北京理工大学机电学院,北京,100081
纸质出版:2015
移动端阅览
李鹏斐, 李孟君, 陈曦, 等. 基于静电信号的人体步伐周期长程相关性研究[J]. 电子学报, 2015,43(6):1078-1083.
LI Peng-fei, LI Meng-jun, CHEN Xi, et al. Research on Long Range Characteristics of Human Gait Cycle Based on Electrostatic Signals[J]. Acta Electronica Sinica, 2015, 43(6): 1078-1083.
李鹏斐, 李孟君, 陈曦, 等. 基于静电信号的人体步伐周期长程相关性研究[J]. 电子学报, 2015,43(6):1078-1083. DOI: 10.3969/j.issn.0372-2112.2015.06.006.
LI Peng-fei, LI Meng-jun, CHEN Xi, et al. Research on Long Range Characteristics of Human Gait Cycle Based on Electrostatic Signals[J]. Acta Electronica Sinica, 2015, 43(6): 1078-1083. DOI: 10.3969/j.issn.0372-2112.2015.06.006.
人的步伐信号包含着人体身体状态和健康状况等多种重要信息
因此受到越来越多的重视和研究.本文利用人体携带大量电荷这一特性
通过静电探测器对人体踏步过程中的步伐静电信号进行采集
研究人体步伐在时间尺度上的变化规律.论文提出一种自相关算法滤除信号中的噪声和干扰
通过相关系数确定步伐中的同相位点
从而获得精确步伐周期值.通过对步伐周期序列进行分解
得到步伐周期增量绝对值和变化符号两个序列
运用消除趋势波动分析对原始步伐周期序列及分解后的两个新序列进行分析
得到其长程相关性规律.通过对于实验所采集的多名测试对象的数据进行分析
发现对于所有被测人员
其步伐周期的增量绝对值序列均呈现出较强的持续正相关
而其周期变化符号呈现出明显的反相关特性.
A variety researches have been done on gait signal in recent years
which contains abundant information of human physical condition and health status.We measured electrostatic gait signal of 6 young men by a novel electrostatic detector instead of accelerometer.The noise was filtered out using self-correlation algorithm to sink the same phase point in every gait and to obtain accurate gait cycle series.The gait cycle series were decomposed into two component series
magnitude series (absolute value of gait cycle increment) and sign series.The detrended fluctuation analysis algorithm was applied to analyze the original gait cycle series and two component series.It is found that the gait cycle series appears positive long range correlation.For the two component series
the magnitude series show strongly positive long range correlation
while the sign series show obviously long range anti-correlation characteristics for all the tested objects.
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