A hybrid accuracy- and energy-aware human activity recognition model in IoT environment

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A hybrid accuracy- and energy-aware human activity recognition model in IoT environment
Title:
A hybrid accuracy- and energy-aware human activity recognition model in IoT environment
Other Titles:
IEEE Transactions on Sustainable Computing
Publication Date:
23 September 2022
Citation:
Jha, D. N., Chen, Z., Liu, S., Wu, M., Zhang, J., Morgan, G., Ranjan, R., & Li, X. (2022). A hybrid accuracy- and energy-aware human activity recognition model in IoT environment. IEEE Transactions on Sustainable Computing, 1–13. https://doi.org/10.1109/tsusc.2022.3209086
Abstract:
Personalised health and fitness provide users with information regarding their wellbeing and an opportunity to inform healthcare services for better patient outcomes. Underpinning this industry sector is the need to establish human activity recognition (HAR) in a ubiquitous manner. For example, through the use of smartwatches and/or mobile phones gathering information such as heart rates, movement, and steps of a user. The engineering challenge is providing accurate, informative, and timely data without rapidly depleting the mobile device's battery life. This problem is compounded as a number of algorithms used to process such data require substantial, cloud-based resources, to achieve higher accuracy. Therefore, a balance is required between battery depletion, accuracy of data, and timely delivery of results through a mixture of cloud and local algorithmic execution. In this paper, we propose AE-HAR (Accuracy and Energy Aware-HAR) model that delivers engineered solutions which approach optimal combinations in the consideration of energy consumption, accuracy, and timeliness of results. AE-HAR introduces a “light-weight” machine learning on-device component identifying the probabilistic accuracy of data together with energy consumption identification requirements. A heuristic is then adopted to determine if cloud-enabled calculations are required while including possible performance costs related to the analysis of networking infrastructures. Our model is validated in a real-world environment through experimentation that demonstrates accuracy in excess of 93% and energy consumption savings in excess of 94%.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - AI Singapore Research Programme
Grant Reference no. : AISG2-RP-2021-027

This project is partially supported by the following UK Research and Innovation council projects including: (i) SUPER, EP/T021985/1 and (ii) Secure Internet of Energy, EP/W003325/1.
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2377-3782
2377-3790
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