Vision-based toddler physical activity recognition using deep learning
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...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institution of Engineering and Technology
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42025/ http://umpir.ump.edu.my/id/eprint/42025/1/Vision-based%20toddler%20physical%20activity%20recognition.pdf http://umpir.ump.edu.my/id/eprint/42025/2/Vision-based%20toddler%20physical%20activity%20recognition%20using%20deep%20learning_ABS.pdf |
| _version_ | 1848826498604072960 |
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| author | Norasyikin, Fadilah Mohd Zamri, Ibrahim Rosdiyana, Samad |
| author_facet | Norasyikin, Fadilah Mohd Zamri, Ibrahim Rosdiyana, Samad |
| author_sort | Norasyikin, Fadilah |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:45:47Z |
| format | Conference or Workshop Item |
| id | ump-42025 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:45:47Z |
| publishDate | 2022 |
| publisher | Institution of Engineering and Technology |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-420252024-09-30T04:38:49Z http://umpir.ump.edu.my/id/eprint/42025/ Vision-based toddler physical activity recognition using deep learning Norasyikin, Fadilah Mohd Zamri, Ibrahim Rosdiyana, Samad T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering 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. Institution of Engineering and Technology 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42025/1/Vision-based%20toddler%20physical%20activity%20recognition.pdf pdf en http://umpir.ump.edu.my/id/eprint/42025/2/Vision-based%20toddler%20physical%20activity%20recognition%20using%20deep%20learning_ABS.pdf 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) https://doi.org/10.1049/icp.2022.2647 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Norasyikin, Fadilah Mohd Zamri, Ibrahim Rosdiyana, Samad Vision-based toddler physical activity recognition using deep learning |
| title | Vision-based toddler physical activity recognition using deep learning |
| title_full | Vision-based toddler physical activity recognition using deep learning |
| title_fullStr | Vision-based toddler physical activity recognition using deep learning |
| title_full_unstemmed | Vision-based toddler physical activity recognition using deep learning |
| title_short | Vision-based toddler physical activity recognition using deep learning |
| title_sort | vision-based toddler physical activity recognition using deep learning |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/42025/ http://umpir.ump.edu.my/id/eprint/42025/ http://umpir.ump.edu.my/id/eprint/42025/1/Vision-based%20toddler%20physical%20activity%20recognition.pdf http://umpir.ump.edu.my/id/eprint/42025/2/Vision-based%20toddler%20physical%20activity%20recognition%20using%20deep%20learning_ABS.pdf |