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

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.

A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment

“…A total number of 13 articles were included for the assessment of posture abnormality or disorder. Some of the reviewed articles [42,46,50,51] did not state the participants in the studies. In Table 4, Kinect v2 was mostly used for skeletal data capturing, except in a few studies that used Kinect v1.…”

Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint–Node Plots

“…Using joint motion trajectories instead of COP or COM displacement for analysis enables the evaluation of posture control ability as well as the posture control strategies used to achieve balance [ 45 , 46 ]. In the current study, the JNPs of the elderly group indicated that they tended to use an extreme joint coordination mode, an inter-joint coordination strategy characterized by total joint dependence, to maintain balance when standing still [ 19 ].…”

“…Kinect’s kinematic information is generally accurate enough for ergonomic assessments [ 18 ]. Postural control is the coordination of multiple joints to maintain postural stability, and the device can be used to collect large amounts of joints data to explore the coordinated relationships among the joints of the whole body during the maintenance of postural control [ 19 ]. Kinect’s kinematic parameters follow joint trajectories and, thus, can be used as a tool for measuring spatiotemporal aspects of postural control.…”

Analysis of the Stability of the Body in a Standing Position When Shooting at a Stationary Target―A Randomized Controlled Trial

“…It is responsible for the control of reflex reactions of the body [ 6 ] and the return of the body’s center of gravity to its balance [ 7 , 8 ]. The central nervous system supervises the maintenance of balance by integrating sensory stimuli from the peripheral nervous system and motor signals from the musculoskeletal system [ 9 , 10 , 11 ]. Postural control is also influenced by genetically determined reactive strategies such as ankle strategies, hip strategies, and steps strategies [ 12 , 13 ].…”