Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Personalized data analytics for internet-of-things-based health monitoring

The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months

Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors

Smart aging : utilisation of machine learning and the Internet of Things for independent living

Smart aging utilises innovative approaches and technology to improve older adults’ quality of life, increasing their prospects of living independently. One of the major concerns the older adults to live independently is “serious fall”, as almost a third of people aged over 65 having a fall each year. Dementia, affecting nearly 9% of the same age group, poses another significant issue that needs to be identified as early as possible. Existing fall detection systems from the wearable sensors generate many false alarms; hence, a more accurate and secure system is necessary. Furthermore, there is a considerable gap to identify the onset of cognitive impairment using remote monitoring for self-assisted seniors living in their residences. Applying biometric security improves older adults’ confidence in using IoT and makes it easier for them to benefit from smart aging. Several publicly available datasets are pre-processed to extract distinctive features to address fall detection shortcomings, identify the onset of dementia system, and enable biometric security to wearable sensors. These key features are used with novel machine learning algorithms to train models for the fall detection system, identifying the onset of dementia system, and biometric authentication system. Applying a quantitative approach, these models are tested and analysed from the test dataset. The fall detection approach proposed in this work, in multimodal mode, can achieve an accuracy of 99% to detect a fall. Additionally, using 13 selected features, a system for detecting early signs of dementia is developed. This system has achieved an accuracy rate of 93% to identify a cognitive decline in the older adult, using only some selected aspects of their daily activities. Furthermore, the ML-based biometric authentication system uses physiological signals, such as ECG and Photoplethysmogram, in a fusion mode to identify and authenticate a person, resulting in enhancement of their privacy and security in a smart aging environment. The benefits offered by the fall detection system, early detection and identifying the signs of dementia, and the biometric authentication system, can improve the quality of life for the seniors who prefer to live independently or by themselves

Continuous monitoring of vital parameters for clinically valid assessment of human health status

