基于足底压力分布时空HOG特征的步态识别方法  

Gait Recognition Based on Spatio-Temporal HOG Feature of Plantar Pressure Distribution

作　　者：夏懿[1,2] 马祖长[2] 姚志明 孙怡宁[2] 

机构地区：[1]中国科学技术大学信息科学技术学院,合肥230026 [2]中国科学院合肥智能机械研究所运动健康与信息技术研究中心,合肥230031

出　　处：《模式识别与人工智能》2013年第6期529-536,共8页Pattern Recognition and Artificial Intelligence

基　　金：国家科技支撑计划项目(No.2013BAH14F01);安徽省自然科学基金项目(No.1208085MF112)资助

摘　　要：提出一种基于足底压力分布时空HOG的步态识别算法,在特征层对足底压力的时间域和空间域信息进行融合.首先寻找足底总压力时间曲线上的极大值和极小值等几个特征点,利用这几个特征点所对应时刻的足底压力分布来构建时空HOG特征向量,最后采用SVM进行步态识别.采集不同行走速度下30人的单步足底压力分布数据进行实验,在不区分样本速度的情况下,该方法的识别率为93.5%.实验结果表明足底压力分布时空HOG特征能较好地刻画步态动力学特征,且具有良好的速度适应性.A gait recognition method proposed. Spatio-temporal HOG based on spatio-temporal histogram of embodies the feature fusion of spatial oriented gradient （ HOG ） is and temporal plantar pressure information. Firstly, several key points, such as the maximum and the minimum points on the pressure-time curve, are picked out. Then, plantar pressure distribution images corresponding to the moment of key points are used to construct spatio-temporal HOG feature vector. Finally, support vector machine classification is applied to implement gait classification. Gait samples are collected from 30 persons at different walking speeds. When the walking speeds of the samples in the training and testing sets are not differentiated, the recognition rate is 93.5%. The experimental results demonstrate that spatio-temporal HOG feature accurately describes the dynamic plantar pressure distribution during walking, and it also has good speed-adaptable properties.

关 键 词：生物特征识别技术 步态识别 足底压力分布 时空方向梯度直方图 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]