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Article

Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection

1
State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, China
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China and Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin 300072, China
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School of life sciences, Tiangong University, Tianjin 300387, China
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Industrial Engineering Department, College of Engineering, King Saud University, PO Box 800, Riyadh 11421, Saudi Arabia
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Robotics and Internet-of-Things Lab, Prince Sultan University, Riyadh 11586, Saudi Arabia
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CISTER, INESC-TEC, ISEP, Polytechnic Institute of Porto, 4249-015 Porto, Portugal
*
Authors to whom correspondence should be addressed.
Electronics 2020, 9(12), 2015; https://doi.org/10.3390/electronics9122015
Received: 26 October 2020 / Revised: 20 November 2020 / Accepted: 24 November 2020 / Published: 28 November 2020
(This article belongs to the Special Issue Computational Intelligence in Healthcare)
Wearable technology plays a key role in smart healthcare applications. Detection and analysis of the physiological data from wearable devices is an essential process in smart healthcare. Physiological data analysis is performed in fog computing to abridge the excess latency introduced by cloud computing. However, the latency for the emergency health status and overloading in fog environment becomes key challenges for smart healthcare. This paper resolves these problems by presenting a novel tri-fog health architecture for physiological parameter detection. The overall system is built upon three layers as wearable layer, intelligent fog layer, and cloud layer. In the first layer, data from the wearable of patients are subjected to fault detection at personal data assistant (PDA). To eliminate fault data, we present the rapid kernel principal component analysis (RK-PCA) algorithm. Then, the faultless data is validated, whether it is duplicate or not, by the data on-looker node in the second layer. To remove data redundancy, we propose a new fuzzy assisted objective optimization by ratio analysis (FaMOORA) algorithm. To timely predict the user’s health status, we enable the two-level health hidden Markov model (2L-2HMM) that finds the user’s health status from temporal variations in data collected from wearable devices. Finally, the user’s health status is detected in the fog layer with the assist of a hybrid machine learning algorithm, namely SpikQ-Net, based on the three major categories of attributes such as behavioral, biomedical, and environment. Upon the user’s health status, the immediate action is taken by both cloud and fog layers. To ensure lower response time and timely service, we also present an optimal health off procedure with the aid of the multi-objective spotted hyena optimization (MoSHO) algorithm. The health off method allows offloading between overloaded and underloaded fog nodes. The proposed tri-fog health model is validated by a thorough simulation performed in the iFogSim tool. It shows better achievements in latency (reduced up to 3 ms), execution time (reduced up to 1.7 ms), detection accuracy (improved up to 97%), and system stability (improved up to 96%). View Full-Text
Keywords: Tri-Fog Health System; fault data elimination; health status prediction; health status detection; health off Tri-Fog Health System; fault data elimination; health status prediction; health status detection; health off
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MDPI and ACS Style

Ijaz, M.; Li, G.; Wang, H.; El-Sherbeeny, A.M.; Moro Awelisah, Y.; Lin, L.; Koubaa, A.; Noor, A. Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection. Electronics 2020, 9, 2015. https://doi.org/10.3390/electronics9122015

AMA Style

Ijaz M, Li G, Wang H, El-Sherbeeny AM, Moro Awelisah Y, Lin L, Koubaa A, Noor A. Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection. Electronics. 2020; 9(12):2015. https://doi.org/10.3390/electronics9122015

Chicago/Turabian Style

Ijaz, Muhammad, Gang Li, Huiquan Wang, Ahmed M. El-Sherbeeny, Yussif Moro Awelisah, Ling Lin, Anis Koubaa, and Alam Noor. 2020. "Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection" Electronics 9, no. 12: 2015. https://doi.org/10.3390/electronics9122015

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