基于多尺度熵和动态时间规整的步态身份识别
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摘要
针对现有人体步态身份识别算法单一、准确率较低的问题,提出了一种基于多尺度熵和动态时间规整(DTW,dynamic time warping)的人体步态身份识别方法。采用自制的APP软件在较低采样率下采集人体步行加速度数据,实验中共采集50名志愿者的正常行走加速度数据,使用多尺度熵算法进行数据处理,得到在各个尺度下的熵值,最后采用DTW算法对多尺度熵值进行特征匹配,得到的相对错误率(EER,equal error rate)为13.7%,仿真结果表明基于多尺度熵和DTW算法相结合的方法较好提高了身份识别的准确率,为人体步态身份识别提供了一个新的思路。
In order to solve the problem of low accuracy of existing gait recognition,a method of gait recognition based on multi-scale entropy and DTW(dynamic time warping)algorithm is proposed.The new method for gait recognition adopts the camera and the high sampling rate sensor,and the acceleration sensor of the mobile phone is adopted to collect the data.The sensor works at the low sampling rate,and the normal walking acceleration data of 50 volunteers are collected and processed by multi-scale entropy,from which the entropy values at various scales are obtained.Finally,the DTW algorithm is used to match the multi-scale entropy.The simulation results show that the method based on the combination of multi-scale entropy and DTW improves the accuracy of identity recognition,and the EER(Equal Error Rate)is13.7%,which provides a new idea for gait recognition.
In order to solve the problem of low accuracy of existing gait recognition,a method of gait recognition based on multi-scale entropy and DTW(dynamic time warping)algorithm is proposed.The new method for gait recognition adopts the camera and the high sampling rate sensor,and the acceleration sensor of the mobile phone is adopted to collect the data.The sensor works at the low sampling rate,and the normal walking acceleration data of 50 volunteers are collected and processed by multi-scale entropy,from which the entropy values at various scales are obtained.Finally,the DTW algorithm is used to match the multi-scale entropy.The simulation results show that the method based on the combination of multi-scale entropy and DTW improves the accuracy of identity recognition,and the EER(Equal Error Rate)is13.7%,which provides a new idea for gait recognition.
引文
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[3]Hoang T.Adaptive cross-device gait recognition using a mobile accelerometer[J].Journal of Information Processing Systems,2013,9(2):333-348.
[4]陈国良,张言哲,杨洲.一种基于手机传感器自相关分析的计步器实现方法[J].中国惯性技术学报,2014,22(6):794-798.CHEN Guoliang,ZHANG Yanzhe,YANG Zhou.Realization of pedometer with auto-correlation analysis based on mobile phone sensor[J].Journal of Chinese Inertial Technology,2014,22(6):794-798.(in Chinese)
[5]衡霞,王忠民.基于手机加速度传感器的人体行为识别[J].西安邮电大学学报,2014,19(6):76-79.HENG Xia,Wang Zhongmin.Human activity recognition based on accelerometer data from a mobile phone[J].Journal of University of Posts and Telecommunications,2014,19(6):76-79.(in Chinese)
[6]Nickel C,Derawi M O,Bours P,et al.Scenario test of accelerometer-based biometric gait recognition[C]∥Third International Workshop on Security and Communication Networks,May 18,2011.Gjvik,Norway:IEEE,2014:15-21.
[7]Thang H M,Viet V Q,Thuc N D,et al.Gait identification using accelerometer on mobile phone[C]∥International Conference on Control,Automation and Information Sciences.Nov 25-28,2013.Nha Trang City,Vietnam:IEEE,2013:344-348.
[8]Costa M,Goldberger A L,Peng C K.Multiscale entropy analysis of biological signals[J].Physical Review E Statistical Nonlinear&Soft Matter Physics,2005,71(2):21-29.
[9]王俊,宁新宝,马千里.基于多尺度熵的心电图分析[J].中国生物医学工程学报,2008,27(3):331-334.WANG Jun,NING Xinbao.MA Qianli.Multiscale entropy based electrocardiogram analysis[J].Chinese Journal of Biomedical Engineering,2008,27(3):331-334.(in Chinese)
[10]Costa M,Peng C K,Goldberger A L,et al.Multiscale entropy analysis of human gait dynamics[J].Physica a Statistical Mechanics&Its Applications,2003,330(1):53-60.
[11]刘晓婷.基于加速度的人体步态信息多尺度熵研究[D].天津:天津大学,2008.LIU Xiaoting.Multiscale entropy analysis of body gait based on acceleration[D].Tianjin:Tianjin University,2008.(in Chinese)
[12]谷丽华,崔畅,高松巍,等.基于MPU-6050的步态信号采集系统[J].沈阳工业大学学报,2015,37(2):176-182.GU Lihua,CUI Chang,GAO Songwei,et al,Gait signal acquisition system based on MPU-6050[J].Journal of Shenyang University of Technology,2015,37(2):176-182.(in Chinese)
[13]Krzeszowski T,Switonski A,Kwolek B,et al.DTW-Based gait recognition from recovered 3-d joint angles and interankle distance[M].USA:Springer International Publishing,2014:356-363.
[14]Derawi M O,Nickel C,Bours P,et al.Unobtrusive user-authentication on mobile phones using biometric gait recognition[C]∥Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing,Oct15-17,2010.Darmstadt,Germany:IEEE,2010:306-311.