基于MEMS磁强计和神经网络的驾驶员头部姿态监测与疲劳分析

Hobeom Han, Hyeongkyu Jang, S. Yoon
{"title":"基于MEMS磁强计和神经网络的驾驶员头部姿态监测与疲劳分析","authors":"Hobeom Han, Hyeongkyu Jang, S. Yoon","doi":"10.1109/SENSORS43011.2019.8956799","DOIUrl":null,"url":null,"abstract":"This paper presents a portable sensor system to monitor unbalanced head postures of long-distance drivers. The head posture monitoring enables driver fatigue analysis (avoiding potential car accidents) and prevents chronic fatigue and possible neck related diseases. A three-axis MEMS magnetometer and miniature magnet are attached on a user’s neck. The user conducts driving scenarios consisting of five common driving actives and one inconvenient posture. Collected magnetometer data are proceeded by neural network algorithms. Part of the data are used to develop a learned model and rest of them are used to calibrate the model. Experiment results are very promising and exhibit superior model accuracy as high as 93.0%.","PeriodicalId":6710,"journal":{"name":"2019 IEEE SENSORS","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Driver Head Posture Monitoring using MEMS Magnetometer and Neural Network for Long-distance Driving Fatigue Analysis\",\"authors\":\"Hobeom Han, Hyeongkyu Jang, S. Yoon\",\"doi\":\"10.1109/SENSORS43011.2019.8956799\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a portable sensor system to monitor unbalanced head postures of long-distance drivers. The head posture monitoring enables driver fatigue analysis (avoiding potential car accidents) and prevents chronic fatigue and possible neck related diseases. A three-axis MEMS magnetometer and miniature magnet are attached on a user’s neck. The user conducts driving scenarios consisting of five common driving actives and one inconvenient posture. Collected magnetometer data are proceeded by neural network algorithms. Part of the data are used to develop a learned model and rest of them are used to calibrate the model. Experiment results are very promising and exhibit superior model accuracy as high as 93.0%.\",\"PeriodicalId\":6710,\"journal\":{\"name\":\"2019 IEEE SENSORS\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE SENSORS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SENSORS43011.2019.8956799\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE SENSORS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SENSORS43011.2019.8956799","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

提出了一种用于远距离驾驶员头部不平衡姿态监测的便携式传感器系统。头部姿势监测使驾驶员疲劳分析(避免潜在的车祸),防止慢性疲劳和可能的颈部相关疾病。一个三轴MEMS磁强计和微型磁体附着在用户的脖子上。用户进行由五种常见驾驶动作和一种不方便姿势组成的驾驶场景。采集到的磁强计数据通过神经网络算法进行处理。部分数据用于建立学习模型,其余数据用于校准模型。实验结果令人满意,模型精度高达93.0%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Driver Head Posture Monitoring using MEMS Magnetometer and Neural Network for Long-distance Driving Fatigue Analysis
This paper presents a portable sensor system to monitor unbalanced head postures of long-distance drivers. The head posture monitoring enables driver fatigue analysis (avoiding potential car accidents) and prevents chronic fatigue and possible neck related diseases. A three-axis MEMS magnetometer and miniature magnet are attached on a user’s neck. The user conducts driving scenarios consisting of five common driving actives and one inconvenient posture. Collected magnetometer data are proceeded by neural network algorithms. Part of the data are used to develop a learned model and rest of them are used to calibrate the model. Experiment results are very promising and exhibit superior model accuracy as high as 93.0%.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Identification of Legionella Species by Photogate-Type Optical Sensor A Nano-Watt Dual-Mode Address Detector for a Wi-Fi Enabled RF Wake-up Receiver Optical Feedback Interferometry imaging sensor for micrometric flow-patterns using continuous scanning DNN-based Outdoor NLOS Human Detection Using IEEE 802.11ac WLAN Signal Disconnect Switch Position Sensor Based on FBG
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1