Development of an automated fall detection device specific to wheelchair users
L. A. Rice, L. Abou, A. Fliflet, P. Presti, J. J. Sosnoff, H. P. Mahajan, M. L. Frechette
Full text PDF 
( Download count: 304)
AbstractFalls are a major health concern among the 65 millions individuals who use a wheelchair worldwide.1 Older adults are four times more likely to use wheelchairs.2 More than 60% of wheelchair users are affected by falls.3 After a fall, wheelchair users spend an average of 9 minutes (range 1-45 min) on the floor and 80% require assistance to recover.4,5 Remaining on the ground for an extended period of time after a fall is associated with a risk of future injurious falls, long term care admissions, and death.6 Automated fall detection devices can be useful to provide timely assistance and minimize the consequences of a long lie.7 Although used widely in ambulatory populations, the ability to detect falls among wheelchair users is limited. Abou, et al8 examined the efficacy of a native fall detection app built into an Apple watch. The device was only able to detected 4.7% of falls from a wheelchair. The development of a fall detection device specific to wheelchair users is needed to facilitate a quick and accurate response after a fall. This presentation will describe the initial development of a fall detection algorithm and device preferences of older adult wheelchair users (target population).Keywords: accidental falls, wheelchair, older adult, fall detection
L. A. Rice, L. Abou, A. Fliflet, P. Presti, J. J. Sosnoff, H. P. Mahajan, M. L. Frechette (2022). Development of an automated fall detection device specific to wheelchair users. Gerontechnology, 21(s),1-1
https://doi.org/10.4017/gt.2022.21.s.739.opp3