摘要
目的建立下肢运动功能评估的算法。方法2016年8月至2017年3月,40例受试者分为年轻健康组(n=20)、中年组(n=10)和老年组(n=10)。采集受试者的步态视频、膝关节角度和地面反作用力,并利用ViBe算法对步态视频进行步态轮廓提取;利用Xception-LSTM网络提取步态图像特征,并与膝关节角度和地面反作用力在特征层进行融合;将融合特征经核主成分分析降维处理,生成步行能力评分(GAS),并对GAS和威斯康辛步态量表(WGS)评分进行相关性分析。结果中年组和老年组的GAS均明显低于年轻健康组(t>4.164,P<0.01),且老年组明显低于中年组(t=7.338,P<0.01)。GAS与WGS评分呈负相关(r=-0.91,P<0.01)。结论GAS能够量化评估下肢运动能力,可帮助制定康复方案,适配助行设备。
Objective To establish an algorithm to quantitatively evaluate the lower limb motor ability.Methods From August,2016 to March,2017,40 subjects were divided into young healthy group(n=20),middle-aged group(n=10)and elderly group(n=10).The gait video,knee angle and ground reaction force of the subjects were collected,and the gait contour was extracted from the gait video by using the ViBe algorithm.The gait image feature was extracted by Xception-LSTM,and fused it with the knee joint angle and the ground reaction force in the feature layer.The fusion features were reduced in dimension by kernel principal component analysis,and the gait ability score(GAS)was established.All the subjects were assessed with Wisconsin Gait Scale(WGS).Results GAS was less in middle-aged group and elderly group than in the young healthy group(t>4.164,P<0.01),and was less in the elderly group than in the middle-aged group(t=7.338,P<0.01).GAS was negative correlated with the score of WGS(r=-0.91,P<0.01).Conclusion The lower limb exercise ability could be quantified with GAS,which may be applied in developing rehabilitation and fitting walking aids.
作者
张燕
王铭玥
王婕
姜恺宁
张筠晗
ZHANG Yan;WANG Ming-yue;WANG Jie;JIANG Kai-ning;ZHANG Jun-han(School of Artificial Intelligence and Data Science,Hebei University of Technology,Tianjin 300130,China;International College,Zhengzhou University,Zhengzhou,Henan 450001,China)
出处
《中国康复理论与实践》
CSCD
北大核心
2020年第6期643-647,共5页
Chinese Journal of Rehabilitation Theory and Practice
基金
国家自然科学基金项目(No.61773151)
河北省自然科学基金项目(No.F2018202279)。
关键词
老年人
下肢
运动能力
迁移学习
卷积神经网络
循环神经网络
特征提取
elderly
lower limb
motor ability
transfer learning
convolutional neural network
recurrent neural network
feature extraction
