用深度学习从上肢运动轨迹识别个人特征，助力康复评估与疾病阶段判断。
Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation
- 基于3D上肢轨迹数据，用深度时序模型区分个体运动模式。
- 9人子集分类准确率达95%，31人全集达78%。
- 方法可移植到便携设备，适合临床康复与神经疾病评估。
个体运动特征的识别为康复进展评估和运动障碍程度、阶段诊断提供了基础。本文基于任务空间中测量的3D上肢运输轨迹数据集，开展个体运动模式差异化的初步研究。通过深度时序学习识别个体，是抽象个体运动特性的关键步骤。研究中，9人子集的分类准确率约为95%，31人全集的准确率约为78%。该结果揭示了通过标准化任务提取患者个体属性的可分性，具有向便携系统迁移的潜力。
原文摘要 · Abstract (English)
The identification of individual movement characteristics sets the foundation for the assessment of personal rehabilitation progress and can provide diagnostic information on levels and stages of movement disorders. This work presents a preliminary study for differentiating individual motion patterns using a dataset of 3D upper-limb transport trajectories measured in task-space. Identifying individuals by deep time series learning can be a key step to abstracting individual motion properties. In this study, a classification accuracy of about 95% is reached for a subset of nine, and about 78% for the full set of 31 individuals. This provides insights into the separability of patient attributes by exerting a simple standardized task to be transferred to portable systems.
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