FPL Demo: A Learning-Based Motion Artefact Detector for Heterogeneous Platforms

被引：0

作者：

Zhao, Yunyi ^{[1
]}

Xia, Yunjia ^{[1
]}

Loureiro, Rui ^{[1
]}

Zhao, Hubin ^{[1
]}

Dolinsky, Uwe ^{[2
]}

Yang, Shufan ^{[3
]}

机构：

[1] Univ Coll London, Stanmore HA7 4LP, Middx, England

[2] Codeplay Software Ltd, Edinburgh, Midlothian, Scotland

[3] Edinburgh Napier Univ, Edinburgh, Midlothian, Scotland

来源：

2023 33RD INTERNATIONAL CONFERENCE ON FIELD-PROGRAMMABLE LOGIC AND APPLICATIONS, FPL | 2023年

关键词：

fNIRS; Deep Learning; Machine Learning; Motion Artifact; FPGA;

D O I：

10.1109/FPL60245.2023.00071

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 learningbased motion artefact detector in a 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.

引用

页码：366 / 366

页数：1