Abstract

This paper develops computational models to monitor patients with hip replacement surgery. The Kinect camera (Xbox One) is used to capture the movements of patients who are performing rehabilitation exercises with both lower limbs, specifically, ‘side step’ and ‘knee lift’ with each leg. The information is measured at 25 body points with their respective coordinates. Features selection algorithms are applied to the 75 attributes of the initial and final position vector of each rehab exercise. Different classification techniques have been tested and Bayesian networks, supervised classifier system and genetic algorithm with neural network have been selected and jointly applied to identify the correct and incorrect movements during the execution of the rehabilitation exercises. Besides, prediction models of the evolution of a patient are developed based on the average values of some motion related variables (opening leg angle, head movement, hip movement and execution speed). These models can help to fasten the recovery of these patients.

中文翻译：

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髋关节手术患者运动检测和身体进化的智能模型

摘要

本文开发了用于监测髋关节置换手术患者的计算模型。Kinect 摄像头 (Xbox One) 用于捕捉正在用双下肢进行康复锻炼的患者的动作，特别是每条腿的“侧步”和“提膝”。该信息是在 25 个身体点及其各自的坐标处测量的。特征选择算法应用于每个康复练习的初始和最终位置向量的 75 个属性。不同的分类技术已经过测试，贝叶斯网络、监督分类系统和带有神经网络的遗传算法已经被选择并联合应用，以识别康复练习中正确和错误的动作。除了，基于一些运动相关变量（张开腿角度、头部运动、臀部运动和执行速度）的平均值开发了患者演变的预测模型。这些模型可以帮助加快这些患者的康复。