以可穿戴设备为导向,支持解释行为对睡眠的影响。

Clauirton A Siebra, Jonysberg Quintino, Andre L M Santos, Fabio Q B Da Silva
{"title":"以可穿戴设备为导向,支持解释行为对睡眠的影响。","authors":"Clauirton A Siebra, Jonysberg Quintino, Andre L M Santos, Fabio Q B Da Silva","doi":"10.1109/EMBC53108.2024.10781768","DOIUrl":null,"url":null,"abstract":"<p><p>Daily behaviour directly impacts health in the short and long term. Thus, embracing and maintaining healthy behaviours work like a preventive action, avoiding or delaying the emergence of chronic diseases. The process of changing daily routines toward healthy behaviours starts by understanding the current problems. Wearable and deep learning (DL) technologies represent important resources for supporting such an understanding. This paper discusses a strategy to interpret multifeatured longitudinal wearable data to analyse possible causes of health issues. We use the sleep domain as a case example where the aim is to clarify the reasons for poor sleep quality. A dataset with wearable data of 1874 days was used to create an explainable DL model, which indicates the main day-before-night sleep behaviours that may cause poor sleep quality. We use a comparative analysis with a hormone-based framework for sleep control as the form of validation. The results show that the explanations corroborate the results of the literature. However, other datasets with more features should be explored to verify the combination of these features and their effects on the health aspect under study.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Wearable-oriented Support for Interpretation of Behavioural Effects on Sleep.\",\"authors\":\"Clauirton A Siebra, Jonysberg Quintino, Andre L M Santos, Fabio Q B Da Silva\",\"doi\":\"10.1109/EMBC53108.2024.10781768\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Daily behaviour directly impacts health in the short and long term. Thus, embracing and maintaining healthy behaviours work like a preventive action, avoiding or delaying the emergence of chronic diseases. The process of changing daily routines toward healthy behaviours starts by understanding the current problems. Wearable and deep learning (DL) technologies represent important resources for supporting such an understanding. This paper discusses a strategy to interpret multifeatured longitudinal wearable data to analyse possible causes of health issues. We use the sleep domain as a case example where the aim is to clarify the reasons for poor sleep quality. A dataset with wearable data of 1874 days was used to create an explainable DL model, which indicates the main day-before-night sleep behaviours that may cause poor sleep quality. We use a comparative analysis with a hormone-based framework for sleep control as the form of validation. The results show that the explanations corroborate the results of the literature. However, other datasets with more features should be explored to verify the combination of these features and their effects on the health aspect under study.</p>\",\"PeriodicalId\":72237,\"journal\":{\"name\":\"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference\",\"volume\":\"2024 \",\"pages\":\"1-4\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EMBC53108.2024.10781768\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EMBC53108.2024.10781768","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

本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wearable-oriented Support for Interpretation of Behavioural Effects on Sleep.

Daily behaviour directly impacts health in the short and long term. Thus, embracing and maintaining healthy behaviours work like a preventive action, avoiding or delaying the emergence of chronic diseases. The process of changing daily routines toward healthy behaviours starts by understanding the current problems. Wearable and deep learning (DL) technologies represent important resources for supporting such an understanding. This paper discusses a strategy to interpret multifeatured longitudinal wearable data to analyse possible causes of health issues. We use the sleep domain as a case example where the aim is to clarify the reasons for poor sleep quality. A dataset with wearable data of 1874 days was used to create an explainable DL model, which indicates the main day-before-night sleep behaviours that may cause poor sleep quality. We use a comparative analysis with a hormone-based framework for sleep control as the form of validation. The results show that the explanations corroborate the results of the literature. However, other datasets with more features should be explored to verify the combination of these features and their effects on the health aspect under study.

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
CiteScore
0.80
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