An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection
An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection is a scholarly work, published in 2024 in ''IEEE Transactions on Very Large Scale Integration Systems''. The main subjects of the publication include power, artificial intelligence, computer vision, time-of-flight camera, remote patient monitoring, motion, biomedical engineering, machine learning, and computer science. The 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.