摘要
设计一款人体颈腰部生理曲线测量装置并对采集到的生理曲线数据进行分析。数据采集装置主要是基于飞行时间(time-of-flight,TOF)测距原理,分为曲线测量装置、数据采集装置、计算机曲线拟合等三个模块。数据分析部分包括基于SPSS 22平台的单因素分析、回归分析,基于Python的K近邻算法分类识别。对受试者的身高、体重、年龄和矢状面垂直距离(SVA)做Pearson相关性分析,对性别和SVA做t检验。对身高、体重、年龄和矢状面垂直距离(SVA)做回归分析。选身高、体重、年龄和矢状面垂直距离(SVA)作为参考指标,将年龄作为标记对象(青少年、成人、老年人)对曲线做K近邻算法分类处理。身高、体重、年龄和矢状面垂直距离(SVA)具有相关性(P<0.05);性别和SVA值不相关(P>0.05)。用K近邻算法可以较好对曲线做分类,识别准确率为74.93%。
To design a device for measuring the physiological curve of human neck,waist and analyze the collected physiological curve data.Data acquisition device was mainly based on the principle of time-of-flight(TOF)flight time ranging.It was divided into three modules:curve measurement device,data acquisition device and computer curve fitting module.The data analysis part was divided into single factor analysis based on SPSS 22 platform,regression analysis,K-nearest neighbor algorithm(KNN)classification and recognition based on python.Pearson correlation analysis was performed for height,weight,age and SVA,and T test for gender and SVA.Regression analysis was made on height,weight,age and sagittal vertical axis(SVA).Height,weight,age and SVA were selected as reference indexes.Age was used as marker objects(adolescents,adults and the elderly)to classify the curve by KNN.Height、weight、age and SVA were correlated(P<0.05),while gender and SVA were not correlated(P>0.05).KNN can be used to classify curves,and the recognition accuracy is 74.93%.
作者
孙琦
严荣国
江容安
刘亚萍
SUN Qi;YAN Rongguo;JIANG Rongan;LIU Yaping(School of Medical Instrument and Food Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Department of Rehabilitation Medicine,Yangpu District Central Hospital,Shanghai 200082)
出处
《生物医学工程研究》
2019年第4期461-465,共5页
Journal Of Biomedical Engineering Research
关键词
颈腰椎曲线
飞行时间测距
单因素分析
回归分析
K近邻算法
Curve of neck and waist
Time-of-flight laser ranging
Single factor analysis
Regression analysis
K-nearest neighbor algorithm
