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

Chin-Hsuan Liu,Yen-Lin Chen,Posen Lee,Chen-Wen Yen,Chao-Wei Yu

doi:10.3390/s20051291

Abstract

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.

Highlights

Human beings are bipedal; balance in humans is extremely complex [1]
In In thethe first part of this section, the results obtained using the local Inter-Joint Coordination Pattern (IJCP) features introduced in first part of this section, the results obtained using the local IJCP features introduced in
With the eigenvectors representing a complete set of orthogonal modes of joint velocity movement, the results shown in Figure 8 demonstrate that the relative contributions of these 15 modes of joint velocity movement of the younger age group are less skewed toward the first principal component, and more evenly distributed than the older age group

Summary

Introduction

Human bodies are complex assemblies, which require continuous active control even when standing still. Such active control is achieved through appropriate spatial and temporal body segment coordination. Studies have shown that a decline in postural control ability leads to a deficit in balance and increases the risk of falling [4,5,6,7,8]. Developing a simple assessment method for postural stability can predict the risk of falling. This is crucial because falls are a major public health problem [9,10].

Methods

Results

Discussion

Conclusion