Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data

One of the main benefits of a wrist-worn computer is its ability to collect a variety of physiological data in a minimally intrusive manner. Among these data, electrodermal activity (EDA) is readily collected and provides a window into a person's emotional and sympathetic responses. EDA data collected using a wearable wristband are easily influenced by motion artifacts (MAs) that may significantly distort the data and degrade the quality of analyses performed on the data if not identified and removed. Prior work has demonstrated that MAs can be successfully detected using supervised machine learning algorithms on a small data set collected in a lab setting. In this paper, we demonstrate that unsupervised learning algorithms perform competitively with supervised algorithms for detecting MAs on EDA data collected in both a lab-based setting and a real-world setting comprising about 23 hours of data. We also find, somewhat surprisingly, that incorporating accelerometer data as well as EDA improves detection accuracy only slightly for supervised algorithms and significantly degrades the accuracy of unsupervised algorithms.Comment: To appear at International Symposium on Wearable Computers (ISWC) 201

Efectos de la pérdida de datos en las métricas de Variabilidad del Ritmo Cardíaco

La explosión en el mercado de dispositivos wearables ha supuesto una revolución en el ámbito de la monitorización de la salud. Gran parte de la población, incluida la población no paciente, posee dispositivos de pulsera capaces de detectar sus latidos a lo largo de todo el día. Juntocon las ventajas que esto supone, aparecen nuevos retos. Uno de ellos es la estabilidad de la calidad de la señal. Los movimientos constantes de estos dispositivos hacen que se produzcan grandes pérdidas de datos, que pueden ocasionar un deterioro de las mediciones. Esto es especialmente relevante en los dispositivos que analizan la variabilidad de ritmo cardíaco, una técnica que permite inferir información del sistema nervioso autónomo de forma no invasiva a partir del control que éste ejerce sobre el sistema circulatorio. Esta técnica necesita que todos los pulsos sean detectados para funcionar correctamente, por lo que la pérdida de datos supone inevitablemente un deterioro. Este trabajo se centra en investigar cómo se produce esta degradación para diferentes métodos y qué técnicas se pueden utilizar para reducirla. Para ello, se ha desarrollado un método de simulación de pérdida de pulsos que permite analizar los dos tipos de errores que se suelen dar: errores aleatoriamente distribuidos y en ráfagas. A su vez, se propone un nuevo método de rellenado de pulsos como una posibilidad de preprocesado, que obtiene mejores resultados que el método de referencia. Dependiendo de la aplicación y de los requerimientos de los dispositivos, se sugieren los métodos más robustos teniendo en cuenta también su coste y la información que proveen. Los métodos se han probado en una base de datos con 17 sujetos sometidos a una prueba de mesa basculante, que permite provocar cambios en la activación del sistema nervioso autónomo sin involucrar al sistema central o causar actividad en los músculos. Las métricas se han comparado tanto en la degradación de sus valores como en la capacidad para distinguir los cambios provocados por la prueba de mesa basculante.<br /

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALTH

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALT

Machine Learning for Stress Monitoring from Wearable Devices: A Systematic Literature Review

Introduction. The stress response has both subjective, psychological and objectively measurable, biological components. Both of them can be expressed differently from person to person, complicating the development of a generic stress measurement model. This is further compounded by the lack of large, labeled datasets that can be utilized to build machine learning models for accurately detecting periods and levels of stress. The aim of this review is to provide an overview of the current state of stress detection and monitoring using wearable devices, and where applicable, machine learning techniques utilized. Methods. This study reviewed published works contributing and/or using datasets designed for detecting stress and their associated machine learning methods, with a systematic review and meta-analysis of those that utilized wearable sensor data as stress biomarkers. The electronic databases of Google Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a total of 24 articles were identified and included in the final analysis. The reviewed works were synthesized into three categories of publicly available stress datasets, machine learning, and future research directions. Results. A wide variety of study-specific test and measurement protocols were noted in the literature. A number of public datasets were identified that are labeled for stress detection. In addition, we discuss that previous works show shortcomings in areas such as their labeling protocols, lack of statistical power, validity of stress biomarkers, and generalization ability. Conclusion. Generalization of existing machine learning models still require further study, and research in this area will continue to provide improvements as newer and more substantial datasets become available for study.Comment: 50 pages, 8 figure

