Document Type
Article
Publication Date
2010
Keywords
health care, elderly, radio-frequency identification devices (RFID)
Digital Object Identifier (DOI)
https://doi.org/10.4017/gt.2010.09.04.005.00
Abstract
Objective To determine whether a Real Time Locating System (RTLS) can be used to accurately detect a fall and discuss the application of RTLS as a fall detection system in the home and health care environments. Methods Phase I used a mannequin to determine the feasibility of RTLS to detect a fall from three positional conditions of: standing, seated in a wheelchair, and laying on a bed. Phase II used a human subject to be an ecologically valid simulation of a fall from these conditions. Ten trials of each of these three conditions were conducted across subjects. The observed time of the fall (the 'gold standard') was compared with the RTLS tag position. A Receiver Operating Characteristic (ROC) curve was used to report the Area Under the Curve (AUC) with 95% confidence intervals (CI) and Cohen's kappa (ϰ) was used to examine inter-rater reliability between the observed fall and the fall detected by the RTLS. Results RTLS accurately identified 89% (p<0.001) of the mannequin falls and 80% (p<0.001) of the human falls. Across subjects there were low false positive rates (specificity); 17% for the mannequin and 16% for the human. Interrater reliability was very good (ϰ=0.82; CI: 0.80-0.84) for mannequin falls and good (ϰ =0.72; CI: 0.69-0.74) for human falls. Implications RTLS technology may be used to improve caregiver and staff response times, patient-care, and reduce health care costs associated with falls in later life.
Rights Information
This work is licensed under a Creative Commons Attribution-No Derivative Works 3.0 License.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Gerontechnology, v. 9, issue 4, p. 464-471
Scholar Commons Citation
Bowen, Mary E.; Craighead, Jeffrey; Wingrave, Chadwick A.; and Kearns, William D., "Real-Time Locating Systems (RTLS) to Improve Fall Detection" (2010). Rehabilitation and Mental Health Counseling Faculty Publications. 103.
https://digitalcommons.usf.edu/mhs_facpub/103
