New movement assessment and data analysis methods are developed to quantify human arm motion patterns during physical interaction with robotic devices for rehabilitation. These methods provide metrics for future use in diagnosis, assessment and rehabilitation of subjects with affected arm movements. Specifically, the current study uses existing pattern recognition methods to evaluate the effect of age on performance of a specific motion, reaching to a target by moving the end-effector of a robot (an X-Y table). Differences in the arm motion patterns of younger and older subjects are evaluated using two measures: the principal component analysis similarity factor (S_PCA) to compare path shape and the number of Fourier modes representing 98% of the path ‘energy’ to compare the smoothness of movement, a particularly important variable for assessment of pathologic movement. Both measures are less sensitive to noise than others previously reported in the literature and preserve information that is often lost through other analysis techniques. Data from the S_PCA analysis indicate that age is a significant factor affecting the shapes of target reaching paths, followed by reaching movement type (crossing body midline/not crossing) and reaching side (left/right); hand dominance and trial repetition are not significant factors. Data from the Fourier-based analysis likewise indicate that age is a significant factor affecting smoothness of movement, and movements become smoother with increasing trial number in both younger and older subjects, although more rapidly so in younger subjects. These results using the proposed data analysis methods confirm current practice that age-matched subjects should be used for comparison to quantify recovery of arm movement during rehabilitation. The results also highlight the advantages that these methods offer relative to other reported measures.

中文翻译：

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与康复机器人设备互动时量化与人类接触的年龄相关差异

开发了新的运动评估和数据分析方法，以量化与机器人设备进行物理交互时人体手臂的运动方式，以进行康复。这些方法提供了度量标准，供将来在手臂运动受到影响的受试者进行诊断，评估和康复时使用。具体而言，当前的研究使用现有的模式识别方法来评估年龄对特定运动效果的影响，方法是通过移动机器人的末端执行器（XY工作台）到达目标。年轻人和老年人受试者手臂运动方式的差异可通过以下两种方法进行评估：主成分分析相似性因子（S _PCA）以比较路径形状和代表路径“能量”的98％的傅立叶模式数目，以比较运动的平滑度，这是评估病理运动的特别重要的变量。两种措施对噪声的敏感度均不如先前文献中报道的其他措施，并且保留了经常通过其他分析技术丢失的信息。来自S _PCA的数据分析表明，年龄是影响目标到达路径形状的重要因素，其次是到达运动类型（越过身体中线/不越过）和到达侧面（左/右）；手的优势和反复试验不是重要因素。来自基于傅立叶的分析的数据同样表明，年龄是影响运动平稳性的重要因素，并且随着试验次数的增加，在年轻和老年受试者中，运动变得更加平滑，尽管在年轻受试者中运动更快。使用建议的数据分析方法得出的这些结果证实了当前的做法，即应该使用年龄匹配的受试者进行比较，以量化康复期间手臂运动的恢复情况。结果还突出显示了这些方法相对于其他已报道的方法所提供的优势。