iMind: Uma ferramenta inteligente para suporte de compreensão de conteúdo

Usually while reading, content comprehension difficulty affects individual performance. Comprehension difficulties, e. g., could lead to a slow learning process, lower work quality, and inefficient decision-making. This thesis introduces an intelligent tool called “iMind” which uses wearable devices (e.g., smartwatches) to evaluate user comprehension difficulties and engagement levels while reading digital content. Comprehension difficulty can occur when there are not enough mental resources available for mental processing. The mental resource for mental processing is the cognitive load (CL). Fluctuations of CL lead to physiological manifestation of the autonomic nervous system (ANS), which can be measured by wearables, like smartwatches. ANS manifestations are, e. g., an increase in heart rate. With low-cost eye trackers, it is possible to correlate content regions to the measurements of ANS manifestation. In this sense, iMind uses a smartwatch and an eye tracker to identify comprehension difficulty at content regions level (where the user is looking). The tool uses machine learning techniques to classify content regions as difficult or non-difficult based on biometric and non-biometric features. The tool classified regions with a 75% accuracy and 80% f-score with Linear regression (LR). With the classified regions, it will be possible, in the future, to create contextual support for the reader in real-time by, e.g., translating the sentences that induced comprehension difficulty.Normalmente durante a leitura, a dificuldade de compreensão pode afetar o desempenho da leitura. A dificuldade de compreensão pode levar a um processo de aprendizagem mais lento, menor qualidade de trabalho ou uma ineficiente tomada de decisão. Esta tese apresenta uma ferramenta inteligente chamada “iMind” que usa dispositivos vestíveis (por exemplo, smartwatches) para avaliar a dificuldade de compreensão do utilizador durante a leitura de conteúdo digital. A dificuldade de compreensão pode ocorrer quando não há recursos mentais disponíveis suficientes para o processamento mental. O recurso usado para o processamento mental é a carga cognitiva (CL). As flutuações de CL levam a manifestações fisiológicas do sistema nervoso autônomo (ANS), manifestações essas, que pode ser medido por dispositivos vestíveis, como smartwatches. As manifestações do ANS são, por exemplo, um aumento da frequência cardíaca. Com eye trackers de baixo custo, é possível correlacionar manifestação do ANS com regiões do texto, por exemplo. Neste sentido, a ferramenta iMind utiliza um smartwatch e um eye tracker para identificar dificuldades de compreensão em regiões de conteúdo (para onde o utilizador está a olhar). Adicionalmente a ferramenta usa técnicas de machine learning para classificar regiões de conteúdo como difíceis ou não difíceis com base em features biométricos e não biométricos. A ferramenta classificou regiões com uma precisão de 75% e f-score de 80% usando regressão linear (LR). Com a classificação das regiões em tempo real, será possível, no futuro, criar suporte contextual para o leitor em tempo real onde, por exemplo, as frases que induzem dificuldade de compreensão são traduzidas

