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首页> 外文期刊>Journal of Biomechanics >Application of a symbolic motion structure representation algorithm to identify upper extremity kinematic changes during a repetitive task
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Application of a symbolic motion structure representation algorithm to identify upper extremity kinematic changes during a repetitive task

机译：符号运动结构表示算法在重复任务期间识别上肢运动变化的应用

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摘要

Efficient and holistic identification of fatigue-induced movement strategies can be limited by large between-subject variability in descriptors of joint angle data. One promising alternative to traditional, or computationally intensive methods is the symbolic motion structure representation algorithm (SMSR), which identifies the basic spatial-temporal structure of joint angle data using string descriptors of temporal joint angle trajectories. This study attempted to use the SMSR to identify changes in upper extremity time series joint angle data during a repetitive goal directed task causing muscle fatigue. Twenty-eight participants (15 M, 13 F) performed a seated repetitive task until fatigued. Upper extremity joint angles were extracted from motion capture for representative task cycles. SMSRs, averages and ranges of several joint angles were compared at the start and end of the repetitive task to identify kinematic changes with fatigue. At the group level, significant increases in the range of all joint angle data existed with large between-subject variability that posed a challenge to the interpretation of these fatigue-related changes. However, changes in the SMSRs across participants effectively summarized the adoption of adaptive movement strategies. This establishes SMSR as a viable, logical, and sensitive method of fatigue identification via kinematic changes, with novel application and pragmatism for visual assessment of fatigue development. (C) 2018 Elsevier Ltd. All rights reserved.
机译：高效且整体识别疲劳诱导的运动策略可以受关节角度数据描述符中的较大的对象变异性的限制。传统或计算密集方法的一个有前途的替代方法是符号运动结构表示算法（SMSR），其使用时间关节角轨迹的字符串描述符识别关节角度数据的基本空间时间结构。本研究试图使用SMSR在重复的目标定向任务期间识别上肢时间序列关节角度数据的变化，导致肌肉疲劳。二十八名参与者（15米，13岁）进行了坐姿的重复任务，直至疲劳。从运动捕获中提取上肢关节角度的代表性任务循环。在重复任务的开始和结束时比较了几个关节角度的SMSR，平均值和范围，以识别具有疲劳的运动变化。在群体层面，所有关节角度数据的范围都存在显着增加，其对象变异性很大，对这些疲劳相关的变化的解释构成了挑战。然而，参与者的SMSR的变化有效地总结了采用自适应运动策略。这使SMSR建立为通过运动变化的可行性，逻辑和敏感的疲劳鉴定方法，具有新颖的应用和实用主义，可视化疲劳发育。 （c）2018年elestvier有限公司保留所有权利。

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