Continuous Assessment of Daily-Living Gait Using Self-Supervised Learning of Wrist-Worn Accelerometer Data.

Journal: medRxiv : the preprint server for health sciences
Published Date: May 21, 2025

Abstract

Physical activity and mobility are critical for healthy aging and predict diverse health outcomes. While wrist-worn accelerometers are widely used to monitor physical activity, estimating gait metrics from wrist data remains challenging. We extend ElderNet, a self-supervised deep-learning model previously validated for walking-bout detection, to estimate gait metrics from wrist accelerometry. Validation involved 819 older adults (Rush-Memory- and-Aging-Project) and 85 individuals with gait impairments (Mobilise-D), from six medical centers. In Mobilise-D, ElderNet achieved an absolute error of 8.82 cm/s and an intra-class correlation of 0.87 for gait speed, outperforming state-of-the-art methods (p < 0.001) and models using a lower-back sensor. ElderNet outperformed (percentage error; p < 0.01) competing approaches in estimating cadence and stride length, and better (p < 0.01) classified mobility disability (AUC = 0.80) than conventional gait or physical activity metrics. These results render ElderNet a scalable tool for gait assessment using wrist-worn devices in aging and clinical populations.

Authors

  • Yonatan E Brand
    Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.
  • Aron S Buchman
    Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA.
  • Felix Kluge
    Biomedical Research, Novartis Pharma AG, Basel, Switzerland.
  • Luca Palmerini
    Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40136 Bologna, Italy.
  • Clemens Becker
  • Andrea Cereatti
    Information Engineering Unit, POLCOMING Department, University of Sassari, Sassari 07100, Italy. acereatti@uniss.it.
  • Walter Maetzler
    Department of Neurology, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany; Center for Neurology and Hertie Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tuebingen, 72074 Tuebingen, Germany; German Center for Neurodegenerative Diseases (DZNE), 72076 Tuebingen, Germany.
  • Beatrix Vereijken
    Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7030 Trondheim, Norway.
  • Alison J Yarnall
    Institute of Neuroscience/Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK.
  • Lynn Rochester
    Institute of Neuroscience, Newcastle University, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK.
  • Silvia Del Din
  • Arne Mueller
    Biomedical Research, Novartis Pharma AG, Basel, Switzerland.
  • Jeffrey M Hausdorff
    The Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Or Perlman
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA. operlman@mgh.harvard.edu.

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