The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors
Open accessAbstract
According to nihseniorhealth.gov (a website for older adults), falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people’s lives. Over 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms every year suffering fractures, loss of independence, and even death. It is clear then, that this problem must be addressed in a prompt manner, and the use of pervasive computing plays a key role to achieve this. Fall detection (FD) and fall prevention (FP) are research areas that have been active for over a decade, and they both strive for improving people’s lives through the use of pervasive computing. This paper surveys the state of the art in FD and FP systems, including qualitative comparisons among various studies. It aims to serve as a point of reference for future research on the mentioned systems. A general description of FD and FP systems is provided, including the different types of sensors used in both approaches. Challenges and current solutions are presented and described in great detail. A 3-level taxonomy associated with the risk factors of a fall is proposed. Finally, cutting edge FD and FP systems are thoroughly reviewed and qualitatively compared, in terms of design issues and other parameters.
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Research of Fall Detection and Fall Prevention Technologies: A Systematic Review
Ren, L., & Peng, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2922708 · Cited by 266
Shameer, K., Badgeley, M. A., Miotto, R., Glicksberg, B. S., Morgan, J. W., & Dudley, J. T. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbv118 · Cited by 198
IoT and digital twin enabled smart tracking for safety management
Zhao, Z., Shen, L., Yang, C., Wu, W., Zhang, M., & Huang, G. Q. (2021) · Computers & Operations Research · doi:10.1016/j.cor.2020.105183 · Cited by 155
Energy efficient wearable sensor node for IoT-based fall detection systems
Nguyen Gia, T., Sarker, V. K., Tcarenko, I., Rahmani, A. M., Westerlund, T., Liljeberg, P., & Tenhunen, H. (2018) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2017.10.014 · Cited by 146
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Metadata
- Authors
- Delahoz, Y., & Labrador, M.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2014-10-22
- Volume
- 14
- Issue
- 10
- Pages
- 19806-19842
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2055698923
- Cited by
- 329
- References
- 49
- FWCI
- 19.88
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
