Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study
This study highlights the potential of integrating advanced sensor technology with machine learning techniques in the analysis of complex clinical data. The developed GDI prediction model, based on sensor-acquired isokinetic dynamometry data, offers a novel, streamlined, and effective tool for assessing rehabilitation progress in post-stroke hemiplegic patients, with promising implications for broader clinical application.
Authors
- Chenye Qiao
- Xiaolei Lu (ORCID: https://orcid.org/0000-0002-9545-2047)
- Y Li (ORCID: https://orcid.org/0000-0002-2070-8714)
- Shuyan Qie (ORCID: https://orcid.org/0000-0003-0787-8986)
- Congxiao Wang
- Jingxuan Wang (ORCID: https://orcid.org/0009-0001-1746-9628)
- Yingpeng Wang (ORCID: https://orcid.org/0000-0003-4387-4667)
- Hujun Wang (ORCID: https://orcid.org/0000-0003-4446-6657)
Institutions
- Capital Medical University (CN)
- China Rehabilitation Research Center (CN)
Publication Details
- Journal
- Sensors
- Published
- 2024-11-13
- DOI
- https://doi.org/10.3390/s24227258
- Citations
- 5
- Primary Topic
- Balance, Gait, and Falls Prevention
- Type
- article
- Field-Weighted Citation Impact
- 5.72
Funders
- National Key Research and Development Program of China