[1]黄文权,王婉华,陈冰莹.利用少量体压传感器和支持向量机算法的坐姿识别方法[J].华侨大学学报(自然科学版),2022,43(2):168-175.[doi:10.11830/ISSN.1000-5013.202108023]
　HUANG Wenquan,WANG Wanhua,CHEN Bingying.Sitting Posture Recognition Method Using Small Number of Pressure Sensors and Support Vector Machine Algorithm[J].Journal of Huaqiao University(Natural Science),2022,43(2):168-175.[doi:10.11830/ISSN.1000-5013.202108023]
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利用少量体压传感器和支持向量机算法的坐姿识别方法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第43卷
期数:
2022年第2期
页码:
168-175
栏目:
出版日期:
2022-03-08

文章信息/Info

Title:
Sitting Posture Recognition Method Using Small Number of Pressure Sensors and Support Vector Machine Algorithm
文章编号:
1000-5013(2022)02-0168-08
作者:
黄文权1;  王婉华2;  陈冰莹1
1. 华侨大学 信息科学与工程学院, 福建 厦门 361021;2. 厦门大学附属第一医院, 福建 厦门 361021
Author(s):
HUANG Wenquan1;  WANG Wanhua2;  CHEN Bingying1
1. College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China; 2. The First Affiliated Hospital of Xiamen University, Xiamen 361021, China
关键词:
坐姿识别;  薄膜压力传感器;  体压分布;  等高线图;  支持向量机
Keywords:
sitting posture recognition;  thin film pressure sensor;  body pressure distribution;  contour map;  support vector machine
分类号:
TP391.41;TP212.9
DOI:
10.11830/ISSN.1000-5013.202108023
文献标志码:
A
摘要:
针对传统坐姿识别系统中传感器数量多和系统较复杂导致成本过高等问题,设计一种基于少量体压传感器和支持向量机(SVM)算法的坐姿识别方法.首先,设计一种由少量薄膜压力传感器构成的体压传感阵列,将其置于坐垫内部;然后,利用该传感阵列采集不同坐姿的体压数据,并绘制相应的体压分布等高线图;最后,以体压数据作为特征向量,结合支持向量机算法建模,以实现坐姿分类自动识别.测试结果表明:少量体压传感器也能获取不同坐姿的体压分布特征;SVM坐姿分类模型在熟悉样本下的坐姿识别准确率达98.3%,在陌生样本下的坐姿识别准确率达92.5%.
Abstract:
In view of the high cost of the sitting posture recognition system due to the large number of sensors and complex system, a sitting posture recognition method based on a small number of body pressure sensors and support vector machine(SVM)algorithm is designed. Firstly, a body pressure sensor array based on a small number of thin film pressure sensors is designed, which is placed in the cushion. Then, the sensor array is used to collect the body pressure data of different sitting posture, and the corresponding contour maps of body pressure distribution are drawn. Finally, taking the body pressure data as the feature vector, the support vector machine model is combined to realize the automatic recognition of sitting posture. The test results show that a small number of body pressure sensors can also obtain the characteristics of body pressure distribution in different sitting positions. The accuracy of sitting posture recognition of SVM sitting posture classification model is 98.3% in familiar samples and 92.5% in unfamiliar samples.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2021-08-21
通信作者: 黄文权(1981-),男,讲师,主要从事建筑电气、设备故障诊断的研究.E-mail:peter81015@hqu.edu.cn.
更新日期/Last Update: 2022-03-20