PDF(Pubmed)
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).
摘要：
功能流动性测试，例如功能移动性的L测试，建议为临床医生提供有关下肢截肢者活动进展的信息。智能手机惯性传感器已用于对功能移动性测试执行子任务分段，提供进一步的临床有用的措施，如跌倒风险。然而,当使用下肢截肢者数据进行测试时，为健全的个体开发的基于规则的L测试子任务分割算法没有产生足够可接受的结果。在本文中,对随机森林机器学习模型进行了训练,以分割L检验的子任务,应用于下肢截肢者.该模型由身体健全的参与者完成的105项试验和下肢截肢者完成的25项试验进行了训练，并使用留一法对下肢截肢者进行了测试。对于大多数下肢截肢者参与者，此算法成功地将子任务分类为一英尺打击。该算法产生了可接受的结果，以增强临床医生对人的移动状态的理解(>85%的准确性，>75%的灵敏度，>95%特异性)。
