Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults

Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults, even among those with gait impairments.

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Publication Details

Journal
Scientific Reports
Published
2024-09-06
DOI
https://doi.org/10.1038/s41598-024-71491-3
Citations
16
Primary Topic
Balance, Gait, and Falls Prevention
Type
article
Field-Weighted Citation Impact
18.32

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article

Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults

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16 citations
Scientific Reports
Balance, Gait, and Falls Prevention
18.32
article

Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults

Basil Sharrack, Andrea Cereatti, Luca Palmerini, Jeffrey M. Hausdorff, Aron S. Buchman, Or Perlman, Yonatan E. Brand, Walter Maetzler, Silvia Del Din, Anisoara Paraschiv-Ionescu, Beatrix Vereijken, Lynn Rochester, Clemens Becker, Felix Kluge, Alison Jane Yarnall, Arne Müller
article en
16 citations

Abstract

Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults, even among those with gait impairments.

Scientific ReportsVol. 14(1)
Novartis (Switzerland) (CH), Rush University Medical Center (US), Tel Aviv University (IL), Politecnico di Torino (IT), Norwegian University of Science and Technology (NO), Heidelberg University (DE), Tel Aviv Sourasky Medical Center (IL), Marconi University (IT), Newcastle upon Tyne Hospitals NHS Foundation Trust (GB), Robert Bosch Stiftung (DE), University Hospital Heidelberg (DE), Laboratori Guglielmo Marconi (Italy) (IT), Sheffield Teaching Hospitals NHS Foundation Trust (GB), NIHR Newcastle Biomedical Research Centre (GB), Rush University (US), École Polytechnique Fédérale de Lausanne (CH), Newcastle University (GB), University of Bologna (IT)
European Federation of Pharmaceutical Industries and Associations, European Commission, Innovative Medicines Initiative, National Institutes of Health
Good health and well-being
Openalex Percentile: Top 0%
Balance, Gait, and Falls Prevention
18.32
DOIOpen Access PDFOpenAlex
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