Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2022 Apr 7;22(8):2840. doi: 10.3390/s22082840.
Accurate and reliable stride length estimation modules play a significant role in Pedestrian Dead Reckoning (PDR) systems, but the accuracy of stride length calculation suffers from individual differences. This paper presents a stride length prediction strategy for PDR systems that can be adapted across individuals and broad walking velocity fields. It consists of a multi-gait division algorithm, which can divide a full stride into push-off, swing, heel-strike, and stance based on multi-axis IMU data. Additionally, based on the acquired gait phases, the correlation between multiple features of distinct gait phases and the stride length is analyzed, and multi regression models are merged to output the stride length value. In experimental tests, the gait segmentation algorithm provided gait phases division with the F-score of 0.811, 0.748, 0.805, and 0.819 for stance, push-off, swing, heel-strike, respectively, and IoU of 0.482, 0.69, 0.509 for push-off, swing, heel-strike, respectively. The root means square error (RMSE) of our proposed stride length estimation was 151.933, and the relative error for total distance in varying walking speed tests was less than 2%. The experimental results validated that our proposed gait phase segmentation algorithm can accurately recognize gait phases for individuals with wide walking speed ranges. With no need for parameter modification, the stride length method based on the fusion of multiple predictions from different gait phases can provide better accuracy than the estimations based on the full stride.
准确可靠的步长估计模块在行人航位推算 (PDR) 系统中起着重要作用，但步长计算的准确性受到个体差异的影响。本文提出了一种适用于个体和广泛步行速度范围的 PDR 系统步长预测策略。它由一个多步态划分算法组成，该算法可以根据多轴 IMU 数据将整个步态分为蹬伸、摆动、脚跟触地和支撑阶段。此外，基于所获得的步态阶段，分析了不同步态阶段的多个特征与步长之间的相关性，并合并了多元回归模型以输出步长值。在实验测试中，步态分割算法提供的步态阶段划分的 F 分数分别为 0.811、0.748、0.805 和 0.819，支撑、蹬伸、摆动和脚跟触地的 IoU 分别为 0.482、0.69、0.509。我们提出的步长估计的均方根误差 (RMSE) 为 151.933，在不同步行速度测试中总距离的相对误差小于 2%。实验结果验证了我们提出的步态阶段分割算法可以准确识别个体的步态阶段，适应广泛的步行速度范围。无需参数修改，基于不同步态阶段的多个预测融合的步长方法比基于整个步态的估计具有更高的准确性。
