GoNet ‑ A New Movement Dynamic Evaluation System in Real Time

Abstract

The development of a hybrid solution (hardware and software) integrating a computer and the Kinect sensor is presents in this paper. The solution proposed, here called GoNet, is a promising prototype to be used in dynamic and automatic evaluation of biomechanical rehabilitation processes. Experimental tests concerning the assessment of the range of motion of patients, particularly for elbow flexion, elbow extension, shoulder abduction, shoulder flexion, radial deviation and ulnar deviation, are presented and discussed. Eight healthy subjects were assessed using GoNet and a goniometer. The intraclass correlation coefficient (ICC) was used to analyze the reproducibility, and the Pearson correlation test was used in the analysis of transversal validity. The significance level was defined as equal to 5%. As to the intra-and inter-examiner reproducibility, high ICC values were found for the range of motion of shoulder flexion/extension, shoulder abduction/adduction, radial deviation and ulnar deviation. When evaluated by two experts the correlation between the goniometry and GoNet, significant results were observed for the amplitude of shoulder flexion/extension (r = 0.74; p = 0.03) and elbow flexion/extension (r = 0.67, p = 0.04). Based on the results obtained, GoNet proved to have high reproducibility, except for intraexaminer assessment of elbow flexion/extension. Regarding the transverse validity, relevant measurement results were found in terms of flexion/extension of the elbow and shoulder.

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“…If the change of pure technical efficiency is greater than 1, the productivity will increase, and the change of scale efficiency is greater than 1, indicating that the scale converges to the optimal scale. The specific index formula is shown in formula (3) [23][24][25]…”

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“…ej., Kinect o Leap Motion) ha permitido que se puedan usar como dispositivos de entrada para interfaces de usuario naturales. Dichos sensores se han utilizado con distintos objetivos y para una gran variedad de aplicaciones, como por ejemplo aprendizaje [7]; rehabilitación [8] reconocimiento del lenguaje de signos [9] [10]; o diagnóstico de parálisis facial [11]. La Tabla I muestra las características de los sensores de profundidad comerciales más utilizados, según Cheng et al [9].…”