基于多尺度熵和动态时间规整的步态身份识别

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。

New method for gait recognition on combinability of multi-scale entropy and dynamic time warping algorithm

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) is 13.7%, which provides a new idea for gait recognition.