基于震动信号的异常步态识别

汪成亮; 刘艺锣

doi:10.12263/DZXB.20210871

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基于震动信号的异常步态识别

学术论文 | 更新时间：2025-12-08

- 基于震动信号的异常步态识别
- Recognition of Abnormal Gait Based on Vibration Signal
- 电子学报 2023年51卷第8期页码：2088-2097
- 作者机构：
  
  重庆大学计算机学院，重庆 400044
- 作者简介：
  
  [ "汪成亮男，1975年5月生，四川资阳人.博士.现为重庆大学计算机学院教授，博士生导师.主要研究领域为复杂系统智能控制、无线网络及RFID研究与应用等." ]
  [ "刘艺锣男，1996年 11月生，重庆人 . 硕士 .主要研究方向为智能家居、震动信号分类处理. Email: lyldwangyi@163.com" ]
- 基金信息：
  
  国家自然科学基金(61672115);重庆市技术创新与应用发展专项重大主题专项(cstc2019jscx-zdztzxX0037;cstc2019jscx-gksbX0038)
- DOI：10.12263/DZXB.20210871
  中图分类号： TP391;
- 收稿：2021-07-09，
  
  修回：2021-10-29，
  
  纸质出版：2023-08-25
- 稿件说明：
移动端阅览
汪成亮,刘艺锣.基于震动信号的异常步态识别[J].电子学报,2023,51(08):2088-2097.

WANG Cheng-liang,LIU Yi-luo.Recognition of Abnormal Gait Based on Vibration Signal[J].ACTA ELECTRONICA SINICA,2023,51(08):2088-2097.
汪成亮,刘艺锣.基于震动信号的异常步态识别[J].电子学报,2023,51(08):2088-2097. DOI： 10.12263/DZXB.20210871.

WANG Cheng-liang,LIU Yi-luo.Recognition of Abnormal Gait Based on Vibration Signal[J].ACTA ELECTRONICA SINICA,2023,51(08):2088-2097. DOI： 10.12263/DZXB.20210871.

摘要

异常步态的识别对老年人的健康看护有很大帮助.现有的相关研究主要通过图像采集设备或穿戴设备获取相关特征信息进行识别，这些方法大多都具有侵入性或对用户有较高的操作要求.本文研究实现了一种基于对脚步震动信号为识别源进行异常步态和跌倒检测的系统原型，该系统通过本文设计的应用于大范围数据采集的多传感器协同信号采集方法采集信号，从中分割出有效部分作为活动元，去噪后再使用改进的动态时间规整算法（Dynamic Time Warping，DTW）计算出代表活动元之间差异性的异常指数，接着由K最近邻（K-Nearest Neighbor，KNN）算法分类异常指数，得到初步表征用户步态的推测值，最后由隐马尔科夫模型（Hidden Markov Model，HMM）进一步处理推测值，识别出用户的步态.实验结果表明，本文提出的方法能够有效在不同的步态模式下识别异常步态，在稳定的环境中识别准确率达到96%，在具有不稳定地板的环境中准确率为94%.

Abstract

The recognition of abnormal gait is of great help to the health care of the elderly. Existing related research mainly uses image acquisition equipment or wearable equipment to obtain relevant feature information for identification. Most of these methods are invasive or have high operational requirements for users. This paper studies and realizes a system prototype based on the detection of abnormal gait and fall based on foot vibration signals as the source of identification. This paper first designs a multi-sensor cooperative signal acquisition method to achieve a large range of signal acquisition

and separate the effective part from it as the active element. After the collected active elements are denoised

an improved dynamic time warping algorithm (DTW) is used to calculate the abnormal index representing the difference between active elements

then the abnormal index is classified by the K nearest neighbor (KNN) algorithm

and the inferred value that initially characterizes the user's gait is obtained. The inferred value is further processed by hidden Markov model (HMM) to identify the user's gait. The experimental results show that the method proposed in this paper can effectively identify abnormal gait in different gait modes

with an accuracy of 96% in a stable environment

and an accuracy of 94% in an environment with unstable floors.

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references

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