Lau Newman, Wong Ben, Chow Daniel
Multimedia Innovation Centre, School of Design, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
J Biomech. 2009 Mar 11;42(4):436-42. doi: 10.1016/j.jbiomech.2008.11.038. Epub 2009 Feb 6.
Motion segmentation and analysis are used to improve the process of classification of motion and information gathered on repetitive or periodic characteristic. The classification result is useful for ergonomic and postural safety analysis, since repetitive motion is known to be related to certain musculoskeletal disorders. Past studies mainly focused on motion segmentation on particular motion characteristic with certain prior knowledge on static or periodic property of motion, which narrowed method's applicability. This paper attempts to introduce a method to tackle human joint motion without having prior knowledge. The motion is segmented by a two-pass algorithm. Recursive least square (RLS) is firstly used to estimate possible segments on the input human-motion set. Further, period identification and extra segmentation process are applied to produce meaningful segments. Each of the result segments is modeled by a damped harmonic model, with frequency, amplitude and duration produced as parameters for ergonomic evaluation and other human factor studies such as task safety evaluation and sport analysis. Experiments show that the method can handle periodic, random and mixed characteristics on human motion, which can also be extended to the usage in repetitive motion in workflow and irregular periodic motion like sport movement.
运动分割与分析用于改进对运动的分类过程以及收集到的关于重复或周期性特征的信息。分类结果对于人体工程学和姿势安全分析很有用，因为已知重复运动会与某些肌肉骨骼疾病相关。过去的研究主要集中在基于对运动的静态或周期性特性的某些先验知识，对特定运动特征进行运动分割，这限制了方法的适用性。本文试图介绍一种无需先验知识就能处理人体关节运动的方法。该运动通过两遍算法进行分割。首先使用递归最小二乘法（RLS）来估计输入人体运动集上的可能片段。此外，应用周期识别和额外的分割过程来生成有意义的片段。每个结果片段都由一个阻尼谐波模型建模，生成频率、幅度和持续时间作为参数，用于人体工程学评估以及其他人体因素研究，如任务安全评估和运动分析。实验表明，该方法可以处理人体运动中的周期性、随机性和混合特征，还可以扩展到工作流程中的重复运动以及像体育动作这样的不规则周期性运动的应用中。
