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Self-Supervised Learning of Wrist-Worn Daily Living Accelerometer Data Improves the Automated Detection of Gait in Older Adults

Lookup NU author(s): Professor Alison Yarnall, 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, Kluge F, Palmerini L, Paraschiv-Ionescu A, Becker C, Cereatti A, Maetzler W, Sharrack B, Vereijken B, Yarnall AJ, Rochester L, Del Din S, Muller A, Buchman AS, Hausdorff JM, Perlman O

Publication type: Article

Publication status: Published

Journal: Scientific Reports

Year: 2024

Volume: 14

Online publication date: 06/09/2024

Acceptance date: 28/08/2024

Date deposited: 05/09/2024

ISSN (electronic): 2045-2322

Publisher: Nature Publishing Group

URL: https://doi.org/10.1038/s41598-024-71491-3

DOI: 10.1038/s41598-024-71491-3

Data Access Statement: Raw data of a representative participant (dataset YAR, participant 0002) can be found on Zenodo: https://doi.org/https://doi.org/10.5281/zenodo.7185429. The full data set will be made available by the Mobilise-D consortium after June 2024. All MAP data 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 (accessed on 17 April 2023). It has descriptions of the studies and available data. Any qualified investigator can create an account and submit requests for deidentified data. The code supporting this study is accessible on GitHub at the following link: https://github.com/yonbrand/ElderNet.

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