Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

Published in: Wireless Personal Communications 2/2022

28-06-2022

A Comprehensive Survey of Various Approaches on Human Fall Detection for Elderly People

Authors: Rohit Parmar, Samir Trapasiya

Published in: Wireless Personal Communications | Issue 2/2022

Login to get access
share
SHARE

Abstract

With the advancement in the healthcare and medicine sector, now a day’s average life span of humans has increased. Due to an increase in average life expectancy, the demographic of old age people has increased. According to a World Health Organization report, old age people have more chances to get with fall and recurrent fall (World Health Organization in Who global report on falls prevention in older age, 2007). For elder people, human falls may create severe medical issues and injuries too. Because of the ever-growing old age people, there is an urgent requirement for the development of fall detection systems. Fortunately, with the help of advanced biomedical wireless sensor networks, the internet of things, Microelectromechanical sensors, and human–computer interaction it is possible to address this issue of human fall detection. In this research article, we have presented a survey on human fall detection methods and Systems. Human fall detection can be developed using one of the following ways: vision-based techniques, ambient sensor-based techniques, and wearable device-based techniques. In this review article, we have presented a brief review of the above-mentioned methods. Various machine learning methods for fall detection and activity of daily life have been discussed rigorously in this article with available literature.
Literature
11.
go back to reference Chin, Z. H., Ng, H., Yap, T. T. V., Tong, H. L., Ho, C. C., & Goh, V. T. (2019). Daily activities classification on human motion primitives detection dataset. In R. Alfred, Y. Lim, A. Ibrahim, P. Anthony (Eds.), Computational science and technology. Lecture notes in electrical engineering (vol. 481). Springer. https://​doi.​org/​10.​1007/​978-981-13-2622-6(12) Chin, Z. H., Ng, H., Yap, T. T. V., Tong, H. L., Ho, C. C., & Goh, V. T. (2019). Daily activities classification on human motion primitives detection dataset. In R. Alfred, Y. Lim, A. Ibrahim, P. Anthony (Eds.), Computational science and technology. Lecture notes in electrical engineering (vol. 481). Springer. https://​doi.​org/​10.​1007/​978-981-13-2622-6(12)
17.
go back to reference Nizam, Y., Haji Mohd, M. N., & Abdul Jamil, M. M. (2016). A study on human fall detection systems: Daily activity classification and sensing techniques. International Journal of Integrated Engineering, 8(1), 66. Nizam, Y., Haji Mohd, M. N., & Abdul Jamil, M. M. (2016). A study on human fall detection systems: Daily activity classification and sensing techniques. International Journal of Integrated Engineering, 8(1), 66.
37.
go back to reference Gunale, K. G., & Mukherji, P. (2018). Indoor human fall detection system based on automatic vision using computer vision and machine learning algorithms. Journal of Engineering Science and Technology, 13(8), 2587–2605. Gunale, K. G., & Mukherji, P. (2018). Indoor human fall detection system based on automatic vision using computer vision and machine learning algorithms. Journal of Engineering Science and Technology, 13(8), 2587–2605.
57.
go back to reference Auvinet, E., Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (2010). Multiple cameras fall dataset, Technical report 1350, DIRO—Universitè de Montrèal. Auvinet, E., Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (2010). Multiple cameras fall dataset, Technical report 1350, DIRO—Universitè de Montrèal.
64.
go back to reference Frank, K., Vera Nadales, M. J., Robertson, P., & Pfeifer, T. Bayesian recognition of motion related activities with inertial sensors. In Proceedings of the 12th ACM international conference adjunct papers on ubiquitous computing-adjunct; Copenhagen, Denmark, 26–29 September 2010 (pp. 445–446). https://​doi.​org/​10.​1145/​1864431.​1864480 Frank, K., Vera Nadales, M. J., Robertson, P., & Pfeifer, T. Bayesian recognition of motion related activities with inertial sensors. In Proceedings of the 12th ACM international conference adjunct papers on ubiquitous computing-adjunct; Copenhagen, Denmark, 26–29 September 2010 (pp. 445–446). https://​doi.​org/​10.​1145/​1864431.​1864480
65.
72.
go back to reference Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R. (2013). Berkeley MHAD: A comprehensive multimodal human action database. In Proceedings of the 2013 IEEE workshop on applications of computer vision (WACV), Clearwater Beach, FL, USA, 15–17 January 2013 (pp. 53–60) Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R. (2013). Berkeley MHAD: A comprehensive multimodal human action database. In Proceedings of the 2013 IEEE workshop on applications of computer vision (WACV), Clearwater Beach, FL, USA, 15–17 January 2013 (pp. 53–60)
Metadata
Title
A Comprehensive Survey of Various Approaches on Human Fall Detection for Elderly People
Authors
Rohit Parmar
Samir Trapasiya
Publication date
28-06-2022
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 2/2022
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-022-09816-6

Other articles of this Issue 2/2022

Wireless Personal Communications 2/2022 Go to the issue