An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection Article Swipe
Renas Ercan
,
Yunjia Xia
,
Yunyi Zhao
,
Rui Loureiro
,
Shufan Yang
,
Hubin Zhao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tvlsi.2024.3356161
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tvlsi.2024.3356161
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tvlsi.2024.3356161
- OA Status
- green
- Cited By
- 6
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391341266
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391341266Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tvlsi.2024.3356161Digital Object Identifier
- Title
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An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts DetectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-30Full publication date if available
- Authors
-
Renas Ercan, Yunjia Xia, Yunyi Zhao, Rui Loureiro, Shufan Yang, Hubin ZhaoList of authors in order
- Landing page
-
https://doi.org/10.1109/tvlsi.2024.3356161Publisher landing page
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/11100859Direct OA link when available
- Concepts
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Computer science, Motion (physics), Artificial intelligence, Power (physics), Machine learning, Computer vision, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
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-
2025: 5, 2024: 1Per-year citation counts (last 5 years)
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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