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Inter-joint coupling and joint angle synergies of human catching movements.

机译:人体捕捉运动的关节间耦合和关节角度协同作用。

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摘要

A central question in motor control is how the central nervous system (CNS) deals with redundant degrees of freedom (DoFs) inherent in the musculoskeletal system. One way to simplify control of a redundant system is to combine several DoFs into synergies. In reaching movements of the human arm, redundancy occurs at the kinematic level because there is an unlimited number of arm postures for each position of the hand. Redundancy also occurs at the level of muscle forces because each arm posture can be maintained by a set of muscle activation patterns. Both postural and force-related motor synergies may contribute to simplify the control problem. The present study analyzes the kinematic complexity of natural, unrestrained human arm movements, and detects the amount of kinematic synergy in a vast variety of arm postures. We have measured inter-joint coupling of the human arm and shoulder girdle during fast, unrestrained, and untrained catching movements. Participants were asked to catch a ball launched towards them on 16 different trajectories. These had to be reached from two different initial positions. Movement of the right arm was recorded using optical motion capture and was transformed into 10 joint angle time courses, corresponding to 3 DoFs of the shoulder girdle and 7 of the arm. The resulting time series of the arm postures were analyzed by principal components analysis (PCA). We found that the first three principal components (PCs) always captured more than 97% of the variance. Furthermore, subspaces spanned by PC sets associated with different catching positions varied smoothly across the arm's workspace. When we pooled complete sets of movements, three PCs, the theoretical minimum for reaching in 3D space, were sufficient to explain 80% of the data's variance. We assumed that the linearly correlated DoFs of each significant PC represent cardinal joint angle synergies, and showed that catching movements towards a multitude of targets in the arm's workspace can be generated efficiently by linear combinations of three of such synergies. The contribution of each synergy changed during a single catching movement and often varied systematically with target location. We conclude that unrestrained, one-handed catching movements are dominated by strong kinematic couplings between the joints that reduce the kinematic complexity of the human arm and shoulder girdle to three non-redundant DoFs.
机译:运动控制中的一个中心问题是中枢神经系统(CNS)如何处理肌肉骨骼系统固有的冗余自由度(DoF)。简化冗余系统控制的一种方法是将多个自由度组合为协同效应。在达到人的手臂运动时,在运动学上会出现冗余,因为手的每个位置都有无限数量的手臂姿势。冗余也发生在肌肉力量的水平,因为每个手臂的姿势都可以通过一组肌肉激活模式来维持。姿势和与力有关的运动协同作用均可有助于简化控制问题。本研究分析了自然的,不受约束的人类手臂运动的运动学复杂性,并检测了各种手臂姿势中的运动学协同作用量。我们已经测量了在快速,不受约束和未经训练的捕捉运动过程中人的手臂和肩带的关节间耦合。要求参与者在16个不同的轨迹上接住一个向他们发射的球。必须从两个不同的初始位置达到这些目标。使用光学运动捕捉记录右臂的运动,并将其转换为10个关节角时程,分别对应于肩带的3个自由度和手臂的7个自由度。通过主成分分析(PCA)分析得到的手臂姿势的时间序列。我们发现前三个主成分(PC)始终捕获超过97%的方差。此外,与不同捕捉位置相关联的PC机所跨越的子空间在手臂的工作空间中平滑变化。当我们收集完整的运动集时,三台PC(在3D空间中达到的理论最小值)足以解释80%的数据差异。我们假设每个重要PC的线性相关的DoF代表基本关节角度的协同作用,并表明通过三个这种协同作用的线性组合可以有效地产生向手臂工作空间中多个目标的捕捉运动。每个协同作用的贡献在一次捕捉动作中就发生了变化,并且通常随目标位置而系统地变化。我们得出的结论是,不受约束的单手捕捉运动主要是关节之间的强运动学耦合,这将人的手臂和肩带的运动复杂性降低到三个非冗余自由度。

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