基于智能手机内置传感器的人体运动状态识别

殷晓玲; 陈晓江; 夏启寿; 何娟; 张鹏艳; 陈峰

doi:10.11959/j.issn.1000-436x.2019057

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基于智能手机内置传感器的人体运动状态识别

学术论文 | 更新时间：2024-06-05

- 基于智能手机内置传感器的人体运动状态识别
- Human motion state recognition based on smart phone built-in sensor
- 通信学报 2019年40卷第3期页码：157-169
- 作者机构：
  
  1. 西北大学信息科学与技术学院，陕西西安 710127
  2. 池州学院数学与计算机学院，安徽池州 247000
- 作者简介：
  
  [ "殷晓玲（1975- ），女，安徽枞阳人，池州学院副教授，主要研究方向为云计算、信息安全、机器学习。" ]
  [ "陈晓江（1972- ），男，陕西西安人，博士，西北大学教授、博士生导师，主要研究方向为无线传感器网络定位、网络安全、软件体系结构。" ]
  [ "夏启寿（1975- ），男，安徽庐江人，池州学院副教授，主要研究方向为云计算、信息安全、机器学习。" ]
  [ "何娟（1994- ），女，江西萍乡人，西北大学硕士生，主要研究方向为无线传感器网络定位。" ]
  [ "张鹏艳（1990- ）女，陕西西安人，西北大学硕士生，主要研究方向为无线传感器网络定位。" ]
  [ "陈峰（1978- ），男，安徽天长人，博士，西北大学助理研究员，主要研究方向为无线网络。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61170218);国家自然科学基金资助项目(61272461);国家自然科学基金资助项目(61373177)
- DOI：10.11959/j.issn.1000-436x.2019057
  中图分类号： TP181
- 网络出版日期：2019-03，
  
  纸质出版日期：2019-03-25
- 稿件说明：
移动端阅览
殷晓玲, 陈晓江, 夏启寿, 等. 基于智能手机内置传感器的人体运动状态识别[J]. 通信学报, 2019,40(3):157-169.

Xiaoling YIN, Xiaojiang CHEN, Qishou XIA, et al. Human motion state recognition based on smart phone built-in sensor[J]. Journal on communications, 2019, 40(3): 157-169.
殷晓玲, 陈晓江, 夏启寿, 等. 基于智能手机内置传感器的人体运动状态识别[J]. 通信学报, 2019,40(3):157-169. DOI： 10.11959/j.issn.1000-436x.2019057.

Xiaoling YIN, Xiaojiang CHEN, Qishou XIA, et al. Human motion state recognition based on smart phone built-in sensor[J]. Journal on communications, 2019, 40(3): 157-169. DOI： 10.11959/j.issn.1000-436x.2019057.

摘要

针对目前智能手机识别人体运动状态种类少、准确率低的问题，提出一种利用加速度传感器和重力传感器分层识别人体运动状态的方案。首先，利用加速度和重力加速度的关系计算出与手机方向无关的惯性坐标系下的线性加速度；其次，根据人体运动频率的变化范围和线性加速度矢量来确定脚步的波峰和波谷位置；最后，提取线性加速度在时域上的特征向量，使用层次支持向量机方法分层识别人体运动状态。实验结果表明，该方法能有效识别人体6种日常运动状态，准确率达到93.37%。

Abstract

To solve problems of low accuracy and fewer types of human motion state recognized by current smart phones

a method to do hierarchical recognition by using acceleration sensors and gravity sensors was proposed.Firstly

linear acceleration in inertial coordinate system and independent of phone direction was calculated by using the relation between acceleration and gravity acceleration.Secondly

according to the span of human motion frequency and linear acceleration vector

positions of peak and trough of footsteps were determined.Finally

feature vector of linear acceleration in time domain was extracted and human motion states were recognized hierarchically by using hierarchical support vector machine (H-SVM).The experiment shows the method can recognize six usual human motion states

while accuracy rate up to 93.37%.

关键词

Keywords

references

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