1. 山西大学计算机与信息技术学院,山西,太原,030006
2. 清华大学自动化系,北京,100084
3. 山西大学计算机与信息技术学院山西太原,030006
4. 清华大学自动化系北京,100084
纸质出版:2009
移动端阅览
李月香, 刘 燕, 袁 涛, 等. 基于加速度信号的走路模式多级分类算法[J]. 电子学报, 2009,37(8):1794-1798.
LI Yue-xiang, LIU Yan, YUAN Tao, et al. Multiple Classifier Based Walking Pattern Recognizing Algorithm Using Acceleration Signals[J]. Acta Electronica Sinica, 2009, 37(8): 1794-1798.
研究了一种基于多级分类模型的非特定人走路模式识别算法
实现了对水平行走和上、下楼梯三种运动状态的识别.将装有微型加速度传感器的无线数据采集装置固定于人体后腰部
获取运动时的三维步态加速度信号.采用离散小波变换提取与运动相关频带的时频特征
并结合步频以及垂直方向和前进方向加速度信号之间的互相关性
经过特征融合设计了多级分类识别算法.通过对10个人共360组数据的测试结果表明:在步频范围扩大到1~3Hz时
识别率达到了96.1%
且对测试对象的依赖性小.
This paper presented the multiple classifier based walking pattern recognition algorithm
which could identify three walking patterns:horizontal walking
up and down staircase walking.Three-dimensional accelerations during walking were acquired from the wireless accelerometer device fixed on the back waist.The discrete wavelet transformation was applied for time-frequency analysis.The time-frequency features associated with the main frequency band of the motion
walking cadence and the correlation between the vertical and forward acceleration signals were combined to design a multiple classifier.A set of 360 gait samples involving 10 people were used for test
giving an overall recognition accuracy for 96.1% when the walking cadence range was within 1~3Hz
and this algorithm was less dependent on individuals.
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