Wearable Technology for Mental Wellness Monitoring and Feedback

This thesis investigates the transformative potential of wearable monitoring devices in empowering individuals to make positive lifestyle changes and enhance mental well-being. The primary objective is to assess the efficacy of these devices in addressing mental health issues, with a specific focus on stress and anxiety biomarkers. The research includes a systematic literature review that uniquely emphasizes integrating wearable technology into mental wellness, spanning diverse domains such as electronics, wearable technology, machine learning, and data analysis. This novel systematic literature review encompasses the period from 2010 to 2023, examining the profound impact of the Internet of Things (IoT) across various sectors, particularly healthcare. The thesis extensively explores wearable technologies capable of identifying a broad spectrum of human biomarkers and stress-related indicators, emphasizing their potential benefits for healthcare professionals. Challenges faced by participants and researchers in the practical implementation of wearable technology are addressed through survey analysis, providing substantial evidence for the potential of wearables in bolstering mental health within professional environments. Meticulous data analysis gathering from biosignals captured by wearables investigates the impact of stress factors and anxiety on individuals' mental well-being. The study concludes with a thorough discussion of the findings and their implications. Additionally, integrating Photoplethysmography (PPG) devices is highlighted as a significant advancement in capturing vital biomarkers associated with stress and mental well-being. Through light-based technology, PPG devices monitor blood volume changes in microvascular tissue, providing real-time information on heart rate variability (HRV). This non-invasive approach enables continuous monitoring, offering a dynamic understanding of physiological responses to stressors. The reliability of wearable devices equipped with PPG and Electroencephalography (EEG) sensors is emphasized in capturing differences in subject biomarkers. EEG devices measure brainwave patterns, providing insights into neural activity associated with stress and emotional states. The combination of PPG and EEG data enhances the precision of stress and mental well-being assessments, offering a holistic approach that captures peripheral physiological responses and central nervous system activity. In conclusion, integrating PPG devices with subjective methods and EEG sensors significantly advances stress and mental well-being assessment. This multidimensional approach improves measurement accuracy, laying the foundation for personalized interventions and innovative solutions in mental health care. The thesis also evaluates body sensors and their correlation with medically established gold references, exploring the potential of wearable devices in advancing mental health and well-being

Anxolotl - An Anxiety Companion App

Dissertação para obtenção do Grau de Mestre em Engenharia Informática e de ComputadoresA Organização Mundial de Saúde apresentou as perturbações mentais como os maiores contribuintes para incapacidade global em 2015, com os distúrbios de ansiedade a ocuparem a sexta posição. Distúrbios de ansiedade têm um alta prevalência na sociedade, e apresentam sintomas precoces que podem ser detetados. Nesta tese, produzimos um sistema capaz de detetar sintomas de distúrbios de ansiedade antes que a doença se instale por completo. Adicionalmente, queremos dar outra opção a portadores, monitorizando o seu estado mental e oferecendo a hipótese de tratarem dos seus níveis de ansiedade antes que apareçam mais sintomas. Aqui introduzimos um sistema de saúde móvel, entitulado de Anxolotl, que pode detetar e classificar níveis de ansiedade multiclasse e detetar níveis binários de estados de pânico . A nossa solução é composta por: datacenter, com o objectivo de guardar dados fisiológicos anónimos, e aplicar modelos de aprendizagem automática; broker de mensagens, que irá providenciar escalaabilidade e habilidade de desacoplamento no sistema; aplicação móvel, que funcionará em conjunto com um wearable para capturar dados fisiológicos. A nossa applicação é capaz de detetar e monitorizar diariamente, os níveis de ansiedade e pânico do utilizador, filtrando dados dúbios com base em atividade física. A aplicação também apresenta múltiplos exercícios de respiração guiados, bem como meditações acompanhadas para vários cenários de saúde mental. O nosso modelo de deteção de ansiedade foi capaz de apresentar uma precisão de 92% e um f1-Score de 90% na classificação de ansiedade multiclasse, treinando com um dataset com 124 entradas, enquanto que o nosso modelo de deteção de pânico apresenta uma precisão de 94% e um f1-Score de 94%. Estes valores foram atingindos utilizando maioritariamente dados de ritmo cardíaco. O código dos modelos está disponível em https://github.com/nunogoms/Anxolotl-engines.World Health Organization referred that common mental health disorders were the biggest contributors to global disability during the year of 2015, with anxiety disorders occupying the 6th position. Currently, anxiety disorders have high prevalence in society, and present early symptoms that are suited to be detected. With this thesis, we intend to produce a system capable of detecting the anxiety disorder early symptoms before the onset of the full range of symptoms. Additionally, we want to give another option to people already affected, in the form of monitoring their mental health, and the ability for them to react to their anxiety state quickly. Herein, we are introducing a mobile health system — Anxolotl, that can detect and classify multi class anxiety levels and detect binary panic states. Our solution is composed by: a datacenter, intended to store anonymous physiological data and applying the machine learning models; a message broker, aiming to provide scalability and decoupling to the system; and, finally a mobile app, which will work in tandem with a wearable to capture physiological data. The app is able to track and monitor, on a daily basis, its user’s anxiety and panic levels, filtering when the data is unreliable based on activity. It also presents the users with guided breathing exercises for multiple mental health scenarios as well as some guided meditations, in an effort to help its users. The Anxiety Engine model provided a 92% accuracy and 90% f1-Score in classifying multi-class anxiety levels, training and testing with a dataset containing 124 entries, and our binary Panic Engine had an accuracy of 94% and a f1-Score of 94%. Both these scenarios were mainly achieved by using heart rate data, activity context was also used in some scenarios. The code for these models is available at https://github.com/nunogoms/Anxolotl-engines.N/

