Smart Sensor-Driven Gait Rehabilitation Walker Using Machine Learning for Predictive Home-Based Therapy
≈ 0.9, with a mean RMSE of 0.0385), while providing uncertainty-aware trend estimation. Validation using an independent amyotrophic lateral sclerosis gait dataset further demonstrated the transferability of the analytical pipeline. These results highlight the potential of sensor-enabled assistive walkers as scalable platforms for quantitative gait rehabilitation, adaptive feedback, and long-term mobility monitoring.
Authors
- Gokul Manavalan (ORCID: https://orcid.org/0000-0003-1588-9562)
- Shlomi Arnon (ORCID: https://orcid.org/0000-0001-8048-3089)
- A.N. Nithyaa
- Yuval Arnon
Institutions
- Ben-Gurion University of the Negev (IL)
- Rajalakshmi Engineering College
Publication Details
- Journal
- Sensors
- Published
- 2026-04-21
- DOI
- https://doi.org/10.3390/s26082547
- Primary Topic
- Balance, Gait, and Falls Prevention
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ben-Gurion University of the Negev
- Kreitman School of Advanced Graduate Studies, Ben-Gurion University of the Negev