MultiGRehab:基于自适应严肃游戏的个性化中风和心脏康复的多模态生物信号采集和分析框架

S. Dias, L. Hadjileontiadis, H. F. Jelinek
{"title":"MultiGRehab:基于自适应严肃游戏的个性化中风和心脏康复的多模态生物信号采集和分析框架","authors":"S. Dias, L. Hadjileontiadis, H. F. Jelinek","doi":"10.1109/ICDH55609.2022.00035","DOIUrl":null,"url":null,"abstract":"Rehabilitation programs for post stroke recovery or following a heart attack are always stressful for patients, who have been spending time in hospital, an unaccustomed environment, experiencing surgery burden, irregular sleep, and undergoing general rehabilitation exercise programs. In the latter, the exercise intensity and difficulty are often more than what a patient can manage, and usually subjective decisions on the level of exercise intensity and difficulty are followed. To address this issue in a more personalized way, the development of a new rehabilitation framework, namely MultiGRehab (multi-sensed biosignals combined with serious games), is proposed here. In fact, MultiGRehab captures multimodal biosignals in a real-time fashion during a patient's rehabilitation session that includes serious gaming. Through biosignals swarm decomposition and deep learning, the emotional state of the patient is estimated and used as a controlling factor for the serious game adaptation, in terms of exercise type, duration and intensity level. In this way, MultiGRehab is expected to increase a patient's motivation, adherence to the exercise protocol and personalization of rehabilitation targets and outcomes.","PeriodicalId":120923,"journal":{"name":"2022 IEEE International Conference on Digital Health (ICDH)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"MultiGRehab: Developing a Multimodal Biosignals Acquisition and Analysis Framework for Personalizing Stroke and Cardiac Rehabilitation based on Adaptive Serious Games\",\"authors\":\"S. Dias, L. Hadjileontiadis, H. F. Jelinek\",\"doi\":\"10.1109/ICDH55609.2022.00035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rehabilitation programs for post stroke recovery or following a heart attack are always stressful for patients, who have been spending time in hospital, an unaccustomed environment, experiencing surgery burden, irregular sleep, and undergoing general rehabilitation exercise programs. In the latter, the exercise intensity and difficulty are often more than what a patient can manage, and usually subjective decisions on the level of exercise intensity and difficulty are followed. To address this issue in a more personalized way, the development of a new rehabilitation framework, namely MultiGRehab (multi-sensed biosignals combined with serious games), is proposed here. In fact, MultiGRehab captures multimodal biosignals in a real-time fashion during a patient's rehabilitation session that includes serious gaming. Through biosignals swarm decomposition and deep learning, the emotional state of the patient is estimated and used as a controlling factor for the serious game adaptation, in terms of exercise type, duration and intensity level. In this way, MultiGRehab is expected to increase a patient's motivation, adherence to the exercise protocol and personalization of rehabilitation targets and outcomes.\",\"PeriodicalId\":120923,\"journal\":{\"name\":\"2022 IEEE International Conference on Digital Health (ICDH)\",\"volume\":\"111 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Digital Health (ICDH)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDH55609.2022.00035\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Digital Health (ICDH)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDH55609.2022.00035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

中风后或心脏病发作后的康复计划对患者来说总是有压力的,他们一直在医院度过时间,不习惯的环境,经历手术负担,不规律的睡眠,并进行一般的康复锻炼计划。在后者中,运动强度和难度往往超过患者的控制能力,通常是主观决定运动强度和难度的水平。为了以更个性化的方式解决这一问题,本文提出了一种新的康复框架,即MultiGRehab(多传感生物信号与严肃游戏相结合)。事实上,MultiGRehab在病人的康复过程中实时捕捉多模态生物信号,包括玩游戏。通过生物信号群分解和深度学习,估计患者在运动类型、持续时间和强度水平方面的情绪状态,并将其作为严重游戏适应的控制因素。通过这种方式,MultiGRehab有望增加患者的动力,坚持锻炼方案,个性化康复目标和结果。
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
MultiGRehab: Developing a Multimodal Biosignals Acquisition and Analysis Framework for Personalizing Stroke and Cardiac Rehabilitation based on Adaptive Serious Games
Rehabilitation programs for post stroke recovery or following a heart attack are always stressful for patients, who have been spending time in hospital, an unaccustomed environment, experiencing surgery burden, irregular sleep, and undergoing general rehabilitation exercise programs. In the latter, the exercise intensity and difficulty are often more than what a patient can manage, and usually subjective decisions on the level of exercise intensity and difficulty are followed. To address this issue in a more personalized way, the development of a new rehabilitation framework, namely MultiGRehab (multi-sensed biosignals combined with serious games), is proposed here. In fact, MultiGRehab captures multimodal biosignals in a real-time fashion during a patient's rehabilitation session that includes serious gaming. Through biosignals swarm decomposition and deep learning, the emotional state of the patient is estimated and used as a controlling factor for the serious game adaptation, in terms of exercise type, duration and intensity level. In this way, MultiGRehab is expected to increase a patient's motivation, adherence to the exercise protocol and personalization of rehabilitation targets and outcomes.
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
Designing User-friendly Medical AI Applications - Methodical Development of User-centered Design Guidelines Digital Health Promotion For Fitness Enthusiasts In Africa Knowledge Management in a Healthcare Enterprise: Creation of a Digital Knowledge Repository A New Low-Cost and Accurate Diagnostic mHealth System for Patients with COVID-19 Pneumonia Detection of Erythropoietin in Blood to Uncover Doping in Sports using Machine Learning
×
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