IoMT Enabled Prototype Edge Computing Healthcare System for Isolated Patients

Shamna P.A, Priyanka C Mohan
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Abstract

Continuous patient care and the use of multiple medical machines are two challenges facing today's healthcare sector in terms of patient’s healthcare. During the pandemic situation, many people isolated in their home, such as covid-19 positive patients, elderly people living away from their families, bedridden patients, etc., need regular health checks and controls, but during this pandemic is lacking. Recent advances in the Internet of Medical Things (IoMT) has been able to give good results in collecting health data of patients at home environment. Deep learning (DL) applications can able to run on edge nodes, it locally processes, computes and analyzes data from IOMT devices to make inferences on patient health information. This ensures the privacy and security of the patient's physiological information and also and allows patient health information to remain at the patient's side. Send all this information to healthcare professionals and relatives of patients. This framework will provide safety for isolated patients and a health support systemas a whole.
支持IoMT的隔离患者原型边缘计算医疗保健系统
就患者医疗保健而言,持续的患者护理和多台医疗机器的使用是当今医疗保健部门面临的两大挑战。在大流行期间,许多在家隔离的人,如covid-19阳性患者、远离家人的老年人、卧床不起的患者等,需要定期进行健康检查和控制,但在这次大流行期间却缺乏这种检查和控制。医疗物联网(IoMT)的最新进展已经能够在家庭环境中收集患者的健康数据。深度学习(DL)应用程序能够在边缘节点上运行,它在本地处理、计算和分析来自IOMT设备的数据,以推断患者的健康信息。这确保了患者生理信息的隐私和安全,也允许患者的健康信息留在患者身边。将所有这些信息发送给医疗保健专业人员和患者亲属。该框架将为隔离患者和整个卫生支持系统提供安全保障。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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