基于室内定位技术的人体姿态识别方法

doi:10.3969/j.issn.0253-2778.2019.10.005

中国科学技术大学学报 ›› 2019, Vol. 49 ›› Issue (10): 805-811.DOI: 10.3969/j.issn.0253-2778.2019.10.005

基于室内定位技术的人体姿态识别方法

黄小平，张健，胡泽林，李淼，曾伟辉，李华龙

中国科学院合肥智能机械研究所，安徽合肥 230031

收稿日期:2018-10-22 修回日期:2019-05-27 接受日期:2019-05-27 出版日期:2019-10-31 发布日期:2019-05-27
通讯作者: 张建
作者简介:黄小平，1984年生，博士生/讲师.研究方向：模式识别.E-mail: hxping@mail.ustc.edu.cn
基金资助:
科技部重点研发计划(2017YFD0701603)，国家自然科学基金(31401285) ，安徽省教育厅自然科学基金(KJ2016A305), 安徽省教育厅高等学校省级质量工程项目(2017jyxm0531)资助.

Human posture recognition method based on indoor positioning technology

HUANG Xiaoping, ZHANG Jian, HU Zelin, LI Miao, ZENG Weihui, LI Hualong

Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China

Received:2018-10-22 Revised:2019-05-27 Accepted:2019-05-27 Online:2019-10-31 Published:2019-05-27

摘要/Abstract

摘要： 独居老人摔倒等姿态检测是当今备受关注的问题.基于机器视觉的方法存在隐私侵入，成本高和实现过程复杂等问题，而基于加速度传感的方法对静止姿态识别存在困难.为此提出一种基于室内定位技术的老人姿态检测方案.首先在人体关键节点安装可穿戴接收标签，然后采用超宽带UWB测距方法，实现人体关键部位的定位和跟踪.在姿态估计算法中，分别采用最小二乘和改进的扩展卡尔曼滤波算法来抑制噪声，提高定位精度.仿真实验表明，改进的扩展卡尔曼滤波算法误差较小，可以较好地识别老人摔倒等姿态信息.

关键词: 室内定位, 姿态识别, 老人监护, 无线体域网, 卡尔曼滤波

Abstract: Solitary elderly person posture recognition, especially when falling down, is a problem of concern today. The traditional method based on machine vision is flawed with too much privacy invasion, high cost and complex factors such as the implementation process, while the method based on acceleration sensor has a lower recognition rate in the stillness of the gesture. This paper introduces a new kind of body posture recognition scheme that employs indoor positioning technologies. The main job is to build an indoor positioning system, and paste tags to the key parts of the clothes and hat. The tags can receive ultra-wideband (UWB) signal from the positioning system. The UWB signal is used to get the distance which is important for the positioning. Finally, body posture can be easily recognized. In gesture recognition algorithm, this paper USES the least squares and the improved extend Kalman filter to suppress the noise of the distances measurement, so as to improve the accuracy of location. The simulation algorithm shows that the improved extend Kalman filter is effective.

Key words: indoor position, posture recognition, elderly monitoring system, wireless body area network, Kalman filter

中图分类号:

黄小平，张健，胡泽林，李淼，曾伟辉，李华龙. 基于室内定位技术的人体姿态识别方法[J]. 中国科学技术大学学报, 2019, 49(10): 805-811.

HUANG Xiaoping, ZHANG Jian, HU Zelin, LI Miao, ZENG Weihui, LI Hualong. Human posture recognition method based on indoor positioning technology[J]. Journal of University of Science and Technology of China, 2019, 49(10): 805-811.

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基于室内定位技术的人体姿态识别方法

Human posture recognition method based on indoor positioning technology

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