With the development of wearable technologies, a new kind of healthcare data has become valuable as medical information. These data provide meaningful information regarding an individual's physiological and psychological states, such as activity level, mood, stress, and cognitive health. These biomarkers are named digital since they are collected from digital devices integrated with various sensors. In this study, we explore digital biomarkers related to stress modality by examining data collected from mobile phones and smartwatches. We utilize machine learning techniques on the Tesserae dataset, precisely Random Forest, to extract stress biomarkers. Using feature selection techniques, we utilize weather, activity, heart rate (HR), stress, sleep, and location (work-home) measurements from wearables to determine the most important stress-related biomarkers. We believe we contribute to interpreting stress biomarkers with a high range of features from different devices. In addition, we classify the $5$ different stress levels with the most important features, and our results show that we can achieve $85\%$ overall class accuracy by adjusting class imbalance and adding extra features related to personality characteristics. We perform similar and even better results in recognizing stress states with digital biomarkers in a daily-life scenario targeting a higher number of classes compared to the related studies.
University of North Carolina（北卡罗来纳大学）；George Washington University（乔治·华盛顿大学）
Breathing rate (BR), minute ventilation (VE), and other respiratory parameters are essential for real-time patient monitoring in many acute health conditions, such as asthma. The clinical standard for measuring respiration, namely Spirometry, is hardly suitable for continuous use. Wearables can track many physiological signals, like ECG and motion, yet not respiration. Deriving respiration from other modalities has become an area of active research. In this work, we infer respiratory parameters from wearable ECG and wrist motion signals. We propose a modular and generalizable classification-re…
Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence
Department of Mathematics, Universidad de Las Palmas de Gran Canaria, 35001, Spain（拉斯帕尔马斯德Gran Canaria大学数学系）；Cancer Research Program, IMDEA Food Institute（IMDEA食品研究所癌症研究计划）；Health, IMDEA Food Institute（IMDEA食品研究所健康）
Swiss Federal Institute of Technology Lausanne (EPFL)（洛桑联邦理工学院）
A person's passage of time perception (POTP) is strongly linked to their mental state and stress response, and can therefore provide an easily quantifiable means of continuous mental health monitoring. In this work, we develop a custom experiment and Machine Learning (ML) models for predicting POTP from biomarkers acquired from wearable biosensors. We first confirm that individuals experience time passing slower than usual during fear or sadness (p = 0.046) and faster than usual during cognitive tasks (p = 2 x 10^-5). Then, we group together the experimental segments associated with fast, slow…
Swiss Federal Institute of Technology Lausanne（洛桑联邦理工学院）
Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms. However, a well-known and important challenge of working on physiological signals recorded by conventional monitoring devices is missing data due to sensors insufficient contact and interference by other equipment. This challenge becomes more problematic when the user/patient is mentally or physically active or stressed because of more frequent conscious or subconscious movements. In this paper, we propose ReLearn, a robust machine learning framework for stress detec…
The advent of IoT has enabled the design of connected and integrated smart health monitoring systems. These smart health monitoring systems could be realized in a smart home context to render long-term care to the elderly population. In this paper, we present the design of a wearable health monitoring system suitable for older adults in a smart home context. The proposed system offers solutions to monitor the stress, blood pressure, and location of an individual within a smart home environment. The stress detection model proposed in this work uses Electrodermal Activity (EDA), Photoplethysmogr…
University of Bern（伯尔尼大学）；University of Zurich（苏黎世大学）；Inselspital, University Hospital of Bern（伯尔尼大学医院）
With growing usage of machine learning algorithms and big data in health applications, digital biomarkers have become an important key feature to ensure the success of those applications. In this paper, we focus on one important use-case, the long-term continuous monitoring of the cognitive ability of older adults. The cognitive ability is a factor both for long-term monitoring of people living alone as well as an outcome in clinical studies. In this work, we propose a new digital biomarker for cognitive abilities based on location eigenbehaviour obtained from contactless ambient sensors. Indo…
Electrocardiography (ECG), an electrical measurement which captures cardiac activities, is the gold standard for diagnosing cardiovascular disease (CVD). However, ECG is infeasible for continuous cardiac monitoring due to its requirement for user participation. By contrast, photoplethysmography (PPG) provides easy-to-collect data, but its limited accuracy constrains its clinical usage. To combine the advantages of both signals, recent studies incorporate various deep learning techniques for the reconstruction of PPG signals to ECG; however, the lack of contextual information as well as the lim…
Student Mental Health Screening via Fitbit Data Collected During the COVID-19 Pandemic
Department of Data Science（数据科学系）；Worcester Polytechnic Institute（沃斯特理工学院）；Department of Information Systems and Analytics（信息系统与分析系）；Bryant University（布莱恩特大学）；Department of Civil, Environmental, and Architectural Engineering（土木、环境与建筑工程系）；Department of Computer Science（计算机科学系）
Multimodal Fusion with LLMs for Engagement Prediction in Natural Conversation
Computer Science Department, Carnegie Mellon University（卡内基梅隆大学计算机科学系）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Neuroscience Institute, Carnegie Mellon University（卡内基梅隆大学神经科学研究所）；Department of Psychology, The University of Texas at Austin（德克萨斯大学奥斯汀分校心理学系）；Center for Perceptual Systems, The University of Texas at Austin（德克萨斯大学奥斯汀分校感知系统中心）
Parkinson's disease (PD) is a slowly progressive debilitating neurodegenerative disease which is prominently characterised by motor symptoms. Indoor localisation, including number and speed of room to room transitions, provides a proxy outcome which represents mobility and could be used as a digital biomarker to quantify how mobility changes as this disease progresses. We use data collected from 10 people with Parkinson's, and 10 controls, each of whom lived for five days in a smart home with various sensors. In order to more effectively localise them indoors, we propose a transformer-based ap…
Objective Features Extracted from Motor Activity Time Series for Food Addiction Analysis Using Machine Learning -- A Pilot Study
Institute of Physiology of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences（俄罗斯科学院乌拉尔分院科米科学中心生理学研究所）；Petrozavodsk State University（彼得罗扎沃茨克国立大学）；Princess Nourah bint Abdulrahman University（努拉·宾特·阿卜杜勒拉赫曼公主大学）；Kuwait College of Science and Technology（科威特科学与技术学院）；Vels Institute of Sciences, Technology, and Advanced Studies（韦尔斯科学、技术与高等研究院）；Pitirim Sorokin Syktyvkar State University（皮季里姆·索罗金瑟克特夫卡尔国立大学）；Municipal Institution of Additional Education "Center for Psychological, Pedagogical, Medical and Social Assistance"（市立补充教育机构“心理、教育、医疗和社会援助中心”）；Research Institute of Biomedicine and Biomedical Technologies, Medical University（医科大学医学生物学与生物医学技术研究所）；Medical University（医科大学）；Tyumen Cardiology Research Center, Tomsk National Research Medical Center, Russian Academy of Sciences（俄罗斯科学院托木斯克国家医学研究中心秋明心脏病学研究中心）
