Vision-based toddler physical activity recognition using deep learning

Norasyikin, Fadilah and Mohd Zamri, Ibrahim and Rosdiyana, Samad (2022) Vision-based toddler physical activity recognition using deep learning. In: IET Conference Proceedings. 2022 Engineering Technology International Conference, ETIC 2022 , 7 - 8 September 2022 , Kuantan, Virtual. pp. 377-383., 2022 (22). ISSN 2732-4494 ISBN 978-183953782-0 (Published)

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Abstract

Human activity recognition (HAR) is a system for understanding human movements and behaviour. It has been applied in many fields such as video surveillance, behaviour analysis, and human-computer interaction. The state-of-the-art studies on HAR generally focus their attention on public dataset which mostly consist of adults as their subjects. Research on HAR for children especially toddlers is important to facilitate their surveillance by monitoring their activities automatically. Since toddlers possess different anatomical proportions than adults, their unusual movements can be a challenge to infer. In this paper, a vision-based deep learning HAR system for toddlers was developed based on skeleton features. Videos of toddlers' activities in a day-care were obtained through different public sources. 2D skeleton data were then extracted from every frame of these videos using a pre-trained deep learning network. These skeleton data were trained on LSTM and fully connected network to infer the toddler's activities. Results showed that this proposed framework managed to achieve 75% accuracies for three toddlers' activities which are jumping, sitting, and standing.

Item Type:	Conference or Workshop Item (Keynote)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	2D skeleton; Activity recognition; Deep learning; LSTM
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	30 Sep 2024 04:38
Last Modified:	30 Sep 2024 04:38
URI:	http://umpir.ump.edu.my/id/eprint/42025
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