模糊逻辑在基于多传感器的医疗服务机器人中的应用,用于大流行病期间的状态监测

A. Rout, G. B. Mahanta, B. Biswal, Renin Francy T., Sri Vardhan Raj, Deepak B.B.V.L.
{"title":"模糊逻辑在基于多传感器的医疗服务机器人中的应用,用于大流行病期间的状态监测","authors":"A. Rout, G. B. Mahanta, B. Biswal, Renin Francy T., Sri Vardhan Raj, Deepak B.B.V.L.","doi":"10.1108/ria-07-2023-0091","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThe purpose of this study is to plan and develop a cost-effective health-care robot for assisting and observing the patients in an accurate and effective way during pandemic situation like COVID-19. The purposed research work can help in better management of pandemic situations in rural areas as well as developing countries where medical facility is not easily available.\n\n\nDesign/methodology/approach\nIt becomes very difficult for the medical staff to have a continuous check on patient’s condition in terms of symptoms and critical parameters during pandemic situations. For dealing with these situations, a service mobile robot with multiple sensors for measuring patients bodily indicators has been proposed and the prototype for the same has been developed that can monitor and aid the patient using the robotic arm. The fuzzy controller has also been incorporated with the mobile robot through which decisions on patient monitoring can be taken automatically. Mamdani implication method has been utilized for formulating mathematical expression of M number of “if and then condition based rules” with defined input Xj (j = 1, 2, ………. s), and output yi. The inputs and output variables are formed by the membership functions µAij(xj) and µCi(yi) to execute the Fuzzy Inference System controller. Here, Aij and Ci are the developed fuzzy sets.\n\n\nFindings\nThe fuzzy-based prediction model has been tested with the output of medicines for the initial 27 runs and was validated by the correlation of predicted and actual values. The correlation coefficient has been found to be 0.989 with a mean square error value of 0.000174, signifying a strong relationship between the predicted values and the actual values. The proposed research work can handle multiple tasks like online consulting, continuous patient condition monitoring in general wards and ICUs, telemedicine services, hospital waste disposal and providing service to patients at regular time intervals.\n\n\nOriginality/value\nThe novelty of the proposed research work lies in the integration of artificial intelligence techniques like fuzzy logic with the multi-sensor-based service robot for easy decision-making and continuous patient monitoring in hospitals in rural areas and to reduce the work stress on medical staff during pandemic situation.\n","PeriodicalId":501194,"journal":{"name":"Robotic Intelligence and Automation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of fuzzy logic in multi-sensor-based health service robot for condition monitoring during pandemic situations\",\"authors\":\"A. Rout, G. B. Mahanta, B. Biswal, Renin Francy T., Sri Vardhan Raj, Deepak B.B.V.L.\",\"doi\":\"10.1108/ria-07-2023-0091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nThe purpose of this study is to plan and develop a cost-effective health-care robot for assisting and observing the patients in an accurate and effective way during pandemic situation like COVID-19. The purposed research work can help in better management of pandemic situations in rural areas as well as developing countries where medical facility is not easily available.\\n\\n\\nDesign/methodology/approach\\nIt becomes very difficult for the medical staff to have a continuous check on patient’s condition in terms of symptoms and critical parameters during pandemic situations. For dealing with these situations, a service mobile robot with multiple sensors for measuring patients bodily indicators has been proposed and the prototype for the same has been developed that can monitor and aid the patient using the robotic arm. The fuzzy controller has also been incorporated with the mobile robot through which decisions on patient monitoring can be taken automatically. Mamdani implication method has been utilized for formulating mathematical expression of M number of “if and then condition based rules” with defined input Xj (j = 1, 2, ………. s), and output yi. The inputs and output variables are formed by the membership functions µAij(xj) and µCi(yi) to execute the Fuzzy Inference System controller. Here, Aij and Ci are the developed fuzzy sets.