Automatic multi-IMU-based deep learning evaluation of intensity during static standing balance training exercises
Findings from this study indicated that balance intensity assessment can be achieved through wearable sensors and a CNN model, which could support the supervision and effectiveness of home-based balance rehabilitation.
Authors
- Lauro Ojeda (ORCID: https://orcid.org/0000-0001-6605-3293)
- Xun Huan (ORCID: https://orcid.org/0000-0001-6544-2764)
- Kathleen H. Sienko (ORCID: https://orcid.org/0000-0002-7967-6788)
- Christopher A. DiCesare (ORCID: https://orcid.org/0000-0002-6576-4904)
- Leia A. Stirling (ORCID: https://orcid.org/0000-0002-0119-1617)
- Jenna Wiens (ORCID: https://orcid.org/0000-0002-1057-7722)
- Wendy J. Carender
- Jeremiah Hauth (ORCID: https://orcid.org/0009-0009-3755-2398)
- Safa Jabri (ORCID: https://orcid.org/0000-0001-5418-1087)
Institutions
- University of Michigan (US)
- Michigan Medicine (US)
- Ann Arbor Center for Independent Living (US)
Publication Details
- Journal
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1186/s12984-025-01760-3
- Primary Topic
- Balance, Gait, and Falls Prevention
- Type
- article
- Field-Weighted Citation Impact
- 0.00