Ercan, R., Xia, Y., Zhao, Y. et al. (3 more authors) (2024) An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 32 (4). 763 -773. ISSN 1063-8210
Abstract
Due to iterative matrix multiplications or gradient computations, machine learning modules often require a large amount of processing power and memory. As a result, they are often not feasible for use in wearable devices, which have limited processing power and memory. In this study, we propose an ultralow-power and real-time machine learning-based motion artifact detection module for functional near-infrared spectroscopy (fNIRS) systems. We achieved a high classification accuracy of 97.42%, low field-programmable gate array (FPGA) resource utilization of 38354 lookup tables and 6024 flip-flops, as well as low power consumption of 0.021 W in dynamic power. These results outperform conventional CPU support vector machine (SVM) methods and other state-of-the-art SVM implementations. This study has demonstrated that an FPGA-based fNIRS motion artifact classifier can be exploited while meeting low power and resource constraints, which are crucial in embedded hardware systems while keeping high classification accuracy.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 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. |
Keywords: | Field-programmable gate array (FPGA), functional near-infrared spectroscopy (fNIRS), low power, machine learning, motion artifact detection, real time, support vector machines (SVMs) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Medical and Biological Engineering (iMBE) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Jan 2024 12:42 |
Last Modified: | 24 Jul 2024 14:31 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TVLSI.2024.3356161 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208160 |