A Survey of Computer Vision-Based Fall Detection and Technology Perspectives
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-2385-4_45
Reference45 articles.
1. WHO global report on falls prevention in older age (2007)
2. Gutierrez, J., Rodriguez, V., Martin, S.: Comprehensive review of vision-based fall detection systems. Sens.-Basel 21(3) (2021)
3. Chen, Z.J., Wang, Y.: Infrared-ultrasonic sensor fusion for support vector machine-based fall detection. J. Intell. Mater. Syst. Struct. 29(9), 2027–2039 (2018)
4. Msaad, S., Cormier, G., Carrault, G.: Detecting falls and estimation of daily habits with depth images using machine learning algorithms. In: 42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Montreal, Canada, pp. 2163–2166. IEEE (2020)
5. Yodpijit, N., Sittiwanchai, T., Jongprasithporn, M.: The development of artificial neural networks (ANN) for falls detection. In: 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), pp. 547–50. IEEE (2017)
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