Yunyi Zhao
FPL Demo: A Learning-Based Motion Artefact Detector for Heterogeneous Platforms
Zhao, Yunyi; Xia, Yunjia; Loureiro, Rui; Zhao, Hubin; Dolinsky, Uwe; Yang, Shufan
Authors
Yunjia Xia
Rui Loureiro
Hubin Zhao
Uwe Dolinsky
Shufan Yang
Abstract
This demonstration showcases a novel FPGA development pipeline for developing a low-power and real-time motion artefact detection module for a wearable functional near-Infrared spectroscopy (fNIRS) processing system. We provide a brief overview of the development design flow for our learning-based motion artefact detector in heterogeneous platform, as well as the evaluation method for removing motion artefacts, which are unwanted signal variations that occur due to subject motion during data acquisition.
Citation
Zhao, Y., Xia, Y., Loureiro, R., Zhao, H., Dolinsky, U., & Yang, S. (2023, September). FPL Demo: A Learning-Based Motion Artefact Detector for Heterogeneous Platforms. Poster presented at FPL 2023: 33rd International Conference on Field-Programmable Logic and Applications, Gothenburg, Sweden
| Presentation Conference Type | Poster |
|---|---|
| Conference Name | FPL 2023: 33rd International Conference on Field-Programmable Logic and Applications |
| Start Date | Sep 4, 2023 |
| End Date | Sep 8, 2023 |
| Deposit Date | Jun 26, 2023 |
| Keywords | fNIRS, Deep Learning, Machine Learning, Motion Artifact, FPGA |
| Public URL | http://researchrepository.napier.ac.uk/Output/3133953 |
| Publisher URL | https://2023.fpl.org/ |