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

Gunasekaran Manogaran¹, P Mohamed Shakeel², H Fouad³

¹University of California Davis, Davis, CA 95616, USA. gunasekaranmanogaran@outlook.com.

Sensors (Basel, Switzerland)

|July 21, 2019

Summary

A new wearable smart-log patch uses Internet of Things (IoT) sensors and edge computing with Bayesian deep learning to accurately monitor physical activities and health conditions. This system offers efficient, low-energy health checking for various age groups.

Keywords:

Bayesian neural network edge computing multi access physical monitoring system multimedia technology smart-log patch

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Area of Science:

Biomedical Engineering
Health Informatics
Wearable Technology

Background:

Nutrient deficiencies cause organ deterioration and health issues in infants, children, and adults.
Continuous monitoring of physical activities is crucial for children and adolescents' well-being.
Real-time health condition diagnosis and information requirements pose challenges in current multi-access physical monitoring systems.

Purpose of the Study:

To design and develop a wearable smart-log patch with Internet of Things (IoT) sensors and multimedia technology.
To analyze data computation using edge computing on a Bayesian deep learning network (EC-BDLN) for accurate physical data inference.
To evaluate the efficiency of the wearable IoT system in terms of accuracy, efficiency, error, delay, and energy consumption.

Main Methods:

Development of a wearable smart-log patch integrated with IoT sensors and multimedia technology.
Application of edge computing on a Bayesian deep learning network (EC-BDLN) for analyzing collected human physical data.
Experimental evaluation of the system's performance metrics including accuracy, efficiency, mean residual error, delay, and energy consumption.

Main Results:

The developed wearable IoT system demonstrates accurate inference and identification of human physical data.
Experimental results show high accuracy, efficiency, and low energy consumption for the smart-log patch.
The system effectively monitors physical activities, contributing to improved health checking.

Conclusions:

The smart-log patch represents an evolutionary advancement in multi-access physical monitoring systems.
The integration of IoT, multimedia technology, and EC-BDLN offers a robust solution for real-time health monitoring.
This research provides a foundation for enhanced health checking, particularly for vulnerable populations.