No 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.

According to the results, the D2 had good test-retest reliability, especially in the scores of TN, TN-E, and CP. For the further research, finding a way to improve the administration procedure to reduce random measurement error would be important for the E1, E2, E, and FR subscores.

The Symbol Digit Modalities Test (SDMT) and Digit Vigilance Test (DVT), both well-recommended attention tests for schizophrenia, are measures of switching and sustained attention, respectively. The purpose of this study was to investigate the test-retest reliability of the two attention tests in schizophrenia. A rater administered both tests on 147 participants with schizophrenia twice at a 1-week interval. Test-retest reliability was determined through the calculation of the intra-class correlation (ICC) coefficient. We also carried out the Bland-Altman analysis, which include a scatter plot of the differences between test and retest against their mean. System biases were evaluated by use of a paired t-test. The ICC for the SDMT was 0.87 and that for the DVT was 0.83. The limits of agreement (LOAs) of the SDMT and DVT were 11.5 to -9.9 correct responses and 156.3 to -249.2 s, respectively. The mean difference scores of the SDMT and DVT were 1.5 (4.7% of the first session mean; p= .002) and -46.4 (7.6% of the first session mean; p< .001). The ICCs show that the SDMT and DVT are stable measures across assessment in different sessions in schizophrenia. However, the paired t-test indicates a practice effect, and the LOAs show large variations. Thus, the SDMT and DVT are reliable for a group of subjects but limited for individual subjects with schizophrenia in 1-week interval clinical trials.

This study proposes an action identification system for home upper extremity rehabilitation. In the proposed system, we apply an RGB-depth (color-depth) sensor to capture the image sequences of the patient’s upper extremity actions to identify its movements. We apply a skin color detection technique to assist with extremity identification and to build up the upper extremity skeleton points. We use the dynamic time warping algorithm to determine the rehabilitation actions. The system presented herein builds up upper extremity skeleton points rapidly. Through the upper extremity of the human skeleton and human skin color information, the upper extremity skeleton points are effectively established by the proposed system, and the rehabilitation actions of patients are identified by a dynamic time warping algorithm. Thus, the proposed system can achieve a high recognition rate of 98% for the defined rehabilitation actions for the various muscles. Moreover, the computational speed of the proposed system can reach 125 frames per second—the processing time per frame is less than 8 ms on a personal computer platform. This computational efficiency allows efficient extensibility for future developments to deal with complex ambient environments and for implementation in embedded and pervasive systems. The major contributions of the study are: (1) the proposed system is not only a physical exercise game, but also a movement training program for specific muscle groups; (2) The hardware of upper extremity rehabilitation system included a personal computer with personal computer and a depth camera. These are economic equipment, so that patients who need this system can set up one set at home; (3) patients can perform rehabilitation actions in sitting position to prevent him/her from falling down during training; (4) the accuracy rate of identifying rehabilitation action is as high as 98%, which is sufficient for distinguishing between correct and wrong action when performing specific action trainings; (5) The proposed upper extremity rehabilitation system is real-time, efficient to vision-based action identification, and low-cost hardware and software, which is affordable for most families.