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Original Article
Human Activity Recognition Using Deep Learning and GUI-Based Prediction Tool
Syed Hyder Ali1
Syeda Mahvish2
1Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Assistant professor, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: September-October 2025
Pages: 25-30
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20250505005
References
1. Y. Chen and Y. Xue, “A deep learning approach to human activity recognition based on single accelerometer,” 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1488–1492, 2015.
2. J. Wang, Y. Chen, S. Hao, X. Peng, and L. Hu, “Deep learning for sensor-based activity recognition: A survey,” Pattern Recognition Letters, vol. 119, pp. 3–11, 2019.
3. A. Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” International Conference on Learning Representations (ICLR), 2021.
4. Vaswani et al., “Attention is all you need,” Advances in Neural Information Processing Systems (NeurIPS), vol. 30, pp. 5998–6008, 2017.
5. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.
6. M. Tan and Q. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” Proceedings of the 36th International Conference on Machine Learning (ICML), pp. 6105–6114, 2019.
7. M. Abdu-Aguye et al., “Transformer-based deep learning approaches for human activity recognition: A review,” Sensors, vol. 22, no. 19, p. 7350, 2022.
8. F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
9. Riverbank Computing, “PyQt5 Documentation,” 2023. [Online]. Available: https://www.riverbankcomputing.com/software/pyqt/intro
10. J. D. Hunter, “Matplotlib: A 2D graphics environment,” Computing in Science & Engineering, vol. 9, no. 3, pp. 90–95, 2007.
2. J. Wang, Y. Chen, S. Hao, X. Peng, and L. Hu, “Deep learning for sensor-based activity recognition: A survey,” Pattern Recognition Letters, vol. 119, pp. 3–11, 2019.
3. A. Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” International Conference on Learning Representations (ICLR), 2021.
4. Vaswani et al., “Attention is all you need,” Advances in Neural Information Processing Systems (NeurIPS), vol. 30, pp. 5998–6008, 2017.
5. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.
6. M. Tan and Q. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” Proceedings of the 36th International Conference on Machine Learning (ICML), pp. 6105–6114, 2019.
7. M. Abdu-Aguye et al., “Transformer-based deep learning approaches for human activity recognition: A review,” Sensors, vol. 22, no. 19, p. 7350, 2022.
8. F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
9. Riverbank Computing, “PyQt5 Documentation,” 2023. [Online]. Available: https://www.riverbankcomputing.com/software/pyqt/intro
10. J. D. Hunter, “Matplotlib: A 2D graphics environment,” Computing in Science & Engineering, vol. 9, no. 3, pp. 90–95, 2007.
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