A Comprehensive Survey on IoT Technologies in Health Care System

Bavya, N; Arunkumar, T.; Adalarasu, K.

doi:10.5958/0974-360x.2018.00580.2

Cited by 6 publications

(1 citation statement)

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“…In the wireless remote physiological parameter transmission system in this paper, the physiological parameters reflecting the function of the human body mainly include ECG, blood pressure, blood oxygen saturation, body temperature, breathing, etc. They have extremely important reference values in human life monitoring [40], [41]. Finding some early physiological parameter changes, and regulating and processing can reduce or even avoid accidents.…”

Section: Research On Telemedicine Multiple Physiological Parametementioning

confidence: 99%

Health Monitoring of Human Multiple Physiological Parameters Based on Wireless Remote Medical System

Zhang

Ling

2020

IEEE Access

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Telemedicine, as a new technical means and medical model, can truly realize the sharing and monitoring of telemedicine information, and ultimately ensure that everyone has equal access to medical and health resources. Based on the research on the status of telemedicine application and wireless communication technology, this paper proposes a multi-physical parameter wireless telemedicine health monitoring system solution, and analyzes the overall structure and functional requirements of the system. Human physiological parameters of the wireless remote medical system for health monitoring include body temperature, respiration, blood oxygen saturation, pulse, blood pressure, and electrocardiogram. In this paper, fabric electrodes are used to extract human bioimpedance signals, discrete Fourier transform algorithm is used to detect human respiratory signals, and respiratory rate is detected based on dynamic differential threshold peak detection technology. The reflection type photoelectric sensor is used to realize the reflection of the human pulse signal, and the continuous measurement of the cuff-free blood pressure based on the pulse wave conduction time is combined with the ECG (Electrocardiogram) data. A self-learning threshold algorithm based on near-infrared photo plethysmo graphy signal trough detection is designed on the reflective blood oxygen saturation calculation algorithm. The difference threshold method is used to extract the QRS band feature points. We tested the overall operation of the system. The results show that the collected human physiological signal data is accurate. After a series of tests, the validity and reliability of the collected physiological signals have been proven. INDEX TERMS Telemedicine, health monitoring, physiological signals, signal processing.

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Section: Research On Telemedicine Multiple Physiological Parametementioning

confidence: 99%

Health Monitoring of Human Multiple Physiological Parameters Based on Wireless Remote Medical System

Zhang

Ling

2020

IEEE Access

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IoT-Enabled Healthcare: Benefits, Issues and Challenges‏

Al-Shargabi¹,

Abuarqoub

2020

The 4th International Conference on Future Networks and Distributed Systems (ICFNDS)

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An adaptive QoS supportive approach for user based services using Krill Herd Approach over Internet of Things (KHAI)

Padmavathi

Saminathan

Selvamuthukumaran³

2021

KES

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The demand for a providing QoS adaptive routing over IoT networks is always a challenge among current research community. This research work KHAI proposes a framework for QoS-adaptive routing approach, which incorporates Krill Herd optimization model over IoT network. Variable QoS user preference and handling differential service types over a scalable IoT network shows that challenge for designing an adaptive QoS is a must. Research survey suggest that major works have been carried out on bandwidth appreciable services and route management approaches. Hence QoS adaptive user defined services, which adapt to variable service priority levels based on user demand and network resource utilization is proposed in this research work. The performance analysis of proposed approach shows an improved throughput of 97.51 Mbps and minimal packet loss of 37.29% over a session in comparison to traditional computational approaches. Considering large scale of interconnected IoT devices, proposed approach delivers near optimal solution of throughput and adaptive utilization of network resources.

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A Comprehensive Survey on IoT Technologies in Health Care System

Cited by 6 publications

References 0 publications

Health Monitoring of Human Multiple Physiological Parameters Based on Wireless Remote Medical System

Health Monitoring of Human Multiple Physiological Parameters Based on Wireless Remote Medical System

IoT-Enabled Healthcare: Benefits, Issues and Challenges‏

An adaptive QoS supportive approach for user based services using Krill Herd Approach over Internet of Things (KHAI)

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