A Comprehensive Study of Pose Estimation in Human Fall Detection
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-1142-2_31
Reference36 articles.
1. Rastogi, S., & Singh, J. (2021). A systematic review on machine learning for fall detection system. Computational Intelligence.
2. Núñez-Marcos, A., Azkune, G., & Arganda-Carreras, I. (2017). Vision-based fall detection with convolutional neural networks. In Wireless communications and mobile computing.
3. Ambrose, A. F., Paul, G., & Hausdorff, J. M. (2013). Risk factors for falls among older adults: A review of the literature. Maturitas, 75(1), 51–61.
4. Gates, S., Fisher, J. D., Cooke, M. W., Carter, Y. H., & Lamb, S. E. (2008). Multifactorial assessment and targeted intervention for preventing falls and injuries among older people in community and emergency care settings: Systematic review and meta-analysis. BMJ, 336(7636), 130–133.
5. Vallabh, P., & Malekian, R. (2018). Fall detection monitoring systems: A comprehensive review. Journal of Ambient Intelligence and Humanized Computing, 9(6), 1809–1833.
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