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Study of Postural Stability Features by Using Kinect Depth Sensors to Assess Body Joint Coordination Patterns

机译:Kinect深度传感器评估人体关节协调模式的姿势稳定性特征研究

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摘要

A stable posture requires the coordination of multiple joints of the body. This coordination of the multiple joints of the human body to maintain a stable posture is a subject of research. The number of degrees of freedom (DOFs) of the human motor system is considerably larger than the DOFs required for posture balance. The manner of managing this redundancy by the central nervous system remains unclear. To understand this phenomenon, in this study, three local inter-joint coordination pattern (IJCP) features were introduced to characterize the strength, changing velocity, and complexity of the inter-joint couplings by computing the correlation coefficients between joint velocity signal pairs. In addition, for quantifying the complexity of IJCPs from a global perspective, another set of IJCP features was introduced by performing principal component analysis on all joint velocity signals. A Microsoft Kinect depth sensor was used to acquire the motion of 15 joints of the body. The efficacy of the proposed features was tested using the captured motions of two age groups (18–24 and 65–73 years) when standing still. With regard to the redundant DOFs of the joints of the body, the experimental results suggested that an inter-joint coordination strategy intermediate to that of the two extreme coordination modes of total joint dependence and independence is used by the body. In addition, comparative statistical results of the proposed features proved that aging increases the coupling strength, decreases the changing velocity, and reduces the complexity of the IJCPs. These results also suggested that with aging, the balance strategy tends to be more joint dependent. Because of the simplicity of the proposed features and the affordability of the easy-to-use Kinect depth sensor, such an assembly can be used to collect large amounts of data to explore the potential of the proposed features in assessing the performance of the human balance control system.
机译:稳定的姿势需要协调身体的多个关节。人体多个关节的这种协调以保持稳定的姿势是研究的主题。人体电机系统的自由度(DOF)数量大大多于姿势平衡所需的DOF。中枢神经系统管理这种冗余的方式仍不清楚。为了理解这种现象,在这项研究中,通过计算关节速度信号对之间的相关系数,引入了三个局部关节间协调模式(IJCP)特征来表征关节间耦合的强度,变化速度和复杂性。此外,为了从全局角度量化IJCP的复杂性,通过对所有联合速度信号执行主成分分析,引入了另一套IJCP功能。 Microsoft Kinect深度传感器用于获取人体15个关节的运动。静止不动时使用两个年龄段(18-24岁和65-73岁)捕获的动作测试了建议功能的有效性。关于人体关节的冗余自由度,实验结果表明,人体采用了一种介于关节完全依赖和独立性两种极端协调模式中间的关节间协调策略。此外,所提出特征的比较统计结果证明,时效增加了耦合强度,降低了变化速度,并降低了IJCP的复杂性。这些结果还表明,随着年龄的增长,平衡策略趋向于更加依赖联合。由于所建议功能的简单性和易于使用的Kinect深度传感器的价格可承受性,此类组件可用于收集大量数据,以探索所建议功能在评估人体平衡性能方面的潜力控制系统。

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