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Human activities detection and classification based on micro-Doppler signatures near the baseline of forward scattering radar

Fall poses a major problem, which raises the concern of elderly populations aged 65 and above in all over the world. In this paper, we propose Forward Scattering Radar system as a Doppler sensor in distinguishing features of fall events from non-fall activities. The signal features of joint time-fre...

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Main Authors: Alnaeb, Ali, Raja Abdullah, Raja Syamsul Azmir, Salah, Asem Ahmad Mohamad, Sali, Aduwati, Abdul Rashid, Nur Emileen, Ibrahim, Idnin Pasya
Format: Conference or Workshop Item
Language:English
Published: IEEE 2018
Online Access:http://psasir.upm.edu.my/id/eprint/68213/1/Human%20activities%20detection%20and%20classification%20based%20on%20micro-Doppler%20signatures%20near%20the%20baseline%20of%20forward%20scattering%20radar.pdf
http://psasir.upm.edu.my/id/eprint/68213/
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spelling my.upm.eprints.682132019-05-09T03:39:47Z http://psasir.upm.edu.my/id/eprint/68213/ Human activities detection and classification based on micro-Doppler signatures near the baseline of forward scattering radar Alnaeb, Ali Raja Abdullah, Raja Syamsul Azmir Salah, Asem Ahmad Mohamad Sali, Aduwati Abdul Rashid, Nur Emileen Ibrahim, Idnin Pasya Fall poses a major problem, which raises the concern of elderly populations aged 65 and above in all over the world. In this paper, we propose Forward Scattering Radar system as a Doppler sensor in distinguishing features of fall events from non-fall activities. The signal features of joint time-frequency representations are used for detection, while the support vector machine, which is based on the short-time Fourier transform feature, has been used in the classification process. An indoor experiment was conducted to emulate the elderly people's daily activities and the falling down event, where 50 trials were carried out by five adults for each of the activity. The detection results indicated that the forward scattering radar has a high ability in detecting the micro-Doppler signatures generated from the low speed motion of a human body segments during daily activities. The preliminary classification results are 100% for the corresponding free fall-sitting on a chair, free fall-sitting on the floor, and for all three activities. IEEE 2018 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68213/1/Human%20activities%20detection%20and%20classification%20based%20on%20micro-Doppler%20signatures%20near%20the%20baseline%20of%20forward%20scattering%20radar.pdf Alnaeb, Ali and Raja Abdullah, Raja Syamsul Azmir and Salah, Asem Ahmad Mohamad and Sali, Aduwati and Abdul Rashid, Nur Emileen and Ibrahim, Idnin Pasya (2018) Human activities detection and classification based on micro-Doppler signatures near the baseline of forward scattering radar. In: 2018 International Conference on Radar (RADAR), 27-31 Aug. 2018, Brisbane, Queensland, Australia. . 10.1109/RADAR.2018.8557303
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Fall poses a major problem, which raises the concern of elderly populations aged 65 and above in all over the world. In this paper, we propose Forward Scattering Radar system as a Doppler sensor in distinguishing features of fall events from non-fall activities. The signal features of joint time-frequency representations are used for detection, while the support vector machine, which is based on the short-time Fourier transform feature, has been used in the classification process. An indoor experiment was conducted to emulate the elderly people's daily activities and the falling down event, where 50 trials were carried out by five adults for each of the activity. The detection results indicated that the forward scattering radar has a high ability in detecting the micro-Doppler signatures generated from the low speed motion of a human body segments during daily activities. The preliminary classification results are 100% for the corresponding free fall-sitting on a chair, free fall-sitting on the floor, and for all three activities.
format Conference or Workshop Item
author Alnaeb, Ali
Raja Abdullah, Raja Syamsul Azmir
Salah, Asem Ahmad Mohamad
Sali, Aduwati
Abdul Rashid, Nur Emileen
Ibrahim, Idnin Pasya
spellingShingle Alnaeb, Ali
Raja Abdullah, Raja Syamsul Azmir
Salah, Asem Ahmad Mohamad
Sali, Aduwati
Abdul Rashid, Nur Emileen
Ibrahim, Idnin Pasya
Human activities detection and classification based on micro-Doppler signatures near the baseline of forward scattering radar
author_facet Alnaeb, Ali
Raja Abdullah, Raja Syamsul Azmir
Salah, Asem Ahmad Mohamad
Sali, Aduwati
Abdul Rashid, Nur Emileen
Ibrahim, Idnin Pasya
author_sort Alnaeb, Ali
title Human activities detection and classification based on micro-Doppler signatures near the baseline of forward scattering radar
title_short Human activities detection and classification based on micro-Doppler signatures near the baseline of forward scattering radar
title_full Human activities detection and classification based on micro-Doppler signatures near the baseline of forward scattering radar
title_fullStr Human activities detection and classification based on micro-Doppler signatures near the baseline of forward scattering radar
title_full_unstemmed Human activities detection and classification based on micro-Doppler signatures near the baseline of forward scattering radar
title_sort human activities detection and classification based on micro-doppler signatures near the baseline of forward scattering radar
publisher IEEE
publishDate 2018
url http://psasir.upm.edu.my/id/eprint/68213/1/Human%20activities%20detection%20and%20classification%20based%20on%20micro-Doppler%20signatures%20near%20the%20baseline%20of%20forward%20scattering%20radar.pdf
http://psasir.upm.edu.my/id/eprint/68213/
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score 13.211869

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