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Continuous assessment of daily-living gait using self-supervised learning of wrist-worn accelerometer data

Lookup NU author(s): Professor Alison YarnallORCiD, Professor Lynn RochesterORCiD, Dr Silvia Del DinORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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Author(s): Brand YE, Buchman AS, Kluge F, Palmerini L, Becker C, Cereatti A, Maetzler W, Vereijken B, Yarnall AJ, Rochester L, Del Din S, Mueller A, Hausdorff JM, Perlman O

Publication type: Article

Publication status: Published

Journal: npj Digital Medicine

Year: 2026

Pages: epub ahead of print

Online publication date: 12/03/2026

Acceptance date: 27/02/2026

Date deposited: 16/03/2026

ISSN (electronic): 2398-6352

Publisher: Nature Publishing Group

URL: https://doi.org/10.1038/s41746-026-02528-2

DOI: 10.1038/s41746-026-02528-2

Data Access Statement: The dataset from the Mobilise-D technical validation study (Dataset 3) can be found on Zenodo: https://doi.org/10.5281/zenodo.13899386. All other data and related algorithms (i.e., Dataset 2) included in these analyses are available via the Rush Alzheimer’s Disease Center Research Resource Sharing Hub, which can be found at www.radc.rush.edu. It has descriptions of the studies and available data. Any qualified investigator can create an account and submit requests for de-identified data.

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