基于MEMS加速度传感器的步态识别

针对最小采集约束条件和经历长时间跨度下识别率低的问题,提出一种基于MEMS加速度传感器的步态识别算法。该算法以右髋部位置采集加速度信号构造多个高斯差分尺度空间,利用局部关键点生成稀疏表示的步态特征位置模板,并采用模板融合来有效转换稀疏性步态周期特征,最后利用最近邻算法和投票机制对步态特征进行识别。在公开的含175名测试者的步态加速度数据集上进行测试,实验结果显示识别率为98.67%和认证率为99.89%,并进一步研究了测试集和训练集样本数目对识别效果的影响,验证了特征提取的有效性和稳定性。

Gait recognition based on MEMS acceleration sensor

The conventional gait recognition algorithm basing on acceleration signal to extract gait features has low recognition rate when with minimal constraint conditions or relatively long time span.To solve this problem,a novel gait recognition algorithm based on MEMS acceleration sensor is proposed,in which the acceleration signals are collected at right-side half-pelvis to construct various DoG (difference of Gaussian) scale-spaces.The location information template of the gait features by sparse representation is built,and the gait cycle features based on sparse representation is effectively converted according to the fusion of gait templates.The gait features are recognized by the nearest neighbor approach and the voting scheme.Experimental results demonstrate that the proposed algorithm significantly outperforms other methods.Based on open access datasets of 175 volunteers,the recognition rate of 98.67％ and the verification of 99.89％ are obtained.Furthermore,the influence on the recognition effect by different composition of training samples and testing samples is further studied,which indicates the stability and effectiveness of the feature extraction by the proposed method.