Energetic improvement of smart Healthcare system using Internet of Things. (2021)

• Record Type: Journal Article
• Authors: Nirmala, P.; Asha, S.; Nalini Joseph, L.; Priya, S.V.
• Is Part Of: Materials today. Volume 47:Part 1(2021)
• Page Start: 63
• Page End: 69
• Publication Date: 2021
• Subjects: HealthCare -- Internet of Things -- IoT -- Healthcare Processing with IoT Surveillance -- HPIoTS -- Intense Data Analysis Scheme -- IDAS; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.03.619
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: In this modern era, each and every individual needs a global communication without any range and timeline restrictions. A well-known Internet of Things (IoT) provides such support to individuals and organization to provide range free communication services without any hurdles. This paper introduces a new methodology called Healthcare Processing with IoT Surveillance (HPIoTS), in which it adopts some latest technologies to provide an efficient support to monitor the healthcare of the respective patient and report those details properly to the server end with the help of Internet of Things. This proposed scheme of HPIoTS enables a shrewd Healthcare framework in Internet of Things platform, in which it screen a patient fundamental health oriented issues with surrounding atmosphere ratios such as temperature, humidity level, position and so on. In this framework, a smart health assisted gadget is designed with different sensors to catch the health related information from the patients' side such as: Heart Rate Monitoring Sensor, Pressure Sensor, Temperature and Humidity Level Monitoring Sensor and MEMS Sensor. The association of these sensors made a device more perfect to acquire the health summary of the patients periodically and pushed into the server end without any timing delay and human interventions. The maximum error rate of this proposed approach is estimated around less than 5% as well as this will be proved properly in results sections via graphical proofs. This paper adopts the machine learning strategy to predict the patient health related issues and provides a good accuracy level of 97% in result. The proposed approach guarantees the efficiency of the healthcare monitoring system by using Healthcare Processing with IoT Surveillance (HPIoTS) as well as the resulting accuracy levels are proved on the results section with proper graphical outcomes.