A Posture Recognition Model Dedicated for Differentiating between Proper and Improper Sitting Posture with Kinect Sensor

LCK. Chin, K. Eu, T. T. Tay, C. Teoh, K. Yap
{"title":"A Posture Recognition Model Dedicated for Differentiating between Proper and Improper Sitting Posture with Kinect Sensor","authors":"LCK. Chin, K. Eu, T. T. Tay, C. Teoh, K. Yap","doi":"10.1109/HAVE.2019.8920964","DOIUrl":null,"url":null,"abstract":"In this era, most of mankind’s activities are carried out on top of a desk, but they rarely bother to sit with the right posture and this can lead to problems like back pain. In 2013, the number of working days lost due to sickness absence in UK is 131 million days, and 23.66% of this number is contributed by back pain and neck pain victims [1]. In this paper, a preliminary study of posture recognition system has been developed, to rectify the user’s sitting posture by alerting him/her. A proper and improper sitting posture might look quite similar to each other in the eye of sensors, especially different heights and genders cause difficulties in detection. Hence, a preliminary posture recognition model that specifically tackles the recognition between a proper and an improper sitting posture has been developed. In this paper, we use Kinect sensor for the sitting postures detection, and then feed the postures data to the posture recognition models such as Support Vector Machine (SVM) and Artificial Neural Network (ANN), for training purpose. We compared these two models and found that SVM with linear kernel has the highest accuracy.","PeriodicalId":446032,"journal":{"name":"2019 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE)","volume":"242 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HAVE.2019.8920964","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

In this era, most of mankind’s activities are carried out on top of a desk, but they rarely bother to sit with the right posture and this can lead to problems like back pain. In 2013, the number of working days lost due to sickness absence in UK is 131 million days, and 23.66% of this number is contributed by back pain and neck pain victims [1]. In this paper, a preliminary study of posture recognition system has been developed, to rectify the user’s sitting posture by alerting him/her. A proper and improper sitting posture might look quite similar to each other in the eye of sensors, especially different heights and genders cause difficulties in detection. Hence, a preliminary posture recognition model that specifically tackles the recognition between a proper and an improper sitting posture has been developed. In this paper, we use Kinect sensor for the sitting postures detection, and then feed the postures data to the posture recognition models such as Support Vector Machine (SVM) and Artificial Neural Network (ANN), for training purpose. We compared these two models and found that SVM with linear kernel has the highest accuracy.
基于Kinect传感器的正确与错误坐姿识别模型
在这个时代,人类的大部分活动都是在桌子上进行的,但他们很少花时间以正确的姿势坐着,这可能会导致背痛等问题。2013年,英国因病缺勤而损失的工作日为1.31亿天,其中23.66%是背痛和颈部疼痛患者造成的。本文对体态识别系统进行了初步研究,通过提醒用户纠正坐姿。在传感器眼中,正确和不正确的坐姿可能看起来非常相似,特别是身高和性别的不同会给检测带来困难。因此,一个初步的姿势识别模型,专门处理正确和不正确的坐姿之间的识别已被开发。在本文中,我们使用Kinect传感器进行坐姿检测,然后将姿势数据馈送到支持向量机(SVM)和人工神经网络(ANN)等姿势识别模型中进行训练。我们比较了这两种模型,发现线性核支持向量机的准确率最高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信