participants found the combination of the three solutions very well-integrated and easy to use. The support received and the possibility to be trained and monitored remotely were well accepted and no privacy issues were reported by any of the participants. Most of the participants also suggested how they will be happy to have a mobile solution in the future to help to monitor their condition. The graph theory measures extracted from short and/or repeated EEG segments recorded from hospitals (RADAR-CNS) allowed us to explore the temporal evolution of brain activity prior to a seizure. Finally, physiological data and non-physiological data (EEG@HOME) were combined to understand and develop a model for each participant which explained a higher or lower risk of seizure over time. We also evaluated the value of repeated unsupervised resting state EEG recorded at home for seizure detection. Conclusion. The use of new technologies is well accepted by PWE in different settings. This thesis gives a detailed overview of two main points. First: PWE can be monitored in the hospital or at home using new wearable sensors or smartphone apps, and they are ready to use them after a short training and minimal supervision. Second: repeated data collection could provide a new way of a monitor, managing, and diagnosing people with epilepsy. Future studies should focus on balancing the acceptability of the solutions and the quality of the data collected. We also suggest that more studies focusing on seizure forecasting and detection using data collected from long-term monitoring need to be conducted. Digital health is the future of clinical practice and will increase PWE safety, independency, treatment, and monitoring

Advances in Digital Processing of Low-Amplitude Components of Electrocardiosignals

This manual has been published within the framework of the BME-ENA project under the responsibility of National Technical University of Ukraine. The BME-ENA “Biomedical Engineering Education Tempus Initiative in Eastern Neighbouring Area”, Project Number: 543904-TEMPUS-1-2013-1-GR-TEMPUS-JPCR is a Joint Project within the TEMPUS IV program. This project has been funded with support from the European Commission.Навчальний посібник присвячено розробці методів та засобів для неінвазивного виявлення та дослідження тонких проявів електричної активності серця. Особлива увага приділяється вдосконаленню інформаційного та алгоритмічного забезпечення систем електрокардіографії високого розрізнення для ранньої діагностики електричної нестабільності міокарда, а також для оцінки функціонального стану плоду під час вагітності. Теоретичні основи супроводжуються прикладами реалізації алгоритмів за допомогою системи MATLAB. Навчальний посібник призначений для студентів, аспірантів, а також фахівців у галузі біомедичної електроніки та медичних працівників.The teaching book is devoted to development and research of methods and tools for non-invasive detection of subtle manifistations of heart electrical activity. Particular attention is paid to the improvement of information and algorithmic support of high resolution electrocardiography for early diagnosis of myocardial electrical instability, as well as for the evaluation of the functional state of the fetus during pregnancy examination. The theoretical basis accompanied by the examples of implementation of the discussed algorithms with the help of MATLAB. The teaching book is intended for students, graduate students, as well as specialists in the field of biomedical electronics and medical professionals

Time series morphological analysis applied to biomedical signals events detection

Dissertation submitted in the fufillment of the requirements for the Degree of Master in Biomedical EngineeringAutomated techniques for biosignal data acquisition and analysis have become increasingly powerful, particularly at the Biomedical Engineering research field. Nevertheless, it is verified the need to improve tools for signal pattern recognition and classification systems, in which the detection of specific events and the automatic signal segmentation are preliminary processing steps. The present dissertation introduces a signal-independent algorithm, which detects significant events in a biosignal. From a time series morphological analysis, the algorithm computes the instants when the most significant standard deviation discontinuities occur, segmenting the signal. An iterative optimization step is then applied. This assures that a minimal error is achieved when modeling these segments with polynomial regressions. The adjustment of a scale factor gives different detail levels of events detection. An accurate and objective algorithm performance evaluation procedure was designed. When applied on a set of synthetic signals, with known and quantitatively predefined events, an overall mean error of 20 samples between the detected and the actual events showed the high accuracy of the proposed algorithm. Its ability to perform the detection of signal activation onsets and transient waveshapes was also assessed, resulting in higher reliability than signal-specific standard methods. Some case studies, with signal processing requirements for which the developed algorithm can be suitably applied, were approached. The algorithm implementation in real-time, as part of an application developed during this research work, is also reported. The proposed algorithm detects significant signal events with accuracy and significant noise immunity. Its versatile design allows the application in different signals without previous knowledge on their statistical properties or specific preprocessing steps. It also brings added objectivity when compared with the exhaustive and time-consuming examiner analysis. The tool introduced in this dissertation represents a relevant contribution in events detection, a particularly important issue within the wide digital biosignal processing research field