Daily Activity Recognition Based on Triaxial Accelerometer of Elderly People

WANG Cheng-liang; WANG Xiao-jun

doi:10.3969/j.issn.0372-2112.2017.03.010

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Daily Activity Recognition Based on Triaxial Accelerometer of Elderly People

更新时间：2025-07-16

- Daily Activity Recognition Based on Triaxial Accelerometer of Elderly People
- Acta Electronica Sinica Vol. 45, Issue 3, Pages: 570-576(2017)
- 作者机构：
  
  1. 重庆大学信息物理社会可信服务计算教育部重点实验室,重庆,400044
  2. 重庆大学计算机学院,重庆,400044
  3. 重庆大学信息物理社会可信服务计算教育部重点实验室,重庆,400044
  4. 重庆大学计算机学院,重庆,400044
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61004112);Fundamental Research Funds for the Central Universities (No.CDJZR12180006)
- DOI：10.3969/j.issn.0372-2112.2017.03.010
  CLC： TP391.4
- Published Online：25 March 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
WANG Cheng-liang, WANG Xiao-jun. Daily Activity Recognition Based on Triaxial Accelerometer of Elderly People[J]. Acta Electronica Sinica, 2017, 45(3): 570-576.
DOI：

WANG Cheng-liang, WANG Xiao-jun. Daily Activity Recognition Based on Triaxial Accelerometer of Elderly People[J]. Acta Electronica Sinica, 2017, 45(3): 570-576. DOI： 10.3969/j.issn.0372-2112.2017.03.010.

摘要

本文针对老年人日常活动类型及特点提出了一种基于三轴加速度传感器和HMM（Hidden Markov Model）的活动识别方法.本文首先提取了针对老年人相异、相似活动的标准差、能量、相关系数、RAF（RAtio Forward）、RVF（Ratio Vertical Forward）等特征值.然后定义老年人的HMM活动识别模型.最后在经过Baum-Welch算法对HMM进行参数训练后使用Viterbi算法来进行老年人活动识别.实验结果表明，本文方法适用于老年人的日常活动的识别，平均识别精度达到了93.3%，尤其是对于相似步态活动的识别准确率达到了93.7%.

Abstract

In the light of the motion type and characteristics of elderly people

we propose an approach which is based on triaxial accelerometer and hidden Markov model(HMM) for activities recognition.Firstly

we extract standard deviation(SD)

energy

correlation coefficients

ratio forward(RAF)

ratio vertical forward(RVF) as the features corresponding to different and similar activities of elderly people.Secondly

we define the activities recognition model based on HMM for elderly people.Finally

we use the Viterbi algorithm to recognize the activities for elderly people after the parameters are trained by Baum-Welch algorithm.The experimental results shows that our approach is can be applied for daily activity recognition of elderly people and the average recognition accuracy is 93.3%

specifically the accuracy of similar walking activities is 93.7%.

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