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L Test Subtask Segmentation for Lower-Limb Amputees Using a Random Forest Algorithm
- Source :
- Sensors, Vol 24, Iss 15, p 4953 (2024)
- Publication Year :
- 2024
- Publisher :
- MDPI AG, 2024.
-
Abstract
- Functional mobility tests, such as the L test of functional mobility, are recommended to provide clinicians with information regarding the mobility progress of lower-limb amputees. Smartphone inertial sensors have been used to perform subtask segmentation on functional mobility tests, providing further clinically useful measures such as fall risk. However, L test subtask segmentation rule-based algorithms developed for able-bodied individuals have not produced sufficiently acceptable results when tested with lower-limb amputee data. In this paper, a random forest machine learning model was trained to segment subtasks of the L test for application to lower-limb amputees. The model was trained with 105 trials completed by able-bodied participants and 25 trials completed by lower-limb amputee participants and tested using a leave-one-out method with lower-limb amputees. This algorithm successfully classified subtasks within a one-foot strike for most lower-limb amputee participants. The algorithm produced acceptable results to enhance clinician understanding of a person’s mobility status (>85% accuracy, >75% sensitivity, >95% specificity).
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 24
- Issue :
- 15
- Database :
- Directory of Open Access Journals
- Journal :
- Sensors
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.1f7e1146114ef7a15d2c0994b2bd94
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/s24154953