在行人步态分类研究领域中,传统的基于微机电系统(micro-electro-mechanical system,MEMS)惯性传感器技术的步态分类方法侧重于对行人单一步态模式进行区分,忽略了两个单一步态模式之间的过渡步态模式,从而降低了行人行走过程中走、跑、停等混合步态的分类精度,还会在时间上造成缺失,进而造成行人航迹推算产生不可估量的定位误差.从人体运动学角度出发分析了行人步态特点,同时利用9轴MEMS惯性传感器采集了行人步态原始数据并对其进行剖析,设定了人体三维惯性传感参数,以供后续分类算法使用.为了进一步提高整体混合步态的分类精度,针对朴素贝叶斯算法对相反过渡步态模式区分精度不高的问题,在其基础上通过加窗判断前后两个步态的连续性,完成了行人混合步态的最终分类.验证结果表明,和传统的样本熵与小波能量相结合方法相比,提出的三维惯性传感参数表征下的行人混合步态分类方法,不仅能区分出行人混合步态中的多种单一步态模式和多种过渡步态模式,同时整体分类精度提高了14.46％,从而有效证明了该方法在行人步态分类领域具有良好的理论价值和应用价值.%In pedestrian gait classification research, the traditional method based on the micro-electro-mechanical system (MEMS) inertial sensor technique focuses on distinguish-ing a single pedestrian gait, and ignoring transition gait between two single gaits. It leads to poor classification accuracy of mixed gaits such as walking, running, halting, and even causes loss of time. As a result, positioning error of pedestrian dead reckoning becomes large. This paper analyzes gait characteristics based on kinesiology, and collects raw data of pedestrian using a 9-axis MEMS sensor. 3D inertial sensor parameters are then selected to be applied to the subsequent classification algorithm. Because the Naive Bayes algorithm has low accuracy to distinguish reverse transition gaits, the improved algorithm based on the Naive Bayes algorithm judges continuity of two adjacent windows to realize mixed gait classification. Experimental results show that the proposed mixed pedestrian gait classification method with 3D inertial sensor parameters characterization can distin-guish a variety of single gaits and transition gaits from mixed gaits. It can also improve the overall classification accuracy by 14.46％ as compared with the method of combining sample entropy and wavelet energy. Therefore, the proposed method has a good theoretical and practical value in gait classification.
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