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Article
Computer Science, Information Systems
Yekta Said Can et al.
Summary: IoT devices generate massive biomedical data which can be used to improve health systems with advanced machine learning algorithms. Affective computing is an emerging field for monitoring mental states using physiological signals. Privacy concerns need to be addressed before analyzing physiological signals.
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2021)
Article
Sport Sciences
Jan M. Muhlen et al.
Summary: This paper presents best-practice validation protocol for consumer wearables assessing heart rate by photoplethysmography (PPG), aiming to standardize the validity testing and reporting of PPG-based heart rate wearables used by consumers.
BRITISH JOURNAL OF SPORTS MEDICINE
(2021)
Article
Health Care Sciences & Services
Yingying Hao et al.
Summary: The study aimed to determine the validity of 11 commercially available wrist-wearable activity devices for monitoring total steps and total 24-hour total energy expenditure in healthy adolescents under simulated free-living conditions, with the conclusion that the Bong 2s had the best accuracy for estimating TEE and total steps. Further research is needed to examine the validity of these devices in different types of physical activities under real-world conditions.
JMIR MHEALTH AND UHEALTH
(2021)
Article
Engineering, Multidisciplinary
Gloria Cosoli et al.
Summary: Wireless ElectroCardioGram (ECG) systems are widely used in various fields such as tele-monitoring, sport applications, support for aging people at home, fetal ECG, and wearable devices. Important aspects to consider when developing a wireless ECG system include electrodes, analog front-end, data acquisition systems, wireless transmission technology, and power consumption. Technological advancements have led to miniaturized and comfortable devices, but further improvements can still be made in multiple aspects.
Review
Chemistry, Analytical
Katrina Hinde et al.
Summary: Heart rate variability (HRV) measurements provide insights into the autonomic nervous system and sympathetic activity balance. High HRV indicates good adaptability, while low HRV may signal fatigue, overtraining, or health issues. The Polar H10 stands out as the most accurate wearable device currently available, although it requires additional cost for data extraction without using an app.
Article
Computer Science, Artificial Intelligence
Majid H. Alsulami et al.
Summary: The proportion of elderly people is increasing globally, leading to a higher risk of heart disease. Smart devices like watches show higher accuracy in monitoring heart rates, offering a cost-effective and efficient solution for healthcare support.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Review
Geriatrics & Gerontology
Eduardo Teixeira et al.
Summary: With the increasing popularity of wearable devices in daily life, especially in the health field, there is still controversy over the accuracy and reliability of most devices for measuring results, particularly in older adults. Research is equivocal on the effectiveness of these devices for older populations, highlighting the need for continuous updates and evaluations due to their constant evolution and redesigns.
Article
Chemistry, Analytical
Asterios Leonidis et al.
Summary: High stress levels and sleep deprivation can lead to various mental or physical health issues. The COVID-19 pandemic has brought unprecedented changes, causing significant stress and worries, highlighting the importance of solutions to help people manage stress and improve sleep quality.
Review
Chemistry, Analytical
Mostafa Haghi et al.
Summary: This study analyzes wearable devices in healthcare from an innovative perspective, categorizing them according to the environmental, behavioral, physiological, and psychological domains. The research indicates a shift towards disease management and specific applications for wearables, highlighting their significant role in proactive healthcare.
Article
Health Care Sciences & Services
Yuezhou Zhang et al.
Summary: This study aimed to extract sleep features from data collected using a wearable device and analyze their associations with depressive symptoms and sleep quality. The research identified specific sleep features, such as awake time percentage, awakening times, insomnia, mean sleep offset time, and hypersomnia, that were significantly correlated with depressive symptoms. The findings suggest the potential of using wearable devices for remote monitoring of sleep as biomarkers of depression in real-world settings.
JMIR MHEALTH AND UHEALTH
(2021)
Review
Multidisciplinary Sciences
Shinjae Kwon et al.
Summary: Despite the increasing awareness of the importance of sleep, many people with sleep disorders remain undiagnosed due to the high cost of traditional sleep assessment methods. The recent development of wearable sensors and electronics allows for portable, continuous monitoring of sleep at home, overcoming the limitations of traditional polysomnography.
Article
Engineering, Electrical & Electronic
Sara Casaccia et al.
IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE
(2021)
Article
Engineering, Electrical & Electronic
Angelica Poli et al.
