%0 Journal Article
%T L Test Subtask Segmentation for Lower-Limb Amputees Using a Random Forest Algorithm.
%A McCreath Frangakis AL
%A Lemaire ED
%A Burger H
%A Baddour N
%J Sensors (Basel)
%V 24
%N 15
%D 2024 Jul 31
%M 39124000
%F 3.847
%R 10.3390/s24154953
%X 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).