IOTeeth:用于识别牙齿咬合疾病的口腔内牙齿传感系统

Zhizhang Hu, Amir Radmehr, Yue Zhang, Shijia Pan, Phuc Nguyen
{"title":"IOTeeth:用于识别牙齿咬合疾病的口腔内牙齿传感系统","authors":"Zhizhang Hu, Amir Radmehr, Yue Zhang, Shijia Pan, Phuc Nguyen","doi":"10.1145/3643516","DOIUrl":null,"url":null,"abstract":"While occlusal diseases - the main cause of tooth loss -- significantly impact patients' teeth and well-being, they are the most underdiagnosed dental diseases nowadays. Experiencing occlusal diseases could result in difficulties in eating, speaking, and chronicle headaches, ultimately impacting patients' quality of life. Although attempts have been made to develop sensing systems for teeth activity monitoring, solutions that support sufficient sensing resolution for occlusal monitoring are missing. To fill that gap, this paper presents IOTeeth, a cost-effective and automated intra-oral sensing system for continuous and fine-grained monitoring of occlusal diseases. The IOTeeth system includes an intra-oral piezoelectric-based sensing array integrated into a dental retainer platform to support reliable occlusal disease recognition. IOTeeth focuses on biting and grinding activities from the canines and front teeth, which contain essential information of occlusion. IOTeeth's intra-oral wearable collects signals from the sensors and fetches them into a lightweight and robust deep learning model called Physioaware Attention Network (PAN Net) for occlusal disease recognition. We evaluate IOTeeth with 12 articulator teeth models from dental clinic patients. Evaluation results show an F1 score of 0.97 for activity recognition with leave-one-out validation and an average F1 score of 0.92 for dental disease recognition for different activities with leave-one-out validation.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"23 4","pages":"7:1-7:29"},"PeriodicalIF":0.0000,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"IOTeeth: Intra-Oral Teeth Sensing System for Dental Occlusal Diseases Recognition\",\"authors\":\"Zhizhang Hu, Amir Radmehr, Yue Zhang, Shijia Pan, Phuc Nguyen\",\"doi\":\"10.1145/3643516\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"While occlusal diseases - the main cause of tooth loss -- significantly impact patients' teeth and well-being, they are the most underdiagnosed dental diseases nowadays. Experiencing occlusal diseases could result in difficulties in eating, speaking, and chronicle headaches, ultimately impacting patients' quality of life. Although attempts have been made to develop sensing systems for teeth activity monitoring, solutions that support sufficient sensing resolution for occlusal monitoring are missing. To fill that gap, this paper presents IOTeeth, a cost-effective and automated intra-oral sensing system for continuous and fine-grained monitoring of occlusal diseases. The IOTeeth system includes an intra-oral piezoelectric-based sensing array integrated into a dental retainer platform to support reliable occlusal disease recognition. IOTeeth focuses on biting and grinding activities from the canines and front teeth, which contain essential information of occlusion. IOTeeth's intra-oral wearable collects signals from the sensors and fetches them into a lightweight and robust deep learning model called Physioaware Attention Network (PAN Net) for occlusal disease recognition. We evaluate IOTeeth with 12 articulator teeth models from dental clinic patients. Evaluation results show an F1 score of 0.97 for activity recognition with leave-one-out validation and an average F1 score of 0.92 for dental disease recognition for different activities with leave-one-out validation.\",\"PeriodicalId\":20463,\"journal\":{\"name\":\"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.\",\"volume\":\"23 4\",\"pages\":\"7:1-7:29\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3643516\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3643516","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

咬合疾病--牙齿脱落的主要原因--严重影响患者的牙齿和健康,但却是目前最容易被忽视的牙科疾病。咬合疾病会导致进食困难、说话困难和长期头痛,最终影响患者的生活质量。虽然人们一直在尝试开发用于牙齿活动监测的传感系统,但目前还缺少能够为咬合监测提供足够传感分辨率的解决方案。为了填补这一空白,本文介绍了 IOTeeth,这是一种经济高效的自动口内传感系统,可对咬合疾病进行连续、精细的监测。IOTeeth 系统包括一个口内压电传感阵列,集成在一个牙科保持器平台上,支持可靠的咬合疾病识别。IOTeeth 主要监测犬齿和前牙的咬合和磨牙活动,这些活动包含咬合的基本信息。IOTeeth 的口内可穿戴设备收集来自传感器的信号,并将这些信号提取到一个名为 "物理感知注意力网络(PAN Net)"的轻量级鲁棒深度学习模型中,用于咬合疾病识别。我们使用牙科诊所患者的 12 个铰接牙齿模型对 IOTeeth 进行了评估。评估结果显示,在留空验证的情况下,活动识别的 F1 得分为 0.97,在留空验证的情况下,不同活动的牙科疾病识别平均 F1 得分为 0.92。
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
IOTeeth: Intra-Oral Teeth Sensing System for Dental Occlusal Diseases Recognition
While occlusal diseases - the main cause of tooth loss -- significantly impact patients' teeth and well-being, they are the most underdiagnosed dental diseases nowadays. Experiencing occlusal diseases could result in difficulties in eating, speaking, and chronicle headaches, ultimately impacting patients' quality of life. Although attempts have been made to develop sensing systems for teeth activity monitoring, solutions that support sufficient sensing resolution for occlusal monitoring are missing. To fill that gap, this paper presents IOTeeth, a cost-effective and automated intra-oral sensing system for continuous and fine-grained monitoring of occlusal diseases. The IOTeeth system includes an intra-oral piezoelectric-based sensing array integrated into a dental retainer platform to support reliable occlusal disease recognition. IOTeeth focuses on biting and grinding activities from the canines and front teeth, which contain essential information of occlusion. IOTeeth's intra-oral wearable collects signals from the sensors and fetches them into a lightweight and robust deep learning model called Physioaware Attention Network (PAN Net) for occlusal disease recognition. We evaluate IOTeeth with 12 articulator teeth models from dental clinic patients. Evaluation results show an F1 score of 0.97 for activity recognition with leave-one-out validation and an average F1 score of 0.92 for dental disease recognition for different activities with leave-one-out validation.
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
Multi-Subject 3D Human Mesh Construction Using Commodity WiFi UHead: Driver Attention Monitoring System Using UWB Radar DeltaLCA: Comparative Life-Cycle Assessment for Electronics Design Multimodal Daily-Life Logging in Free-living Environment Using Non-Visual Egocentric Sensors on a Smartphone Lateralization Effects in Electrodermal Activity Data Collected Using Wearable Devices
×
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