Wearable sensors for learning enhancement in higher education

Wearable sensors have traditionally been used to measure and monitor vital human signs for well-being and healthcare applications. However, there is a growing interest in using and deploying these technologies to facilitate teaching and learning, particularly in a higher education environment. The aim of this paper is therefore to systematically review the range of wearable devices that have been used for enhancing the teaching and delivery of engineering curricula in higher education. Moreover, we compare the advantages and disadvantages of these devices according to the location in which they are worn on the human body. According to our survey, wearable devices for enhanced learning have mainly been worn on the head (e.g., eyeglasses), wrist (e.g., watches) and chest (e.g., electrocardiogram patch). In fact, among those locations, head-worn devices enable better student engagement with the learning materials, improved student attention as well as higher spatial and visual awareness. We identify the research questions and discuss the research inclusion and exclusion criteria to present the challenges faced by researchers in implementing learning technologies for enhanced engineering education. Furthermore, we provide recommendations on using wearable devices to improve the teaching and learning of engineering courses in higher education

Novel Approaches to Pervasive and Remote Sensing in Cardiovascular Disease Assessment

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, responsible for 45% of all deaths. Nevertheless, their mortality is decreasing in the last decade due to better prevention, diagnosis, and treatment resources. An important medical instrument for the latter processes is the Electrocardiogram (ECG). The ECG is a versatile technique used worldwide for its ease of use, low cost, and accessibility, having evolved from devices that filled up a room, to small patches or wrist- worn devices. Such evolution allowed for more pervasive and near-continuous recordings. The analysis of an ECG allows for studying the functioning of other physiological systems of the body. One such is the Autonomic Nervous System (ANS), responsible for controlling key bodily functions. The ANS can be studied by analyzing the characteristic inter-beat variations, known as Heart Rate Variability (HRV). Leveraging this relation, a pilot study was developed, where HRV was used to quantify the contribution of the ANS in modulating cardioprotection offered by an experimental medical procedure called Remote Ischemic Conditioning (RIC), offering a more objective perspective. To record an ECG, electrodes are responsible for converting the ion-propagated action potential to electrons, needed to record it. They are produced from different materials, including metal, carbon-based, or polymers. Also, they can be divided into wet (if an elec- trolyte gel is used) or dry (if no added electrolyte is used). Electrodes can be positioned either inside the body (in-the-person), attached to the skin (on-the-body), or embedded in daily life objects (off-the-person), with the latter allowing for more pervasive recordings. To this effect, a novel mobile acquisition device for recording ECG rhythm strips was developed, where polymer-based embedded electrodes are used to record ECG signals similar to a medical-grade device. One drawback of off-the-person solutions is the increased noise, mainly caused by the intermittent contact with the recording surfaces. A new signal quality metric was developed based on delayed phase mapping, a technique that maps time series to a two-dimensional space, which is then used to classify a segment into good or noisy. Two different approaches were developed, one using a popular image descriptor, the Hu image moments; and the other using a Convolutional Neural Network, both with promising results for their usage as signal quality index classifiers.As doenças cardiovasculares (DCVs) são a principal causa de morte no mundo, res- ponsáveis por 45% de todas estas. No entanto, a sua mortalidade tem vindo a diminuir na última década, devido a melhores recursos na prevenção, diagnóstico e tratamento. Um instrumento médico importante para estes recursos é o Eletrocardiograma (ECG). O ECG é uma técnica versátil utilizada em todo o mundo pela sua facilidade de uso, baixo custo e acessibilidade, tendo evoluído de dispositivos que ocupavam uma sala inteira para pequenos adesivos ou dispositivos de pulso. Tal evolução permitiu aquisições mais pervasivas e quase contínuas. A análise de um ECG permite estudar o funcionamento de outros sistemas fisiológi- cos do corpo. Um deles é o Sistema Nervoso Autônomo (SNA), responsável por controlar as principais funções corporais. O SNA pode ser estudado analisando as variações inter- batidas, conhecidas como Variabilidade da Frequência Cardíaca (VFC). Aproveitando essa relação, foi desenvolvido um estudo piloto, onde a VFC foi utilizada para quantificar a contribuição do SNA na modulação da cardioproteção oferecida por um procedimento mé- dico experimental, denominado Condicionamento Isquêmico Remoto (CIR), oferecendo uma perspectiva mais objetiva. Na aquisição de um ECG, os elétrodos são os responsáveis por converter o potencial de ação propagado por iões em eletrões, necessários para a sua recolha. Estes podem ser produzidos a partir de diferentes materiais, incluindo metal, à base de carbono ou polímeros. Além disso, os elétrodos podem ser classificados em húmidos (se for usado um gel eletrolítico) ou secos (se não for usado um eletrólito adicional). Os elétrodos podem ser posicionados dentro do corpo (dentro-da-pessoa), colocados em contacto com a pele (na-pessoa) ou embutidos em objetos da vida quotidiana (fora-da-pessoa), sendo que este último permite gravações mais pervasivas . Para este efeito, foi desenvolvido um novo dispositivo de aquisição móvel para gravar sinal de ECG, onde elétrodos embutidos à base de polímeros são usados para recolher sinais de ECG semelhantes a um dispositivo de grau médico. Uma desvantagem das soluções onde os elétrodos estão embutidos é o aumento do ruído, causado principalmente pelo contato intermitente com as superfícies de aquisição. Uma nova métrica de qualidade de sinal foi desenvolvida com base no mapeamento de fase atrasada, uma técnica que mapeia séries temporais para um espaço bidimensional, que é então usado para classificar um segmento em bom ou ruidoso. Duas abordagens diferentes foram desenvolvidas, uma usando um popular descritor de imagem, e outra utilizando uma Rede Neural Convolucional, com resultados promissores para o seu uso como classificadores de qualidade de sinal