Summary: In the field of automatic recognition and classification of Activities of Daily Living (ADLs), sensor technologies play a crucial role in determining the accuracy of algorithms. This study examines the influence of wearable device characteristics and measurement uncertainty on classification accuracy and finds that accuracy fluctuates based on sensor choice, affecting the processing of relevant features. The research highlights the importance of tuning the combination of domain, number, and type of features to achieve the best classification accuracy based on specific sensors, even when measuring the same type of signal under identical circumstances.
IEEE SENSORS JOURNAL
(2021)
Review
Green & Sustainable Science & Technology
Ana Culic et al.
Summary: Improving the efficiency of building energy systems is crucial for achieving high-performance building goals. The current trend of user-centric approach in personal thermal comfort modeling within smart building paradigm can bring various benefits leading towards improved building energy performance. Technological solutions for detection of thermal comfort parameters in data-driven models are mainly based on network of connected sensors, divided into camera-based technologies, wearable devices, and connected sensor systems.
JOURNAL OF CLEANER PRODUCTION
(2021)
Proceedings Paper
Engineering, Biomedical
Sara Casaccia et al.
Summary: This study focused on evaluating the accuracy of step measurement using smartwatches by having subjects wear different watches during natural, slow, and fast walking activities while using videos as a reference system. The statistical confidence for step measurement was found to be 4.2% for natural pace, 7.5% for slow pace, and 7.1% for fast pace.
2021 IEEE INTERNATIONAL SYMPOSIUM ON MEDICAL MEASUREMENTS AND APPLICATIONS (IEEE MEMEA 2021)
(2021)
Proceedings Paper
Computer Science, Theory & Methods
Gloria Cosoli et al.
Summary: This research introduces a Machine Learning classifier to evaluate the physical state of subjects by analyzing their Blood Volume Pulse signals, and introduces a Fatigue-Related Index to quantify physical well-being. Results show that the Support Vector Machine classifier achieves the best performance in detecting fatigue-induced stress, with an accuracy of 97.50%. The study aims to support the assessment of worker's well-being status and improve workload management in the context of Industry 4.0.
2021 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR INDUSTRY 4.0 & IOT (IEEE METROIND4.0 & IOT)
(2021)
Article
Engineering, Electrical & Electronic
Nicole Morresi et al.
Summary: This study shows that combining environmental parameters and physiological indicators can improve the prediction of thermal sensation vote (TSV) compared to using only HRV features, with an average accuracy of 92.2%. By utilizing HRV features as the input for Machine Learning classification algorithms, the research demonstrates that physiological quantities related to thermal comfort can enhance TSV prediction when combined with environmental quantities.
IEEE SENSORS JOURNAL
(2021)
Review
Engineering, Electrical & Electronic
Sheikh M. A. Iqbal et al.
Summary: Wearable devices have various applications in healthcare, from monitoring physiological diseases to serving as drug delivery systems. Despite facing challenges, they hold promise in becoming personalized healthcare systems.
NPJ FLEXIBLE ELECTRONICS
(2021)
Article
Medical Informatics
Gloria Cosoli et al.
Summary: The use of wearable devices, combined with big data and artificial intelligence, has shown potential in quickly identifying patterns between healthy and infected individuals for early detection and risk reduction. However, further research is needed in terms of measurement accuracy, data management, and results exploitation.
HEALTH AND TECHNOLOGY
(2021)
Article
Respiratory System
Gordon B. Drummond et al.
Summary: This study used machine learning and neural networks to select and classify respiratory signals from acutely ill patients, concluding that accelerometers provide reliable measurements of respiratory rate. Even with only 10% of observation time, it is still more reliable than traditional nurse charting.
Article
Computer Science, Information Systems
Jiayi Zhao et al.
COMPUTER COMMUNICATIONS
(2020)
Article
Health Care Sciences & Services
Peter Dueking et al.
JMIR MHEALTH AND UHEALTH
(2020)
Article
Engineering, Electrical & Electronic
Sara Casaccia et al.
IEEE SENSORS JOURNAL
(2020)
Review
Engineering, Multidisciplinary
Gloria Cosoli et al.
Article
Chemistry, Analytical
Francesco Scardulla et al.
Article
Clinical Neurology
Giulia Regalia et al.
Review
Chemistry, Analytical
Jermana L. Moraes et al.
Article
Chemistry, Analytical
Oscar Belmonte-Fernandez et al.
Review
Computer Science, Information Systems
Toshiyo Tamura et al.
