Poster abstract: Trade-off Analysis of Inference Accuracy and Resource Usage for Energy-Positive Activity Recognition
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Accepted Version
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106019059 - poster abstract. Available under License Creative Commons Attribution Non-commercial 4.0. |
Description
Energy-positive activity recognition classifies human activities, including walking, running, and sitting, while harvesting kinetic energy from such activities. In this setting, the device's lifetime de-pends on the user's activity profile and the resources needed to run inference to classify activities. Thus, the selection of machine learning classification models for energy-positive activity recognition must consider both model's classification accuracy and energy con-sumption compared to the harvested energy from human activities. In this paper, we study the trade-off between accuracy and resource usage of a neural network model when different feature extraction techniques are used. Our results indicate that an on-board sched-uling algorithm can be used to dynamically switch between the optimal feature input tuned for accuracy and energy consumption.
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ID Code: | 228444 | ||||
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Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||
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Measurements or Duration: | 2 pages | ||||
Keywords: | activity recognition, batteryless IoT, deep learning, energy-positive | ||||
DOI: | 10.1109/IPSN54338.2022.00072 | ||||
ISBN: | 978-1-6654-9625-4 | ||||
Pure ID: | 106019059 | ||||
Divisions: | Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Computer Science Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Electrical Engineering & Robotics |
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Copyright Owner: | Consult author(s) regarding copyright matters | ||||
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Deposited On: | 01 Mar 2022 04:29 | ||||
Last Modified: | 08 Aug 2024 00:43 |
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