WITHDRAWN: Computational efficient wearable sensor network health monitoring system for sports athletics using IoT

Li, Shuisheng; Zhang, Baoxin; Fei, Panfeng; Shakeel, P. Mohamed; Samuel, R. Dinesh Jackson

doi:10.1016/j.avb.2020.101541

Cited by 60 publications

(18 citation statements)

References 24 publications

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“…During the past decade, the sports health monitoring system (SHMS) [1][2][3][4] has attracted much attention from academia and industry. e corresponding research and development efforts have increased year by year.…”

Section: Introductionmentioning

confidence: 99%

Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks

Nie

Xiong

et al. 2021

Journal of Healthcare Engineering

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The healthcare benefits associated with regular physical activity recognition and monitoring have been considered in several research studies. Regular recognition and monitoring of health status can potentially assist in managing and reducing the risk of many diseases such as cardiovascular disease, diabetes, and obesity. Using healthcare equipment in hospitals, people can conduct regular physical examinations to check their health status. However, most of the time, it is difficult to reach a specific medical environment and use special medical equipment. In this paper, a deep learning framework based on the bidirectional gated recurrent unit for health status recognition is implemented to improve the accuracy by making full use of the information provided by smartphone acceleration sensors. A model based on a bidirectional gated recurrent unit is constructed to describe the relationship between input acceleration signals and output information through a gating approach. Therefore, it can automatically detect the health status of the sportsman as healthy, subhealthy, and unhealthy. Finally, the practical data collected from an athlete have been used to evaluate the recognition performance of the system. Results show that the proposed methodology can predicate the sports health status accurately.

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Section: Introductionmentioning

confidence: 99%

Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks

Nie

Xiong

et al. 2021

Journal of Healthcare Engineering

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“…Data from the sensor can be correlated with other sensor data to discover any abnormalities in the patients [68,85]. The research on [86] describes a health monitoring system for sports athletes by continuously monitoring their vitals and transferring them to the cloud [5,22,24,29,48,53,77]. The paper [42] proposes a CPS framework, where the data are sent to various cloud storage systems.…”

Section: Blood Pressure Sensorsmentioning

confidence: 99%

Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

et al. 2021

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IoT has played an essential role in many industries over the last few decades. Recent advancements in the healthcare industry have made it possible to make healthcare accessible to more people and improve their overall health. The next step in healthcare is to integrate it with IoT-assisted wearable sensor systems seamlessly. This review rigorously discusses the various IoT architectures, different methods of data processing, transfer, and computing paradigms. It compiles various communication technologies and the devices commonly used in IoT-assisted wearable sensor systems and deals with its various applications in healthcare and their advantages to the world. A comparative analysis of all the wearable technology in healthcare is also discussed with tabulation of various research and technology. This review also analyses all the problems commonly faced in IoT-assisted wearable sensor systems and the specific issues that need to be tackled to optimize these systems in healthcare and describes the various future implementations that can be made to the architecture and the technology to improve the healthcare industry.

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“…e effectiveness, reliability, and user-friendliness are demonstrated through results of the experiments [2].…”

Section: Introductionmentioning

confidence: 99%

Selection and Ranking of Fog Computing‐Based IoT for Monitoring of Health Using the Analytic Network Approach

et al. 2021

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Numerous raised areas are established in the field of fog computing (FC), applied for various purposes, and are evaluated for running analytics on various devices including devices of internet of things and many others in a disseminated way. FC progresses the prototype of cloud computing to network edge leading various possibilities and services. FC improves processing, decision, and intervention to take place through devices of IoT and communicate essential details. The idea of FC in healthcare based on frameworks of IoT is exploited by determining dispersed delegate layer of comprehension between the cloud and sensor hubs. The clouds suggested systems improved to overcome several challenges in ubiquitous frameworks of medical services such as energy efficiency, portability, adaptableness, and quality issues by accommodating right to take care of definite weights of the distant medical services group and sensor networks. The proposed research work has considered the analytic network process (ANP) for selection and ranking of FC-based IoT for health monitoring systems. The approach works in situation when complexity arises for health monitoring. Results of the study show the success of the research for facilitating healthcare.

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WITHDRAWN: Computational efficient wearable sensor network health monitoring system for sports athletics using IoT

Cited by 60 publications

References 24 publications

Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks

Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks

Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

Selection and Ranking of Fog Computing‐Based IoT for Monitoring of Health Using the Analytic Network Approach

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