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa, Faculdade de Ciências, 2019The lack of devices suitable for acquiring accurate and reliable measures of patients' physiolog-ical signals in a remote and continuous manner together with the advances in data acquisition technol-ogies during the last decades, have led to the emergence of wearable devices for healthcare. Wearable devices enable remote, continuous and long-term health monitoring in unattended setting. In this con-text, the Swiss Federal Laboratories for Material Science and Technology (Empa) developed a wearable system for long-term electrocardiogram measurements, referred to as textile belt. It consists of a chest strap with two embroidered textile electrodes. The validity of Empa’s system for electrocardiogram monitoring has been proven in a clinical setting. This work aimed to assess the validity of the textile belt for electrocardiogram monitoring in a home setting and to supplement the existing system with sensors for respiratory monitoring. Another objective was to evaluate the suitability of the same weara-ble, as a multi-sensor system, for activity monitoring. A study involving 12 patients (10 males and 2 females, interquartile range for age of 48–59 years and for body mass indexes of 28.0–35.5 kg.m-2) with suspected sleep apnoea was carried out. Overnight electrocardiogram was measured in a total of 28 nights. The quality of recorded signals was assessed using signal-to-noise ratio, artefacts detection and Poincaré plots. Study data were compared to data from the same subjects, acquired in the clinical setting. For respiratory monitoring, optical fibre-based sensors of different geometries were integrated into the textile belt. Signal processing algorithms for breathing rate and tidal volume estimation based on respiratory signals acquired by the sensors were developed. Pilot studies were conducted to compare the different approaches for respiratory monitoring. The quality of respiratory signals was determined based on signal segments “sinusoidality”, evaluated through the calculation of the cross-correlation between signal segments and segment-specific reference waves. A method for accelerometry-based lying position recognition was proposed, and the proof of concept of activity intensity classification through the combination of subjects’ inertial acceleration, heart rate and breathing rate data, was presented. Finally, a study with three participants (1 male and 2 females, aged 21 ± 2 years, body mass index of 20.3 ± 1.5 kg.m-2) was conducted to assess the validity of the textile belt for respiratory and activity monitoring. Electrocardiogram signals acquired by the textile belt in the home setting were found to have better quality than the data acquired by the same device in the clinical setting. Although a higher artefact percentage was found for the textile belt, signal-to-noise ratio of electrocardiogram signals recorded by the textile belt in the home setting was similar to that of signals acquired by the gel electrodes in the clinical setting. A good agreement was found between the RR-intervals derived from signals recorded in home and clinical settings. Besides, for artefact percentages greater than 3%, visual assessment of Poincaré plots proved to be effective for the determination of the primary source of artefacts (noise or ectopic beats). Acceleration data allowed posture recognition (i.e. lying or standing/sitting, lying position) with an accuracy of 91% and positive predictive value of 80%. Lastly, preliminary results of physical activity intensity classification yielded high accuracy, showing the potential of the proposed method. The textile belt proved to be appropriate for long-term, remote and continuous monitoring of subjects’ physical and physiological parameters. It can monitor not only electrocardiogram, but also breathing rate, body posture and physical activity intensity, having the potential to be used as tool for disease prediction and diagnose support.Contexto: A falta de dispositivos adequados para a monitorização de sinais fisiológicos de um modo remoto e contínuo, juntamente com avanços tecnológicos na área de aquisição de dados nas últimas décadas, levaram ao surgimento de wearable devices, i.e. dispositivos vestíveis, no sector da saúde. Wearable devices possibilitam a monitorização do estado de saúde, de uma forma remota, contínua e de longa duração. Quando feito em ambiente domiciliar, este tipo de monitorização (i.e. contínua, remota e de longa duração) tem várias vantagens: diminui a pressão posta sobre o sistema de saúde, reduz despesas associadas ao internamento e acelera a resposta a emergências, permitindo deteção precoce e prevenção de condições crónicas. Neste contexto, a Empa, Laboratórios Federais Suíços de Ciência e Tecnologia de Materiais, desenvolveu um sistema vestível para a monitorização de eletrocardiograma de longa duração. Este sistema consiste num cinto peitoral com dois elétrodos têxteis integrados. Os elétrodos têxteis são feitos de fio de polietileno tereftalato revestido com prata e uma ultrafina camada de titânio no topo. De modo a garantir a aquisição de sinais de alta qualidade, o cinto tem nele integrado um reservatório de água que liberta vapor de água para humidificar os elétrodos. Este reservatório per-mite a monitorização contínua de eletrocardiograma por 5 a 10 dias, sem necessitar de recarga. A vali-dade do cinto para a monitorização de eletrocardiograma em ambiente clínico já foi provada. Objetivo: Este trabalho teve por objetivo avaliar a validade do cinto para a monitorização de eletrocar-diograma em ambiente domiciliar e complementar o sistema existente com sensores para monitorização respiratória. Um outro objetivo foi analisar a adequação do cinto, como um sistema multisensor, para monitorização da atividade física. Métodos: Um estudo com 12 pacientes com suspeita de apneia do sono (10 homens e 2 mulheres, am-plitude interquartil de 48–59 anos para a idade e de 28.0–35.5 kg.m-2 para o índice de massa corporal) foi conduzido para avaliar a qualidade do sinal de eletrocardiograma medido em ambiente domiciliar. O sinal de eletrocardiograma dos pacientes foi monitorizado continuamente, num total de 28 noites. A qualidade dos sinais adquiridos foi analisada através do cálculo da razão sinal-ruído; da deteção de ar-tefactos, i.e., intervalos RR com um valor inviável de um ponto de vista fisiológico; e de gráficos de Poincaré, um método de análise não linear da distribuição dos intervalos RR registados. Os dados ad-quiridos neste estudo foram comparados com dados dos mesmos pacientes, adquiridos em ambiente hospitalar. Para a monitorização respiratória, sensores feitos de fibra óptica foram integrados no cinto. Al-gorítmicos para a estimar a frequência respiratória e o volume corrente dos sujeitos tendo por base o sinal medido pelas fibras ópticas foram desenvolvidos neste trabalho. As diferentes abordagens foram comparadas através de estudos piloto. Diferentes métodos para avaliação da qualidade do sinal adquirido foram sugeridos. Um método de reconhecimento da postura corporal através do cálculo de ângulos de orientação com base na aceleração medida foi proposto. A prova de conceito da determinação da intensidade da atividade física pela combinação de informações relativas á aceleração inercial e frequências cardíaca e respiratória dos sujeitos, é também apresentada neste trabalho. Um estudo foi conduzido para avaliar a validade do cinto para monitorização da respiração e da atividade física. O estudo contou com 10 parti-cipantes, dos quais 3 vestiram o cinto para monitorização da respiração (1 homem e 2 mulheres, idade 21 ± 2 anos, índice de massa corporal 20.3 ± 1.5 kg.m-2). Resultados: O estudo feito com pacientes com suspeita de apneia do sono revelou que os sinais eletro-cardiográficos adquiridos pelo cinto em ambiente domiciliar foram de melhor qualidade que os sinais adquiridos pelo mesmo dispositivo em ambiente hospitalar. Uma percentagem de artefacto de 2.87% ±4.14% foi observada para os dados adquiridos pelos elétrodos comummente usados em ambiente hospi-talar, 7.49% ± 10.76% para os dados adquiridos pelo cinto em ambiente domiciliar e 9.66% ± 14.65% para os dados adquiridos pelo cinto em ambiente hospitalar. Embora tenham tido uma maior percenta-gem de artefacto, a razão sinal-ruído dos sinais eletrocardiográficos adquiridos pelo cinto em ambiente domiciliar foi semelhante á dos sinais adquiridos pelos elétrodos de gel em ambiente hospitalar. Resul-tados sugerem uma boa concordância entre os intervalos RR calculados com base nos eletrocardiogra-mas registados em ambientes hospitalar e domiciliar. Além disso, para sinais com percentagem de arte-facto superior a 3%, a avaliação visual dos gráficos de Poincaré provou ser um bom método para a determinação da fonte primária de artefactos (batimentos irregulares ou ruído). A monitorização da aceleração dos sujeitos permitiu o reconhecimento da postura corporal (isto é, deitado ou sentado/em pé) com uma exatidão de 91% e valor preditivo positivo de 80%. Por fim, a classificação da intensidade da atividade física baseado na aceleração inercial e frequências cardíaca e respiratória revelou elevada exatidão, mostrando o potencial desta técnica. Conclusão: O cinto desenvolvido pela Empa provou ser apropriado para monitorização de longa-dura-ção de variáveis físicas e fisiológicos, de uma forma remota e contínua. O cinto permite não só monito-rizar eletrocardiograma, mas também frequência respiratória, postura corporal e intensidade da atividade física. Outros estudos devem ser conduzidos para corroborar os resultados e conclusões deste trabalho. Outros sensores poderão ser integrados no cinto de modo a possibilitar a monitorização de outras vari-áveis fisiológicas de relevância clínica. Este sistema tem o potencial de ser usado como uma ferramenta para predição de doenças e apoio ao diagnóstico

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking