AI-based indoor human gesture recognition using FMCW radar

Human gesture recognition is an emerging necessity of in the industry. How- ever, traditional Computer Vision, which typically uses optical sensors, often fails in this task, due to unstable light conditions and possible blockages. Radar- based sensors, however, are able to detect an object in low-l...

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Main Author: Liu, Boya
Other Authors: Wen Bihan
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/161668
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1616682022-09-14T00:04:11Z AI-based indoor human gesture recognition using FMCW radar Liu, Boya Wen Bihan School of Electrical and Electronic Engineering bihan.wen@ntu.edu.sg Engineering::Electrical and electronic engineering Human gesture recognition is an emerging necessity of in the industry. How- ever, traditional Computer Vision, which typically uses optical sensors, often fails in this task, due to unstable light conditions and possible blockages. Radar- based sensors, however, are able to detect an object in low-light conditions, as well as penetrate through blockages. Therefore, in this project, we focus on radar-based gesture recognition. We used TI’s AW1642 FMCW radar to collect data from three volunteers, per- forming 7 common gestures in an indoor laboratory setting. A preprocessing pipeline was built to generate Micro-Doppler heatmaps, which describes the mo- tion of different part of human body. These are fed into our carefully-designed deep neural networks to perform gesture classification. We are able to achieve an average accuracy of 82.13% for the 7-gesture classification. However, there remains an important need to generalize the solution, i.e., to achieve good performance on people not seen in the training set. In our ex- periments, we found the gaps in the same gestures of different people. We proposed to solve this using domain adversarial training. This allows us to ex- tract person/domain-independent features. When testing with unseen individuals, we are able to improve the accuracy by over 8% for 7-gesture classification. Keywords: radar, FMCW radar, Deep-learning, Micro-Doppler, Gesture Classifi- cation, Domain Adaptation Master of Science (Signal Processing) 2022-09-14T00:04:11Z 2022-09-14T00:04:11Z 2022 Thesis-Master by Coursework Liu, B. (2022). AI-based indoor human gesture recognition using FMCW radar. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/161668 https://hdl.handle.net/10356/161668 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Liu, Boya
AI-based indoor human gesture recognition using FMCW radar
description Human gesture recognition is an emerging necessity of in the industry. How- ever, traditional Computer Vision, which typically uses optical sensors, often fails in this task, due to unstable light conditions and possible blockages. Radar- based sensors, however, are able to detect an object in low-light conditions, as well as penetrate through blockages. Therefore, in this project, we focus on radar-based gesture recognition. We used TI’s AW1642 FMCW radar to collect data from three volunteers, per- forming 7 common gestures in an indoor laboratory setting. A preprocessing pipeline was built to generate Micro-Doppler heatmaps, which describes the mo- tion of different part of human body. These are fed into our carefully-designed deep neural networks to perform gesture classification. We are able to achieve an average accuracy of 82.13% for the 7-gesture classification. However, there remains an important need to generalize the solution, i.e., to achieve good performance on people not seen in the training set. In our ex- periments, we found the gaps in the same gestures of different people. We proposed to solve this using domain adversarial training. This allows us to ex- tract person/domain-independent features. When testing with unseen individuals, we are able to improve the accuracy by over 8% for 7-gesture classification. Keywords: radar, FMCW radar, Deep-learning, Micro-Doppler, Gesture Classifi- cation, Domain Adaptation
author2 Wen Bihan
author_facet Wen Bihan
Liu, Boya
format Thesis-Master by Coursework
author Liu, Boya
author_sort Liu, Boya
title AI-based indoor human gesture recognition using FMCW radar
title_short AI-based indoor human gesture recognition using FMCW radar
title_full AI-based indoor human gesture recognition using FMCW radar
title_fullStr AI-based indoor human gesture recognition using FMCW radar
title_full_unstemmed AI-based indoor human gesture recognition using FMCW radar
title_sort ai-based indoor human gesture recognition using fmcw radar
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/161668
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