Citation

Brandstötter M, Lumetzberger J, Kampel M, Planinc R. Stud. Health Technol. Inform. 2023; 306: 113-119.

Copyright

(Copyright © 2023, IOS Press)

DOI

10.3233/SHTI230604

PMID

37638906

Abstract

The majority of falls leading to death occur among the elderly population. The use of fall detection technology can help to ensure quick help for fall victims by automatically informing caretakers. Our fall detection method is based on depth data and has a high level of reliability in detecting falls while maintaining a low false alarm rate. The technology has been deployed in over 1,200 installations, indicating user acceptance and technological maturity. We follow a privacy by design approach by using range maps for the analysis instead of RGB images and process all the data in the sensor. The literature review shows that real-world fall detection evaluation is scarce, and if available, is conducted with a limited amount of participants. To our knowledge, our depth image based fall detection method has achieved the largest field evaluation up to date, with more than 100,000 events manually annotated and an evaluation on a dataset with 2.2 million events. We additionally present an 8-months study with more than 120,000 alarms analysed, provoked by 214 sensors located in 16 care facilities in Austria. We learned that on average 2.3 times more falls happen than are documented. Consequently, the system helps to detect falls that are otherwise overseen. The presented solution has the potential to make a significant impact in reducing the risk of accidental falls.

Language: en

Keywords

Aged; Humans; evaluation; Austria; Knowledge; *Accidental Falls/prevention & control; *Privacy; depth data; fall detection; practice; Reproducibility of Results