基于人体骨骼关键点的心血管患者康复训练动作评估方法  

Action assessment method of rehabilitation training based onhuman skeleton key points for cardiovascular patients

作　　者：张睿泽 郭威 杨观赐[1,2] 罗可欣 李杨[1] 何玲[1] Zhang Ruize;Guo Wei;Yang Guanci;Luo Kexin;Li Yang;He Ling(Key Laboratory of Advanced Manufacturing Technology of Ministry of Education,Guizhou University,Guiyang 550025,China;State Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China;School of Mechanical Engineering,Guizhou University,Guiyang 550025,China;Guizhou Provincial Staff&Workers Hospital,Guiyang 550025,China)

机构地区：[1]贵州大学现代制造技术教育部重点实验室,贵阳550025 [2]贵州大学公共大数据国家重点实验室,贵阳550025 [3]贵州大学机械工程学院,贵阳550025 [4]贵州省职工医院,贵阳550025

出　　处：《计算机应用研究》2024年第8期2441-2447,共7页Application Research of Computers

基　　金：国家自然科学基金资助项目(62373116,62163007);贵州省科技计划资助项目(黔科合平台人才[2020]6007-2,黔科合支撑[2021]一般439,黔科合支撑[2023]一般117)。

摘　　要：为解决心血管患者日常康复训练依赖于康复中心专业医务人员现场指导的问题,围绕获得支撑心血管患者自主康复训练的动作评估系统,提出了基于人体骨骼关键点的心血管患者康复训练动作评估方法(ASRT-PHS)。首先,根据心血管患者的康复训练动作规范拍摄构建了康复训练动作数据集;然后,引入基于深度学习的检测器和姿态估计器采集人体位置信息与人体关键点信息,并将提取结果输入到卷积神经网络中进行动作识别;接着,通过关节距离比值计算、关节角度阈值计算与标准动作判断,构建基于关节距离比值的动作切分模型和基于动作关节角度阈值的动作评估模型;最后,通过测试ASRT-PHS在不同关节角度阈值和不同动作识别方法下的动作切分与评估性能,得出了ASRT-PHS的最优工作参数。测试结果表明,ASRT-PHS在动作识别、动作切分与动作评估中的准确率分别达到92.78%、77.6%和87%。此外,真实心血管患者案例测试表明,原型系统的动作评估平均准确率为71.3%,为患者居家自主康复训练提供了可行的智能辅助系统。In order to solve the problem that the daily rehabilitation training of cardiovascular patients depends on directing by health care professions in rehabilitation centre,during assessing and correcting the movements system about cardiovascular patients’independent rehabilitation training at home,this paper proposed an action assessment method for cardiovascular patients’rehabilitation training based on key points of the human skeleton(ASRT-PHS).Firstly,this paper constructed a dataset for rehabilitation training actions using a camera and data augmentation in accordance with the specified rehabilitation trai-ning specification for cardiovascular patients.Secondly,this paper employed a deep learning-based detector and pose estimator to capture human body positions and extract key points of the human skeleton,respectively,and then input the results into a convolutional neural network for action recognition.Thirdly,by calculating joint angle thresholds,joint distance ratio and assessing standard motions,this paper constructed a motion segmentation model based on joint distance ratios and an action assessment model based on action joint angle thresholds.This paper investigated the optimal combination of ASRT-PHS by assessing its performance with various joint angle thresholds and action recognition approaches.The results show that ASRT-PHS achieves an average action recognition,segmentation and assessment accuracy of 92.78%,77.6%and 87%,respectively.Furthermore,case tests about the true cardiovascular patients show that the average accuracy of the prototype system is 71.3%,which provides a feasible intelligent auxiliary system for patients’autonomous rehabilitation training at home.

关 键 词：人体关键点 康复训练 心血管患者 动作评估 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]