Browse by author
Lookup NU author(s): Professor Graham MorganORCiD,
Professor Raj Ranjan
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
IEEE. 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%.
Author(s): Jha DN, Chen Z, Liu S, Wu M, Zhang J, Morgan G, Ranjan R, Li X
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Sustainable Computing
Print publication date: 01/01/2023
Online publication date: 23/09/2022
Acceptance date: 02/04/2018
ISSN (electronic): 2377-3782
Altmetrics provided by Altmetric