Prediction of harvestable energy for self-powered wearable healthcare devices: filling a gap

Wahba, M. A., Ashour, A. S. and Ghannam, R. (2020) Prediction of harvestable energy for self-powered wearable healthcare devices: filling a gap. IEEE Access, 8, pp. 170336-170354. (doi: 10.1109/ACCESS.2020.3024167)

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Abstract

Self-powered or autonomously driven wearable devices are touted to revolutionize the personalized healthcare industry, promising sustainable medical care for a large population of healthcare seekers. Current wearable devices rely on batteries for providing the necessary energy to the various electronic components. However, to ensure continuous and uninterrupted operation, these wearable devices need to scavenge energy from their surroundings. Different energy sources have been used to power wearable devices. These include predictable energy sources such as solar energy and radio frequency, as well as unpredictable energy from the human body. Nevertheless, these energy sources are either intermittent or deliver low power densities. Therefore, being able to predict or forecast the amount of harvestable energy over time enables the wearable to intelligently manage and plan its own energy resources more effectively. Several prediction approaches have been proposed in the context of energy harvesting wireless sensor network (EH-WSN) nodes. In their architectural design, these nodes are very similar to self-powered wearable devices. However, additional factors need to be considered to ensure a deeper market penetration of truly autonomous wearables for healthcare applications, which include low-cost, low-power, small-size, high-performance and lightweight. In this paper, we review the energy prediction approaches that were originally proposed for EH-WSN nodes and critique their application in wearable healthcare devices. Our comparison is based on their prediction accuracy, memory requirement, and execution time. We conclude that statistical techniques are better designed to meet the needs of short-term predictions, while long-term predictions require the hybridization of several linear and non-linear machine learning techniques. In addition to the recommendations, we discuss the challenges and future perspectives of these technique in our review.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Ghannam, Dr Rami
Authors: Wahba, M. A., Ashour, A. S., and Ghannam, R.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:IEEE Access
Publisher:IEEE
ISSN:2169-3536
ISSN (Online):2169-3536
Published Online:15 September 2020
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in IEEE Access 8: 170336-170354
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
304896EPSRC-IAA: Early Stage Commercialisation of a PET Imaging Agent for the Detection of Cardiovascular Disease and CancerAndrew SutherlandEngineering and Physical Sciences Research Council (EPSRC)EP/R511705/1Chemistry