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.

翻译：个体运动特征的识别为评估个人康复进展奠定基础，并能提供关于运动障碍程度与阶段诊断信息。本研究利用任务空间测量的三维上肢传输轨迹数据集，开展区分个体运动模式的初步研究。通过深度时间序列学习识别个体，可成为抽象化个体运动属性的关键步骤。本研究中，九人子集的分类准确率约达95%，而31人完整集的准确率约为78%。这为通过执行可移植至便携系统的简单标准化任务来分离患者属性提供了见解。