Using smartwatch sensors to support the acquisition of sleep quality data for supervised machine learning

It is a common practice in supervised learning techniques to use human judgment to label training data. For this process, data reliability is fundamental. Research on sleep quality found that human sleep stage misperception may occur. In this paper we propose that human judgment be supported by software-driven evaluation based on physiological parameters, selecting as training data only data sets for which human judgment and software evaluation are aligned. A prototype system to provide a broad-spectrum perception of sleep quality data comparable with human judgment is presented. The system requires users to wear a smartwatch recording heartbeat rate and wrist acceleration. It estimates an overall percentage of the sleep stages, to achieve an effective approximation of conventional sleep measures, and to provide a three-class sleep quality evaluation. The training data are composed of the heartbeat rate, the wrist acceleration and the three-class sleep quality. As a proof of concept, we experimented the approach on three subjects, each one over 20 nights

Monitoring student activities with smartwatches: On the academic performance enhancement

Motivated by the importance of studying the relationship between habits of students and their academic performance, daily activities of undergraduate participants have been tracked with smartwatches and smartphones. Smartwatches collect data together with an Android application that interacts with the users who provide the labeling of their own activities. The tracked activities include eating, running, sleeping, classroom-session, exam, job, homework, transportation, watching TV-Series, and reading. The collected data were stored in a server for activity recognition with supervised machine learning algorithms. The methodology for the concept proof includes the extraction of features with the discrete wavelet transform from gyroscope and accelerometer signals to improve the classification accuracy. The results of activity recognition with Random Forest were satisfactory (86.9%) and support the relationship between smartwatch sensor signals and daily-living activities of students which opens the possibility for developing future experiments with automatic activity-labeling, and so forth to facilitate activity pattern recognition to propose a recommendationsystem to enhance the academic performance of each student

Machine Learning Models for Mental Stress Classification based on Multimodal Biosignal Input

Mental stress is a largely prevalent condition directly or indirectly responsible for almost half of all work-related diseases. Work-Related Stress is the second most impactful occupational health problem in Europe, behind musculoskeletal diseases. When mental health is adequately handled, a worker’s well-being, performance, and productivity can be considerably improved. This thesis presents machine learning models to classify mental stress experienced by computer users using physiological signals including heart rate, acquired using a smart- watch; respiration, derived from a smartphone’s acc placed on the chest; and trapezius electromyography, using proprietary electromyography sensors. Two interactive proto- cols were implemented to collect data from 12 individuals. Time and frequency domain features were extracted from the heart rate and electromyography signals, and statistical and temporal features were extracted from the derived respiration signal. Three algorithms: Support Vector Machine, Random Forest, and K-Nearest-Neighbor were employed for mental stress classification. Different input modalities were tested for the machine learning models: one for each physiological signal and a multimodal one, combining all of them. Random Forest obtained the best mean accuracy (98.5%) for the respiration model whereas K-Nearest-Neighbor attained higher mean accuracies for the heart rate (89.0%) left, right and total electromyography (98.9%, 99.2%, and 99.3%) models. KNN algorithm was also able to achieve 100% mean accuracy for the multimodal model. A possible future approach would be to validate these models in real-time.O stress mental é uma condição amplamente prevalente direta ou indiretamente responsável por quase metade de todas doenças relacionadas com trabalho. O stress expe- rienciado no trabalho é o segundo problema de saúde ocupacional com maior impacto na Europa, depois das doenças músculo-esqueléticas. Quando a saúde mental é adequada- mente cuidada, o bem-estar, o desempenho e a produtividade de um trabalhador podem ser consideravelmente melhorados. Esta tese apresenta modelos de aprendizagem automática que classificam o stress mental experienciado por utilizadores de computadores recorrendo a sinais fisiológi- cos, incluindo a frequência cardíaca, adquirida pelo sensor de fotopletismografia de um smartwatch; a respiração, derivada de um acelerómetro incorporado no smartphone po- sicionado no peito; e electromiografia de cada um dos músculos trapézios, utilizando sensores electromiográficos proprietários. Foram implementados dois protocolos inte- ractivos para recolha de dados de 12 indivíduos. Características do domínio temporal e de frequência foram extraídas dos sinais de frequência cardíaca e electromiografia, e características estatísticas e temporais foram extraídas do sinal respiratório. Três algoritmos entitulados K-Nearest-Neighbor, Random Forest, e Support Vector Machine foram utilizados para a classificação do stress mental. Foram testadas diferentes modalidades de dados para os modelos de aprendizagem automática: uma para cada sinal fisiológico e uma multimodal, combinando os três. O Random Forest obteve a melhor precisão média (98,5%) para o modelo de respiração enquanto que o K-Nearest-Neighbor atingiu uma maior precisão média nos modelos de frequência cardíaca (89,0%) e electro- miografia esquerda, direita e total (98,9%, 99,2%, e 99,3%). O algoritmo KNN conseguiu ainda atingir uma precisão média de 100% para o modelo multimodal. Uma possível abordagem futura seria efetuar uma validação destes modelos em tempo real

Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey

Ubiquitous in-home health monitoring systems have become popular in recent years due to the rise of digital health technologies and the growing demand for remote health monitoring. These systems enable individuals to increase their independence by allowing them to monitor their health from the home and by allowing more control over their well-being. In this study, we perform a comprehensive survey on this topic by reviewing a large number of literature in the area. We investigate these systems from various aspects, namely sensing technologies, communication technologies, intelligent and computing systems, and application areas. Specifically, we provide an overview of in-home health monitoring systems and identify their main components. We then present each component and discuss its role within in-home health monitoring systems. In addition, we provide an overview of the practical use of ubiquitous technologies in the home for health monitoring. Finally, we identify the main challenges and limitations based on the existing literature and provide eight recommendations for potential future research directions toward the development of in-home health monitoring systems. We conclude that despite extensive research on various components needed for the development of effective in-home health monitoring systems, the development of effective in-home health monitoring systems still requires further investigation.Comment: 35 pages, 5 figure

Photoplethysmography based atrial fibrillation detection: an updated review from July 2019

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field. This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 59 pertinent studies. Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis

INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life

Sviluppo di un metodo innovativo per la misura del comfort termico attraverso il monitoraggio di parametri fisiologici e ambientali in ambienti indoor

openLa misura del comfort termico in ambienti indoor è un argomento di interesse per la comunità scientifica, poiché il comfort termico incide profondamente sul benessere degli utenti ed inoltre, per garantire condizioni di comfort ottimali, gli edifici devono affrontare costi energetici elevati. Anche se esistono norme nel campo dell'ergonomia del comfort che forniscono linee guida per la valutazione del comfort termico, può succedere che in contesti reali sia molto difficile ottenere una misurazione accurata. Pertanto, per migliorare la misura del comfort termico negli edifici, la ricerca si sta concentrando sulla valutazione dei parametri personali e fisiologici legati al comfort termico, per creare ambienti su misura per l’utente. Questa tesi presenta diversi contributi riguardo questo argomento. Infatti, in questo lavoro di ricerca, sono stati implementati una serie di studi per sviluppare e testare procedure di misurazione in grado di valutare quantitativamente il comfort termico umano, tramite parametri ambientali e fisiologici, per catturare le peculiarità che esistono tra i diversi utenti. In primo luogo, è stato condotto uno studio in una camera climatica controllata, con un set di sensori invasivi utilizzati per la misurazione dei parametri fisiologici. L'esito di questa ricerca è stato utile per ottenere una prima accuratezza nella misurazione del comfort termico dell'82%, ottenuta mediante algoritmi di machine learning (ML) che forniscono la sensazione termica (TSV) utilizzando la variabilità della frequenza cardiaca (HRV) , parametro che la letteratura ha spesso riportato legato sia al comfort termico dell'utenza che alle grandezze ambientali. Questa ricerca ha dato origine a uno studio successivo in cui la valutazione del comfort termico è stata effettuata utilizzando uno smartwatch minimamente invasivo per la raccolta dell’HRV. Questo secondo studio consisteva nel variare le condizioni ambientali di una stanza semi-controllata, mentre i partecipanti potevano svolgere attività di ufficio ma in modo limitato, ovvero evitando il più possibile i movimenti della mano su cui era indossato lo smartwatch. Con questa configurazione, è stato possibile stabilire che l'uso di algoritmi di intelligenza artificiale (AI) e il set di dati eterogeneo creato aggregando parametri ambientali e fisiologici, può fornire una misura di TSV con un errore medio assoluto (MAE) di 1.2 e un errore percentuale medio assoluto (MAPE) del 20%. Inoltre, tramite il Metodo Monte Carlo (MCM) è stato possibile calcolare l'impatto delle grandezze in ingresso sul calcolo del TSV. L'incertezza più alta è stata raggiunta a causa dell'incertezza nella misura della temperatura dell'aria (U = 14%) e dell'umidità relativa (U = 