Real-time Health Monitoring using Wireless Body Area Network

BACKGROUND: Health monitoring is important for early disease diagnosis and will reduce the discomfort and treatment expenses, which is very relevant in terms of prevention. The early diagnosis and treatment of multiple conditions will improve solutions to the patient’s healthcare radically. A concept model for the real-time patient tracking system is the primary goal of the method. The Internet of things (IoT) has made health systems accessible for programs based on the value of patient health. OBJECTIVE: In this paper, the IoT-based cloud computing for patient health monitoring framework (IoT-CCPHM), has been proposed for effective monitoring of the patients. METHOD: The emerging connected sensors and IoT devices monitor and test the cardiac speed, oxygen saturation percentage, body temperature, and patient’s eye movement. The collected data are used in the cloud database to evaluate the patient’s health, and the effects of all measures are stored. The IoT-CCPHM maintains that the medical record is processed in the cloud servers. RESULTS: The experimental results show that patient health monitoring is a reliable way to improve health effectively.

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An Intelligent Framework for Energy Efficient Health Monitoring System Using Edge-Body Area Networks

Poovammal

2023

Advances in Science and Technology

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Body Area Networks (BAN) consists of sensors, microcontrollers interfaced with the wireless transceivers. BAN sensors are implanted or placed on the body's surface which allows for continuous monitoring of patients' health parameters. According to recent studies, BAN is a viable option for an effective transmission of detected parameters to the nearby health care centers. This transmission helps in energy consumption for further better diagnosis. With the advent of machine learning and Internet of Things (IoT), BAN has taken the dimension in achieving the better performance with limited threshold. Although, BANs are light weight implanted nodes, the problem in improving the performance still remains demur for researchers. This paper proposes the edge based BAN which integrates the powerful Bi layered feed forward (BLFF) learning models for efficient data transmission with lower consumption of energy. The proposed model works on the adaptive distance principle of Extreme Learning Machines (ELM) which detects the cluster head BAN network. The extensive experimentation has been carried out to find the consumption of energy in the network. Additionally, the performance of the proposed ELM-BLFF learning model has been compared with the other machine learning models which are integrated in BAN-IoT frameworks. An experimental result demonstrates that the proposed ELM-BLFF model outperforms the traditional learning model with 30% lesser in terms of energy consumption.

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Real-time Health Monitoring using Wireless Body Area Network

Cited by 3 publications

References 18 publications

Reinforcement Learning based Cloud and Edge Resource Allocation for Real-Time Telemedicine

Reinforcement Learning based Cloud and Edge Resource Allocation for Real-Time Telemedicine

Internet of things-based cloud computing platform for analyzing the physical health condition

An Intelligent Framework for Energy Efficient Health Monitoring System Using Edge-Body Area Networks

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