It measures and reports the accuracy of a sensor that can be directly used for monitoring physical therapy exercises. Using this sensor facilitates remote rehabilitation.

BackgroundPerformance indices provide quantitative measures for the quality of motion, and therefore, assist in analyzing and monitoring patients’ progress. Measurement of performance indices requires costly devices, such as motion capture systems. Recent developments of sensors for game controllers, such as Microsoft Kinect, have motivated many researchers to develop affordable systems for performance measurement applicable to home and clinical care. In this work, the capability of Kinect in finding motion performance indices was assessed by analyzing intra-session and inter-session test–retest reliability.MethodEighteen stroke patients and twelve healthy subjects participated in this investigation. The intra-session and inter-session reliability of eight performance indices, namely mean velocity (MV), normalized mean speed (NMS), normalized speed peaks (NSP), logarithm of dimensionless jerk (LJ), curvature (C), spectral arc length (SAL), shoulder angle (SA), and elbow angle (EA), were assessed using intra-class correlation coefficient (ICC), standard error of measurement (SEM) and coefficient of variation (CV).ResultsThe results showed that, among the performance indices, MV, LJ, C, SA and EA have more than 0.9 ICC together with an acceptable SEM and CV in both stroke patients and healthy subjects. Comparing the results of different therapy sessions showed that MV, LJ and C are more sensitive than other indices, and hence, more capable of reflecting the progress of a patient during the rehabilitation process.ConclusionThe results of this study shows acceptable reliability and sensitivity across the sessions for MV, LJ and C measured by Kinect for both healthy subjects and stroke patients. The results are promising for the development of home-based rehabilitation systems, which can analyze patient’s movements using Kinect as an affordable motion capture sensor.

Abstract. A ordable motion sensors that are recently developed for video gaming have formed a budding line of research in the eld of physical rehabilitation. These sensors have been used in many task-based applications to analyze the patients' status based on their completion of assigned tasks. However, as the accuracy of such sensors is lower than that of the clinical ones, their measured data has had very limited use in quantitative motion analysis to this date. The aim of this article is to determine Kinect's ability and accuracy in calculating higher-order kinematic parameters, such as velocity and acceleration, in hand movements. Four methods, i.e. moving average, Butterworth lter, B-spline, and Kalman lter, were proposed to calculate velocity and acceleration from Kinect's raw position data. The results were experimentally compared with two established motion capture systems, i.e. Vicon and Xsens, to analyze the strengths and weaknesses of each method. The results show that B-spline is the best method for calculating velocity and acceleration from Kinect's position data. Using this method, these parameters can be measured with an acceptable accuracy.

Recently online prediction of plate deformations in modern systems have been considered by many researchers, common standard methods are highly time consuming and powerful processors are needed for online computation of deformations. Artificial neural networks have capability to develop complex, nonlinear functional relationships between input and output patterns based on limited data. A good trained network could predict output data very fast with acceptable accuracy. This paper describes the application of an artificial neural network to identify deformation pattern of a four-side clamped plate under external loads. In this paper the distributed loads are approximated by a set of concentrated loads. An artificial neural network is designed to predict plate deformation pattern under external forces. Results indicate a well trained artificial neural network reveals an extremely fast convergence and a high degree of accuracy in the process of predicting deformation pattern of plates. Additionally this paper represents application of neural network in inverse problem. This part illustrates the capability of neural networks in identification of plate external loads based on plate deformations. Load identification has many applications in identification of real loads in machineries for design and development.

In this study, motion performance indices based on the kinematics of upper body have been presented and compared to be used in a home-based rehabilitation device. Microsoft Kinect sensor is used to extract and calculate such indices. A set of experiments has been designed and carried out in which, kinematic data of three patients has been recorded. Finally, the selected indices have been calculated, and the results were compared with those of a healthy subject.