Non-contact, non-visual, multi-person hallway gait monitoring
This paper presents a multi-person gait monitoring system designed for efficient operation in cluttered environments. The system demonstrates robust capabilities in tracking multiple closely spaced individuals and accurately extracting the walking speed, even in the presence of others. We address two significant challenges, including enhancing radar resolution and mitigating multipath effects in cluttered settings. Our method shows remarkable accuracy, with a maximum error of 0.33 m/s and a minimum of 0.005 m/s, as validated through 25 walking tests in a bedrest study. Its adaptability makes it a valuable clinical tool, offering insights for predicting underlying health issues in older adults.
Authors
- Ahmad Ansariyan
- Jennifer Boger (ORCID: https://orcid.org/0000-0001-6150-151X)
- Eric T. Hedge (ORCID: https://orcid.org/0000-0002-9507-4533)
- Carmelo Mastrandrea (ORCID: https://orcid.org/0000-0001-6746-6639)
- Plinio Pelegrini Morita (ORCID: https://orcid.org/0000-0001-9515-6478)
- George Shaker (ORCID: https://orcid.org/0000-0002-1450-2138)
- Richard L. Hughson (ORCID: https://orcid.org/0000-0001-6915-2959)
- Hajar Abedi (ORCID: https://orcid.org/0000-0002-4089-0373)
- Alexander Wong (ORCID: https://orcid.org/0000-0001-5729-5899)
Institutions
- University of Waterloo (CA)
- Research Institute for Aging (CA)
Publication Details
- Journal
- Scientific Reports
- Published
- 2025-08-25
- DOI
- https://doi.org/10.1038/s41598-025-97757-y
- Citations
- 2
- Primary Topic
- Gait Recognition and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- McGill University Health Centre
- Canadian Frailty Network
- Canadian Space Agency
- Canadian Institutes of Health Research