Human Activity Recognition Using Graph Structures and Deep Neural Networks
Human activity recognition (HAR) systems are essential in healthcare, surveillance, and sports analytics, enabling automated movement analysis. This research presents a novel HAR system combining graph structures with deep neural networks to capture both spatial and temporal patterns in activities....
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/1/9 |
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| author | Abed Al Raoof K. Bsoul |
| author_facet | Abed Al Raoof K. Bsoul |
| author_sort | Abed Al Raoof K. Bsoul |
| collection | DOAJ |
| description | Human activity recognition (HAR) systems are essential in healthcare, surveillance, and sports analytics, enabling automated movement analysis. This research presents a novel HAR system combining graph structures with deep neural networks to capture both spatial and temporal patterns in activities. While CNN-based models excel at spatial feature extraction, they struggle with temporal dynamics, limiting their ability to classify complex actions. To address this, we applied the Firefly Optimization Algorithm to fine-tune the hyperparameters of both the graph-based model and a CNN baseline for comparison. The optimized graph-based system, evaluated on the UCF101 and Kinetics-400 datasets, achieved 88.9% accuracy with balanced precision, recall, and F1-scores, outperforming the baseline. It demonstrated robustness across diverse activities, including sports, household routines, and musical performances. This study highlights the potential of graph-based HAR systems for real-world applications, with future work focused on multi-modal data integration and improved handling of occlusions to enhance adaptability and performance. |
| format | Article |
| id | doaj-art-1c28fcb1167843169a91f397ace1d5c4 |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-1c28fcb1167843169a91f397ace1d5c42025-01-24T13:27:51ZengMDPI AGComputers2073-431X2024-12-01141910.3390/computers14010009Human Activity Recognition Using Graph Structures and Deep Neural NetworksAbed Al Raoof K. Bsoul0Collage of Computer Science and Information Technology, Yarmouk University, Irbid 21163, JordanHuman activity recognition (HAR) systems are essential in healthcare, surveillance, and sports analytics, enabling automated movement analysis. This research presents a novel HAR system combining graph structures with deep neural networks to capture both spatial and temporal patterns in activities. While CNN-based models excel at spatial feature extraction, they struggle with temporal dynamics, limiting their ability to classify complex actions. To address this, we applied the Firefly Optimization Algorithm to fine-tune the hyperparameters of both the graph-based model and a CNN baseline for comparison. The optimized graph-based system, evaluated on the UCF101 and Kinetics-400 datasets, achieved 88.9% accuracy with balanced precision, recall, and F1-scores, outperforming the baseline. It demonstrated robustness across diverse activities, including sports, household routines, and musical performances. This study highlights the potential of graph-based HAR systems for real-world applications, with future work focused on multi-modal data integration and improved handling of occlusions to enhance adaptability and performance.https://www.mdpi.com/2073-431X/14/1/9human activity recognition (HAR)deep learningfirefly optimization algorithmgraph-based modelsspatial–temporal analysis |
| spellingShingle | Abed Al Raoof K. Bsoul Human Activity Recognition Using Graph Structures and Deep Neural Networks Computers human activity recognition (HAR) deep learning firefly optimization algorithm graph-based models spatial–temporal analysis |
| title | Human Activity Recognition Using Graph Structures and Deep Neural Networks |
| title_full | Human Activity Recognition Using Graph Structures and Deep Neural Networks |
| title_fullStr | Human Activity Recognition Using Graph Structures and Deep Neural Networks |
| title_full_unstemmed | Human Activity Recognition Using Graph Structures and Deep Neural Networks |
| title_short | Human Activity Recognition Using Graph Structures and Deep Neural Networks |
| title_sort | human activity recognition using graph structures and deep neural networks |
| topic | human activity recognition (HAR) deep learning firefly optimization algorithm graph-based models spatial–temporal analysis |
| url | https://www.mdpi.com/2073-431X/14/1/9 |
| work_keys_str_mv | AT abedalraoofkbsoul humanactivityrecognitionusinggraphstructuresanddeepneuralnetworks |