10,5%). L'ultimo contributo rilevante ottenuto con questa ricerca riguarda la misura del comfort termico in ambiente reale, semi controllato, in cui il partecipante non è stato costretto a limitare i propri movimenti. La temperatura della pelle è stata inclusa nel set-up sperimentale, per migliorare la misurazione del TSV. I risultati hanno mostrato che l'inclusione della temperatura della pelle per la creazione di modelli personalizzati, realizzati utilizzando i dati provenienti dal singolo partecipante, porta a risultati soddisfacenti (MAE = 0,001±0,0003 e MAPE = 0,02%±0,09%). L'approccio più generalizzato, invece, che consiste nell'addestrare gli algoritmi sull'intero gruppo di partecipanti tranne uno, e utilizzare quello tralasciato per il test, fornisce prestazioni leggermente inferiori (MAE = 1±0.2 e MAPE = 25% ±6%). Questo risultato evidenzia come in condizioni semi-controllate, la previsione di TSV utilizzando la temperatura della pelle e l'HRV possa essere eseguita con un certo grado di incertezza.Measuring human thermal comfort in indoor environments is a topic of interest in the scientific community, since thermal comfort deeply affects the well-being of occupants and furthermore, to guarantee optimal comfort conditions, buildings must face high energy costs. Even if there are standards in the field of the ergonomics of the thermal environment that provide guidelines for thermal comfort assessment, it can happen that in real-world settings it is very difficult to obtain an accurate measurement. Therefore, to improve the measurement of thermal comfort of occupants in buildings, research is focusing on the assessment of personal and physiological parameters related to thermal comfort, to create environments carefully tailored to the occupant that lives in it. This thesis presents several contributions to this topic. In fact, in the following research work, a set of studies were implemented to develop and test measurement procedures capable of quantitatively assessing human thermal comfort, by means of environmental and physiological parameters, to capture peculiarities among different occupants. Firstly, it was conducted a study in a controlled climatic chamber with an invasive set of sensors used for measuring physiological parameters. The outcome of this research was helpful to achieve a first accuracy in the measurement of thermal comfort of 82%, obtained by training machine learning (ML) algorithms that provide the thermal sensation vote (TSV) by means of environmental quantities and heart rate variability (HRV), a parameter that literature has often reported being related to both users' thermal comfort. This research gives rise to a subsequent study in which thermal comfort assessment was made by using a minimally invasive smartwatch for collecting HRV. This second study consisted in varying the environmental conditions of a semi-controlled test-room, while participants could carry out light-office activities but in a limited way, i.e. avoiding the movements of the hand on which the smartwatch was worn as much as possible. With this experimental setup, it was possible to establish that the use of artificial intelligence (AI) algorithms (such as random forest or convolutional neural networks) and the heterogeneous dataset created by aggregating environmental and physiological parameters, can provide a measure of TSV with a mean absolute error (MAE) of 1.2 and a mean absolute percentage error (MAPE) of 20%. In addition, by using of Monte Carlo Method (MCM), it was possible to compute the impact of the uncertainty of the input quantities on the computation of the TSV. The highest uncertainty was reached due to the air temperature uncertainty (U = 14%) and relative humidity (U = 10.5%). The last relevant contribution obtained with this research work concerns the measurement of thermal comfort in a real-life setting, semi-controlled environment, in which the participant was not forced to limit its movements. Skin temperature was included in the experimental set-up, to improve the measurement of TSV. The results showed that the inclusion of skin temperature for the creation of personalized models, made by using data coming from the single participant brings satisfactory results (MAE = 0.001±0.0003 and MAPE = 0.02%±0.09%). On the other hand, the more generalized approach, which consists in training the algorithms on the whole bunch of participants except one, and using the one left out for the test, provides slightly lower performances (MAE = 1±0.2 and MAPE = 25%±6%). This result highlights how in semi-controlled conditions, the prediction of TSV using skin temperature and HRV can be performed with acceptable accuracy.INGEGNERIA INDUSTRIALEembargoed_20220321Morresi, Nicol

