Year: 2026
Type: preprint
Abstract: Much of human health and function unfolds beyond the clinic, through the movements of everyday life. Wrist-worn accelerometers capture these movements continuously, yet their rich signals are often reduced to a small set of predefined behavioural summary measures. Here, we present Sensori, a self-supervised foundation model that learns general-purpose health representations directly from 24 hours of raw tri-axial wrist movement. We developed and evaluated the model across four population-based cohorts from the United Kingdom, China and the United States, comprising 122,640 participants contributing 683,617 person-days of free-living recordings. Sensori condensed each day of movement into a representation that captured diverse movement behaviours, demographic characteristics, health axes and physical function. Evaluation in independent cohorts showed that these representations generalised across populations and measurement settings without retraining. When added to common clinical covariates, Sensori significantly improved prevalent disease classification for 52 of 102 eligible conditions (median delta AUROC, 0.060; range, 0.012-0.242) and incident disease risk prediction for 26 of 87 eligible conditions (median delta Uno's C-index, 0.064; range, 0.025-0.172), with the largest gains for neurological and psychiatric disorders. These findings establish 24-hour wrist movement as a rich and scalable source of health information, with the potential to support passive health monitoring and disease prediction at population scale.
Source: arXiv (Cornell University)
Authors: Wang, Yong, McGagh, Dylan, Broomberg, Katya, Zhang, Zizheng, Carter, Jonathan, Naushad, Junayed, Brocklebank, Laura, Sun, Yang, Nicholson, George, Sun, Dianjianyi, Yu, Canqing, Lv, Jun, Barnard, Maxim, Lam, Hubert, Steptoe, Andrew, Eyre, David W., Li, Liming, Chen, Zhengming, Wray, Naomi, Denaxas, Spiros, Collins, Gary S., Du, Huaidong, Doherty, Aiden, Yuan, Hang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Wellcome Trust, Cancer Research UK, National Institute for Health and Care Research, British Heart Foundation, Department of Health and Social Care, University of Oxford, National Research Foundation, Danmarks Grundforskningsfond, National Natural Science Foundation of China, National Science and Technology Major Project, National Institutes of Health, Engineering and Physical Sciences Research Council, Biomedical Research Council
Awards: G1018192509, Using wearable sensors to transform the understanding of cardiovascular disease: Prediction, mechanisms, and patient relevant endpoints., G20484840, Using wearable sensors to transform the understanding of cardiovascular disease: Prediction, mechanisms, and patient relevant endpoints., EPSRC Centre for Doctoral Training in Health Data Science, G3119995753, G3178855558, G3262907292, Research Excellence (round 3), G3457649894, G3873921545, G3882161067, G3979305625, G4068791849, G4300974189, G440698137, G4546568785, G4910881425, Brain Networks Underlying Sleep and Circadian Rhythm Disruption (SCRD) in Mental Health, G5496894145, G583177589, G5965697188, G776764879, G8069003457, Brain Networks Underlying Sleep and Circadian Rhythm Disruption (SCRD) in Mental Health
OpenAlex ID: W7204964410