\\n\\n\\nFindings\\nThe fuzzy-based prediction model has been tested with the output of medicines for the initial 27 runs and was validated by the correlation of predicted and actual values. The correlation coefficient has been found to be 0.989 with a mean square error value of 0.000174, signifying a strong relationship between the predicted values and the actual values. The proposed research work can handle multiple tasks like online consulting, continuous patient condition monitoring in general wards and ICUs, telemedicine services, hospital waste disposal and providing service to patients at regular time intervals.\\n\\n\\nOriginality/value\\nThe novelty of the proposed research work lies in the integration of artificial intelligence techniques like fuzzy logic with the multi-sensor-based service robot for easy decision-making and continuous patient monitoring in hospitals in rural areas and to reduce the work stress on medical staff during pandemic situation.\\n\",\"PeriodicalId\":501194,\"journal\":{\"name\":\"Robotic Intelligence and Automation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Robotic Intelligence and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/ria-07-2023-0091\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotic Intelligence and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ria-07-2023-0091","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

目的本研究的目的是规划和开发一种具有成本效益的医疗保健机器人,用于在 COVID-19 等大流行病期间准确有效地协助和观察病人。设计/方法/途径在大流行病期间,医务人员很难持续检查病人的症状和关键参数。为了应对这种情况,我们提出了一种带有多个传感器的服务型移动机器人,用于测量病人的身体指标,并开发了该机器人的原型,可以利用机械臂监测和帮助病人。移动机器人还采用了模糊控制器,可自动做出监测病人的决定。马姆达尼蕴含法被用于制定 M 个 "如果和然后条件规则 "的数学表达式,其中定义了输入 Xj(j = 1、2、.......... s)和输出 yi。输入和输出变量由成员函数 µAij(xj) 和 µCi(yi) 组成,以执行模糊推理系统控制器。研究结果基于模糊的预测模型通过最初 27 次运行的药品输出进行了测试,并通过预测值和实际值的相关性进行了验证。相关系数为 0.989,均方误差值为 0.000174,表明预测值与实际值之间的关系密切。原创性/价值这项研究工作的新颖之处在于将模糊逻辑等人工智能技术与基于多传感器的服务机器人相结合,便于农村地区医院做出决策和对病人进行连续监测,并减轻大流行病期间医务人员的工作压力。
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
Application of fuzzy logic in multi-sensor-based health service robot for condition monitoring during pandemic situations
Purpose The purpose of this study is to plan and develop a cost-effective health-care robot for assisting and observing the patients in an accurate and effective way during pandemic situation like COVID-19. The purposed research work can help in better management of pandemic situations in rural areas as well as developing countries where medical facility is not easily available. Design/methodology/approach It becomes very difficult for the medical staff to have a continuous check on patient’s condition in terms of symptoms and critical parameters during pandemic situations. For dealing with these situations, a service mobile robot with multiple sensors for measuring patients bodily indicators has been proposed and the prototype for the same has been developed that can monitor and aid the patient using the robotic arm. The fuzzy controller has also been incorporated with the mobile robot through which decisions on patient monitoring can be taken automatically. Mamdani implication method has been utilized for formulating mathematical expression of M number of “if and then condition based rules” with defined input Xj (j = 1, 2, ………. s), and output yi. The inputs and output variables are formed by the membership functions µAij(xj) and µCi(yi) to execute the Fuzzy Inference System controller. Here, Aij and Ci are the developed fuzzy sets. Findings The fuzzy-based prediction model has been tested with the output of medicines for the initial 27 runs and was validated by the correlation of predicted and actual values. The correlation coefficient has been found to be 0.989 with a mean square error value of 0.000174, signifying a strong relationship between the predicted values and the actual values. The proposed research work can handle multiple tasks like online consulting, continuous patient condition monitoring in general wards and ICUs, telemedicine services, hospital waste disposal and providing service to patients at regular time intervals. Originality/value The novelty of the proposed research work lies in the integration of artificial intelligence techniques like fuzzy logic with the multi-sensor-based service robot for easy decision-making and continuous patient monitoring in hospitals in rural areas and to reduce the work stress on medical staff during pandemic situation.
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
Indoor fixed-point hovering control for UAVs based on visual inertial SLAM Design and performance analysis of different cambered wings for flapping-wing aerial vehicles based on wind tunnel test A novel framework inspired by human behavior for peg-in-hole assembly Development of vision–based SLAM: from traditional methods to multimodal fusion An MS-TCN based spatiotemporal model with three-axis tactile for enhancing flexible printed circuit assembly
×
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