{Reference Type}: Journal Article
{Title}: L Test Subtask Segmentation for Lower-Limb Amputees Using a Random Forest Algorithm.
{Author}: McCreath Frangakis AL;Lemaire ED;Burger H;Baddour N;
{Journal}: Sensors (Basel)
{Volume}: 24
{Issue}: 15
{Year}: 2024 Jul 31
{Factor}: 3.847
{DOI}: 10.3390/s24154953
{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).