Open Access
Issue |
E3S Web Conf.
Volume 605, 2025
The 9th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2024)
|
|
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Article Number | 03051 | |
Number of page(s) | 9 | |
Section | Environment | |
DOI | https://doi.org/10.1051/e3sconf/202560503051 | |
Published online | 17 January 2025 |
- P. T. Huong, L. T. Hien, N. M. Son, and T. Q. Nguyen, “Deep learning application in fall detection using image recognition based on models trained from LH_Dataset and UM_Dataset,” (2024) [Google Scholar]
- F. G. D. Duarte, L. R. de Oliveira, F. N. Melanda, and F. S. de Carlo, Fisioterapia em Movimento, 37 (2024) [Google Scholar]
- S. Ahn, J. Kim, B. Koo, and Y. Kim, Sensors (Switzerland), 19, 4, Feb (2019) [Google Scholar]
- S. Juraev, A. Ghimire, J. Alikhanov, V. Kakani, and H. Kim, “Exploring Human Pose Estimation and the Usage of Synthetic Data for Elderly Fall Detection in Real-World Surveillance,” IEEE Access, 10, 94249–94261 (2022) [CrossRef] [Google Scholar]
- C. Fernandes, S. Miles, and C. J. P. Lucena, JMIR Med Inform, 8, 5, May (2020) [Google Scholar]
- T.H. Nguyen, and V.T. Nguyen, JST: Smart Systems and Devices, 34, 2, 35–43, May (2024) [Google Scholar]
- J. R. Lim, B. F. Liu, and M. Egnoto, Weather, Climate, and Society, 11, 3, 549–563, (2019) [CrossRef] [Google Scholar]
- E. al. Saranya, International Journal on Recent and Innovation Trends in Computing and Communication, 11, 10, 397–405, Nov. (2023) [CrossRef] [Google Scholar]
- M. Islam et al., “Deep Learning Based Systems Developed for Fall Detection: A Review,” Institute of Electrical and Electronics Engineers Inc. (2020) [Google Scholar]
- M. Zerkouk and B. Chikhaoui, “patio-temporal abnormal behavior prediction in elderly persons using deep learning models,” Sensors (Switzerland), 20, 8, Apr. (2020) [Google Scholar]
- E. al. Saranya, International Journal on Recent and Innovation Trends in Computing and Communication, 11, 10, 397–405, Nov. (2023) [CrossRef] [Google Scholar]
- M. Islam et al., “Deep Learning Based Systems Developed for Fall Detection: A Review,” Institute of Electrical and Electronics Engineers Inc. (2020) [Google Scholar]
- M. Kchouri, N. Harum, A. Obeid, H. Hazimeh, F. T. Maklumat, and D. Komunikasi, “Fuzzy Support Vector Machine based Fall Detection Method for Traumatic Brain Injuries A New Systematic Approach of Combining Fuzzy Logic with Support Vector Machine to Achieve Higher Accuracy in Fall Detection System.” [Online]. Available: www.ijacsa.thesai.org [Google Scholar]
- X. Liu, Applied and Computational Engineering, 51, 1, 225–230, Mar. (2024) [CrossRef] [Google Scholar]
- C. N. Noviyanti and A. Alamsyah, Journal of Information System Exploration and Research, 2, 1, Jan. (2024) [CrossRef] [Google Scholar]
- M. M. Zaheer and P. Nirmala, “An Efficient Approach to Detect Liver Disorder Using Customised SVM in Comparison with Random Forest Algorithm to Measure Accuracy,” CARDIOMETRY, no. 25, pp. 1024–1030, Feb. (2023) [CrossRef] [Google Scholar]
- A. Zakaria, R. R. Isnanto, and O. D. Nurhayati, Int J Comput Appl, 182, 18, 9–13, Sep. (2018) [Google Scholar]
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