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activity recognition: A comprehensive survey, Pattern Recognition, Volume 108, 2020, 107561, ISSN 0031-3203,
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neural networks. Applied Sciences, 14(5), Article 2107. https://doi.org/10.3390/app14052107
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11. Wenbin Gao, Lei Zhang, Qi Teng, Jun He, Hao Wu, DanHAR: Dual Attention Network for multimodal human activity recognition using
wearable sensors, Applied Soft Computing, Volume 111, 2021, 107728, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2021.107728.
12. Nguyen, N., Nguyen, T. T., Pham, M. N., & Tran, Q. D. (2023). Improving human activity classification based on micro-Doppler signatures
separation of FMCW radar. In 2023 International Conference on Computing, Communication and Control Applications (ICCAIS) (pp. 454–
459). IEEE. https://doi.org/10.1109/ICCAIS59597.2023.10382332
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Sensors, 23(17), Article 7486. https://doi.org/10.3390/s23177486
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human activity recognition. Pervasive and Mobile Computing, 96, Article 101850. https://doi.org/10.1016/j.pmcj.2023.101850
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16. Flores-Castañeda, R. O., Olaya-Cotera, S., & Iparraguirre-Villanueva, O. (2025). Exploring wearable technologies for health monitoring: A
systematic review of applications, advantages and disadvantages. Neural Computing and Applications, 37, 27957–27983.
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17. Host, K., & Ivašić-Kos, M. (2022). An overview of human action recognition in sports based on computer vision. Heliyon, 8(6), Article
e09633. https://doi.org/10.1016/j.heliyon.2022.e09633
18. Waghchaware, S., & Joshi, R. (2024). Machine learning and deep learning models for human activity recognition in security and surveillance:
A review. Knowledge and Information Systems, 66(8), 4405–4436. https://doi.org/10.1007/s10115-024-02122-6
19. Hussain, S., Saeed, K., Baimagambetov, A., Rab, S., & Saad, M. (2024). Advancements in gesture recognition techniques and machine
learning for enhanced human-robot interaction: A comprehensive review. arXiv preprint arXiv:2409.06503. https://arxiv.org/abs/2409.06503
20. Shin, J., Hassan, N., Miah, A. S. M., & Nishimura, S. (2025). A comprehensive methodological survey of human activity recognition across
diverse data modalities. Sensors, 25(13), 4028. https://doi.org/10.3390/s25134028
21. Jeyakumar, J. V., Sarker, A., Garcia, L. A., & Srivastava, M. (2023). X-CHAR: A concept-based explainable complex human activity
recognition model. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 7(1), Article 17.
https://doi.org/10.1145/3580804
22. Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019). Deep learning for sensor-based activity recognition: A survey. Pattern Recognition
Letters, 119(Special Issue), 3–11. https://doi.org/10.1016/j.patrec.2018.02.010
23. Zhang, L., Cui, W., Li, B., Chen, Z., Wu, M., & Gee, T. S. (2023). Privacy-preserving cross-environment human activity recognition. IEEE
Transactions on Cybernetics, 53(3), 1765–1775. https://doi.org/10.1109/TCYB.2021.3126831