Walking Pattern Classification and Walking Distance Estimation Algorithms Using Gait Phase Information
Walking Pattern Classification and Walking Distance Estimation Algorithms Using Gait Phase Information
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DOI：
10.1109/tbme.2012.2212245
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发表时间：
2012-10-01
影响因子：
4.6
通讯作者：
Ho, Yu-Jen
中科院分区：
文献类型：
--
作者：
Wang, Jeen-Shing;Lin, Che-Wei;Ho, Yu-Jen
提出了一种基于步态相位信息的步行模式分类和步行距离估计算法。开发了一种步态相位信息检索算法来分析步态周期中相位的持续时间（即，站立、蹬离、摆动和脚跟着地阶段）。基于步态相位信息，构建了基于步态相位之间关系的决策树，用于分类3种不同的步行模式（水平步行、上楼步行和下楼步行）。步态相位信息也被用于开发步行距离估计算法。步行距离估计算法由步数计算和步长估计过程组成。通过一系列实验验证了所提出的步行模式分类和步行距离估计算法的有效性。水平行走、上楼行走和下楼行走的步行模式分类准确率分别为98.87%、95.45%和95.00%。所提出的步行距离估计算法的准确率为96.42%以上的步行距离。
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.