Machine learning algorithms development for sleep cycles detection and general physical activity based on biosignals

In this work, machine learning algorithms for automatic sleep cycles detection were developed. The features were selected based on the AASM manual, which is considered the gold standard for human technicians. These include features such as saturation of peripheral oxygen or others related to heart rate variation. As normally, the sleep phases naturally differ in frequency, to balance the classes within the dataset, we either oversampled the least common sleep stages or undersampled the most common, allowing for a less skewed performance favouring the most represented stages, while simultaneously improving worst-stage classification. For training the models we used MESA, a database containing 2056 full overnight unattended polysomnographies from a group of 2237 participants. With the goal of developing an algorithm that would only require a PPG device to be able to accurately predict sleep stages and quality, the main channels used from this dataset were SpO2 and PPG. Employing several popular Python libraries used for the development of machine learning and deep learning algorithms, we exhaustively explored the optimisation of the manifold parameters and hyperparameters conditioning both the training and architecture of these models in order for them to better fit our purposes. As a result of these strategies, we were able to develop a neural network model (Multilayer perceptron) with 80.50% accuracy, 0.7586 Cohen’s kappa, and 77.38% F1- score, for five sleep stages. The performance of our algorithm does not seem to be correlated with sleep quality or the number of transitional epochs in each recording, suggesting uniform performance regardless of the presence of sleep disorders. To test its performance in a different real-world scenario we compared the classifications attributed by a popular sleep stage classification android app, which collected information using a smartwatch, and our algorithm, using signals obtained from a device developed by PLUX. These algorithms displayed a strong level of agreement (90.96% agreement, 0.8663 Cohen’s kappa).Neste trabalho, foram desenvolvidos algoritmos de aprendizagem de máquinas para a detecção automática de ciclos de sono. Os sinais específicos captados durante a extração de características foram selecionados com base no manual AASM, que é considerado o padrão-ouro para técnicos. Estas incluem características como a saturação do oxigénio periférico ou outras relacionadas com a variação do ritmo cardíaco. A fim de equilibrar a frequência das classes dentro do conjunto de dados, ora se fez a sobreamostragem das fases menos comuns do sono, ora se fez a subamostragem das mais comuns, permitindo um desempenho menos enviesado em favor das fases mais representadas e, simultaneamente, melhorando a classificação das fases com pior desempenho. Para o treino dos modelos criados, utilizámos MESA, uma base de dados contendo 2056 polissonografias completas, feitas durante a noite e sem vigilância, de um grupo de 2237 participantes. Do conjunto de dados escolhido, os principais canais utilizados foram SpO2 e PPG, com o objetivo de desenvolver um algoritmo que apenas exigiria um dispositivo PPG para poder prever com precisão as fases e a qualidade do sono. Utilizando várias bibliotecas populares de Python para o desenvolvimento de algoritmos de aprendizagem de máquinas e de aprendizagem profunda, explorámos exaustivamente a optimização dos múltiplos parâmetros e hiperparâmetros que tanto condicionam a formação como a arquitetura destes modelos, de modo a que se ajustem melhor aos nossos propósitos. Como resultado disto, fomos capazes de desenvolver um modelo de rede neural (Multilayer perceptron) com 80.50% de precisão, 0.7586 kappa de Cohen e F1-score de 77.38%, para cinco fases de sono. O desempenho do nosso algoritmo não parece estar correlacionado com a qualidade do sono ou o número de épocas de transição em cada gravação, sugerindo um desempenho uniforme independentemente da presença de distúrbios do sono. Para testar o seu desempenho num cenário de mundo real diferente, comparámos as classificações atribuídas por uma aplicação Android de classificação de fases do sono popular, através da recolha de informação por um smartwatch, e o nosso algoritmo, utilizando sinais obtidos a partir de um dispositivo desenvolvido pela PLUX. Estes algoritmos demonstraram um forte nível de concordância (90.96% de concordância, 0.8663 kappa de Cohen)