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


Citation

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. .

Abstract / Synopsis

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.


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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.1109/RADAR.2018.8557303
Publisher: IEEE
Keywords: Elderly fall detection; Forward scattering radar; Time-frequency domain analysis; Support vector machine; Classification
Depositing User: Nabilah Mustapa
Date Deposited: 09 May 2019 11:39
Last Modified: 09 May 2019 11:39
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/RADAR.2018.8557303
URI: http://psasir.upm.edu.my/id/eprint/68213
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