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PreprintCited by 19Context-Aware Activity Recognition SystemsBalance, Gait, and Falls PreventionGait Recognition and Analysis

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Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist- Worn Accelerometer Data: Development and Validation of ElderNet

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Brand, Yonatan E., Kluge, Felix, Palmerini, Luca, Paraschiv-Ionescu, Anisoara, Becker, Clemens, Cereatti, Andrea, Maetzler, Walter, Sharrack, Basil, Vereijken, Beatrix, Yarnall, Alison J., Rochester, Lynn, Din, Silvia Del, Muller, Arne, Buchman, Aron S., Hausdorff, Jeffrey M., Perlman, Or·2024·Scientific Reports
DOI 10.21203/rs.3.rs-4102403/v1PMC PMC11379690

Preprint — published as 10.1038/s41598-024-71491-3 · Paper page

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Abstract

Progressive gait impairment is common in 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 1,000 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 gait detection method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults.

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Metadata

Authors
Brand, Y. E., Kluge, F., Palmerini, L., Paraschiv-Ionescu, A., Becker, C., Cereatti, A., Maetzler, W., Sharrack, B., Vereijken, B., Yarnall, A. J., Rochester, L., Din, S. D., Muller, A., Buchman, A. S., Hausdorff, J. M., & Perlman, O.
Affiliations
  • Tel Aviv University (IL)
  • Novartis (Switzerland) (CH)
  • University of Bologna (IT)
  • École Polytechnique Fédérale de Lausanne (CH)
  • University Hospital Heidelberg (DE)
  • Politecnico di Torino (IT)
  • University Hospital Schleswig-Holstein (DE)
  • Sheffield Teaching Hospitals NHS Foundation Trust (GB)
  • Norwegian University of Science and Technology (NO)
  • Newcastle upon Tyne Hospitals NHS Foundation Trust (GB)
  • Novartis Pharma AG
  • Rush University Medical Center (US)
Corresponding author
Perlman, Or
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2024-03-15
Volume
14
Issue
1
Pages
20854-20854
ISSN
2045-2322 (ISSN-L)
DOI
10.21203/rs.3.rs-4102403/v1
PMC
PMC11379690
OpenAlex
W4402307918
Cited by
19
References
1
Citations by year
2026: 6 · 2025: 13
FWCI
2.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PORTO@Iris (submittedVersion, cc-by)
  • PubMed Central (submittedVersion, cc-by)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • MACAU: Open Access Repository of Kiel University (University Library Kiel) (submittedVersion, cc-by)
  • Newcastle University ePrints (Newcastle Univesity) (cc-by)
Funders
  • European Federation of Pharmaceutical Industries and Associations (820820)
  • European Commission (820820, HORIZON2020, COVID-19)
  • Innovative Medicines Initiative (820820)
  • National Institutes of Health (COVID-19)
  • National Institute on Aging (R01AG017917, R01AG056352, R01AG079133)
  • National Center for Complementary and Integrative Health (R01AT012228)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
  • Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
Keywords
gait detection, self-supervised learning, wrist-worn accelerometers, older adults, gait disturbance, Parkinson's disease, activities of daily living, walking time, mobility impairment

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