An algorithm for stance and swing phase detection of human gait cycle

H. Dasgupta
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引用次数: 2

Abstract

The main objective of this work is to make an algorithm to detect onset and offset of stance and swing phase of human gait cycle. In order to proceed, the signal from the force sensors (placed underneath each foot) were first divided according to the fundamental period by auto-correlation. This ensures that each period contains a full stance phase and two fragmented swing phases. After this step, Short Time Fourier transform has been performed on each period of the signal to detect the initial search point (highest DC value) on the stance phase. Then by judging the width and slope of the straight line from changing search points on the signal to a changing destination point (starting from the minimum or maximum time value) of the graph the onset and offset of the stance and swing phase has been detected. The changing search point has small distance from the previous one, if the slope of the signal is small, but the distance increases with the increasing change of slope of the signal. The destination point changes with the change of slope of the straight line. This algorithm has been applied to the signals in the database of Parkinsons disease, which shows a success rate of more than 90%.
人体步态周期的姿态和摆动相位检测算法
本工作的主要目的是提出一种检测人体步态周期的姿态和摆动相位的起始和偏移的算法。为了继续进行,首先通过自相关法根据基本周期对来自力传感器(放置在每只脚下面)的信号进行分割。这确保了每个周期包含一个完整的姿态阶段和两个分散的摇摆阶段。在此步骤之后,对信号的每个周期进行短时傅里叶变换,以检测姿态相位上的初始搜索点(最高直流值)。然后,通过判断从信号上搜索点的变化到图的目的点(从最小或最大时间值开始)的直线的宽度和斜率,检测出姿态和摆动相位的起始和偏移。当信号斜率较小时,变化搜索点与前一个搜索点的距离较小,但随着信号斜率变化的增大,变化搜索点与前一个搜索点的距离增大。终点随直线斜率的变化而变化。该算法已应用于帕金森病数据库中的信号,成功率达90%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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