摘要
针对日常生活中人体的运动具有准周期特性的特点,设计了一种基于加速度信息与脚底压力的下肢运动信息采集系统。通过采集脚底压力信息监测关键步态事件,在事件前后对下肢加速度数据进行特征提取,从而对4种常见行为(走路、跑步、上楼和下楼)进行识别。实验结果表明:提出的方法在仅提取较为简单的时域特征信息情况下,平均识别正确率为94. 79%。
Aiming at characteristic of quasi-periodic that motion of human body has in daily life,a motion information collection system of lower limbs which is used for collecting acceleration and foot bottom pressure is designed. Acceleration data features are extracted prior to and immediately after gait events to recognize four motion modes including walking,running,stair ascent,stair descent. Experimental result shows that the average recognition accuracy is 94. 79 %,while only extracting simple time-domain features information classification.
作者
张乾勇
张涛
赵治羽
ZHANG Qian-yong;ZHANG Tao;ZHAO Zhi-yu(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 611756,China)
出处
《传感器与微系统》
CSCD
2019年第2期73-76,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金重点资助项目(61433015
61433011)
国家自然科学基金青年科学基金资助项目(61603316)
关键词
行为识别
步态事件检测
Relief特征选取
线性判别分析分类器
外骨骼
motion recognition
gait events detection
Relief feature selection
linear discriminant analysis (LDA)classifier
exoskeleton