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Open AccessArticle

Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System

1
University of California Davis, Davis, CA 95616, USA
2
Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Melaka, Malaysia
3
Biomedical Engineering Department, Faculty of Engineering, Helwan University, Helwan 11792, Egypt
4
Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Korea
5
Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu 641021, India
6
Department of Computer Science and IT, La Trobe University, Melbourne 3086, Australia
7
Anna University, Tamil Nadu 600025, India
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(13), 3030; https://doi.org/10.3390/s19133030
Received: 28 May 2019 / Revised: 26 June 2019 / Accepted: 1 July 2019 / Published: 9 July 2019
(This article belongs to the Special Issue Mobile and Embedded Devices in Multi-access Edge Computing)
According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents’ physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology. View Full-Text
Keywords: multi access physical monitoring system; multimedia technology; edge computing; Bayesian neural network; smart-log patch multi access physical monitoring system; multimedia technology; edge computing; Bayesian neural network; smart-log patch
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Manogaran, G.; Shakeel, P.M.; Fouad, H.; Nam, Y.; Baskar, S.; Chilamkurti, N.; Sundarasekar, R. Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System. Sensors 2019, 19, 3030.

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