Department of Mechanical Engineering, Faculty of Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada.
Sensors (Basel). 2024 Jul 31;24(15):4953. doi: 10.3390/s24154953.
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).
功能性移动测试，如功能性移动 L 测试，被推荐为临床医生提供有关下肢截肢者移动进展的信息。智能手机惯性传感器已被用于对功能性移动测试进行子任务分段，提供进一步的临床有用的措施，如跌倒风险。然而，为健全人开发的基于规则的 L 测试子任务分段算法，在使用下肢截肢者数据进行测试时，并未产生足够可接受的结果。在本文中，训练了一个随机森林机器学习模型，用于分割 L 测试的子任务，以便应用于下肢截肢者。该模型使用 105 次健全参与者完成的试验和 25 次下肢截肢者参与者完成的试验进行训练，并使用下肢截肢者的留一法进行测试。该算法成功地对大多数下肢截肢者参与者的单足触地进行了子任务分类。该算法产生了可接受的结果，增强了临床医生对患者移动状态的理解（>85%的准确率，>75%的灵敏度，>95%的特异性）。