Investigating different approaches and analyses of psychological variables to enhance sport and exercise

This thesis addresses the acquisition of knowledge through a logical step by step process during the PhD course, highlighting five research activities with a main focus on sport and exercise psychology. The ultimate goal for research looked at exploring wearable devices and associated digital technology to deliver interventions aimed to increase exercise while measuring psychological variables such as stress. A foundation was initially set with a systematic review and meta-analysis on correlations between physical activity and key variables such as self-efficacy, self-regulation, and anxiety measured using validated questionnaires. A continued interest in exploring psychometric tools and their validation in sport drove the analysis of a motivation scales and related parameters in a cohort of Italian rugby players. With the beginning of the COVID-19 pandemic, however, community-based sports activities stopped, and the way in which exercise was performed and measured rapidly changed, as I highlighted in the report “Physical activity: Benefits and challenges during the COVID-19 pandemic”. In this unexpected scenario, government agencies as well as private entities and academic institutions applied digital technology to deliver health and wellbeing messages. The use of novel tools was beneficial while facing increased sedentarism occurring during restrictions and lock-down periods. The study performed, involving office workers and electronically delivering exercise interventions in the form of active breaks, showed improvement in wellbeing and stress reduction. Finally, the last study presented can be viewed as a marker in time, as people return to normality, exercising and performing their normal routine but with a new emphasis in keeping track of their own health and wellbeing through wearable technology, following the change in measuring physical and psychological variables consolidated during the pandemic. The results met the intended goal to successfully provide a message-based, digitally delivered intervention aimed at increasing exercise and reducing stress among university students, using wearables to measure the outcome. Moreover, the comparison of wearable-associated stress (based on physiological stimuli) with self-reported stress using a validated questionnaire (e.g., Perceived Stress Scale-10) showed a promising connection. I intend to continue in this direction to further explore benefits and limitations of digital technology in sport and exercise psychology