Machine learning for healthcare wearable devices: the big picture

F Sabry, T Eltaras, W Labda, K Alzoubi… - Journal of Healthcare …, 2022 - Wiley Online Library
Using artificial intelligence and machine learning techniques in healthcare applications has
been actively researched over the last few years. It holds promising opportunities as it is …

[HTML][HTML] A systematic review of sensing technologies for wearable sleep staging

SA Imtiaz - Sensors, 2021 - mdpi.com
Designing wearable systems for sleep detection and staging is extremely challenging due to
the numerous constraints associated with sensing, usability, accuracy, and regulatory …

Wearable photoplethysmography for cardiovascular monitoring

PH Charlton, PA Kyriacou, J Mant… - Proceedings of the …, 2022 - ieeexplore.ieee.org
Smart wearables provide an opportunity to monitor health in daily life and are emerging as
potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands …

[HTML][HTML] A deep transfer learning approach for wearable sleep stage classification with photoplethysmography

M Radha, P Fonseca, A Moreau, M Ross, A Cerny… - NPJ digital …, 2021 - nature.com
Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography
(PPG) could open the way for better sleep disorder screening and health monitoring …

Application of photoplethysmography signals for healthcare systems: An in-depth review

HW Loh, S Xu, O Faust, CP Ooi, PD Barua… - Computer Methods and …, 2022 - Elsevier
Background and objectives Photoplethysmography (PPG) is a device that measures the
amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn …

[HTML][HTML] The promise of sleep: A multi-sensor approach for accurate sleep stage detection using the oura ring

M Altini, H Kinnunen - Sensors, 2021 - mdpi.com
Sensors | Free Full-Text | The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep
Stage Detection Using the Oura Ring Next Article in Journal The 2019–2020 Rise in Lake …

Detecting sleep using heart rate and motion data from multisensor consumer-grade wearables, relative to wrist actigraphy and polysomnography

DM Roberts, MM Schade, GM Mathew, D Gartenberg… - Sleep, 2020 - academic.oup.com
Abstract Study Objectives Multisensor wearable consumer devices allowing the collection of
multiple data sources, such as heart rate and motion, for the evaluation of sleep in the home …

[HTML][HTML] Computational diagnostic techniques for electrocardiogram signal analysis

L Xie, Z Li, Y Zhou, Y He, J Zhu - Sensors, 2020 - mdpi.com
Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina,
myocardial infarction, and ischemic heart failure, are the leading cause of death globally …

[HTML][HTML] Past, present, and future of multisensory wearable technology to monitor sleep and circadian rhythms

MR Lujan, I Perez-Pozuelo, MA Grandner - Frontiers in digital health, 2021 - frontiersin.org
Movement-based sleep-wake detection devices (ie, actigraphy devices) were first
developed in the early 1970s and have repeatedly been validated against …

[HTML][HTML] Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank

M Wainberg, SE Jones, LM Beaupre, SL Hill… - PLoS …, 2021 - journals.plos.org
Background Sleep problems are both symptoms of and modifiable risk factors for many
psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at …