Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
IEEE Trans Biomed Eng. 2012 Oct;59(10):2884-92. doi: 10.1109/TBME.2012.2212245. Epub 2012 Aug 8.
This paper presents a walking pattern classification and a walking distance estimation algorithm using gait phase information. A gait phase information retrieval algorithm was developed to analyze the duration of the phases in a gait cycle (i.e., stance, push-off, swing, and heel-strike phases). Based on the gait phase information, a decision tree based on the relations between gait phases was constructed for classifying three different walking patterns (level walking, walking upstairs, and walking downstairs). Gait phase information was also used for developing a walking distance estimation algorithm. The walking distance estimation algorithm consists of the processes of step count and step length estimation. The proposed walking pattern classification and walking distance estimation algorithm have been validated by a series of experiments. The accuracy of the proposed walking pattern classification was 98.87%, 95.45%, and 95.00% for level walking, walking upstairs, and walking downstairs, respectively. The accuracy of the proposed walking distance estimation algorithm was 96.42% over a walking distance.
本文提出了一种基于步态相位信息的行走模式分类和行走距离估计算法。开发了一种步态相位信息检索算法，以分析步态周期中各相位的持续时间（即站立、蹬离、摆动和脚跟触地相位）。基于步态相位信息，构建了一个基于步态相位关系的决策树，用于对三种不同的行走模式（水平行走、上楼梯行走和下楼梯行走）进行分类。还利用步态相位信息开发了一种行走距离估计算法。行走距离估计算法包括步数和步长估计过程。通过一系列实验验证了所提出的行走模式分类和行走距离估计算法。对于水平行走、上楼梯行走和下楼梯行走，所提出的行走模式分类的准确性分别为 98.87%、95.45%和 95.00%。所提出的行走距离估计算法的行走距离的准确性为 96.42%。
