This study proposes a new calculation method for the anomaly score of repetitive tasks based on singular spectrum transformation (SST) that accounted for a long‐term history of human motion. To validate the efficacy of the proposed method, the calculated anomaly score was compared with movement variability computed by a traditional method and to its SST‐computed score. Eleven male participants performed repetitive lightweight material handling tasks under different work conditions and an electromagnetic tracking system measured their working posture. Movement variability and anomaly score on the shoulder and elbow joints were calculated based on measured working postures. The movement variability on the elbow flexion angle increased with time. In contrast, the anomaly score of the elbow flexion angle decreased with time, but shoulder flexion and inner rotation angles showed increased scores with the passage of time. These findings are similar to those of previous studies that stated that movement variability increased from redundant degrees of freedom available for performing multi‐joint movements; this occurred due to the development of muscle fatigue on the shoulder joint from performing repetitive tasks. On comparing this to the anomaly scores calculated by conventional SST, it was observed that the score computed by the proposed method reflected the whole trend of human motion in repetitive tasks and did not depend on local problems in working posture. Therefore, it was concluded that the new method of calculating the anomaly score is more suitable to detect changes in movement variability in repetitive tasks.

中文翻译：

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一种计算重复任务运动变异性异常得分的新方法

这项研究提出了一种基于奇异频谱变换（SST）的重复性任务异常评分的新计算方法，该方法解释了人类运动的长期历史。为了验证所提出方法的有效性，将计算出的异常分数与传统方法计算出的运动变异性及其SST计算出的分数进行了比较。11名男性参与者在不同的工作条件下执行了重复的轻型物料搬运任务，并且电磁跟踪系统测量了他们的工作姿势。根据测得的工作姿势计算肩关节和肘关节的运动变异性和异常评分。肘关节弯曲角度的运动变化随时间增加。相比之下，肘关节屈曲角度的异常评分随时间降低，但随着时间的流逝，肩膀的屈曲度和内旋转角显示得分增加。这些发现与以前的研究相似，后者指出运动的可变性由于可进行多关节运动的冗余自由度而增加；发生这种情况是由于执行重复性任务导致肩关节肌肉疲劳的发展。通过将其与常规SST计算出的异常分数进行比较，可以发现，该方法计算出的分数反映了重复性任务中人体运动的整个趋势，并且不依赖于工作姿势中的局部问题。因此，可以得出结论，计算异常得分的新方法更适合于检测重复性任务中运动变异性的变化。