Ercan, Renas; Xia, Yunjia; Zhao, Yunyi; Loureiro, Rui; Yang, Shufan; Zhao, Hubin; (2024) An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection. IEEE Transactions on Very Large Scale Integration Systems 10.1109/TVLSI.2024.3356161. (In press).

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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 38 354 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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