Development and Validation of a Fall Detection System for Older Adults Who Use Wheelchairs
Laura Rice, David Peeler, Peter Presti, Andrea Tangonan

TL;DR
This paper presents a new fall detection system for older adults who use wheelchairs or scooters, aiming to improve safety and response times after falls.
Contribution
The paper introduces a custom algorithm with >99% accuracy for fall detection in wheelchair/scooter users, along with user feedback on design preferences.
Findings
The custom algorithm achieved high accuracy (>99%) in detecting falls from wheelchairs or scooters.
Users preferred a watch form and the ability to modify contacts and cancel false alarms.
Clinicians emphasized the system's potential to improve user-centered fall interventions and patient connections.
Abstract
Falls are a concern for the 1 million older adults who use a wheelchairs or scooters (WC/S) in the US. ∼60% of the population are affected by falls. After a fall, people who use WC/S spend an average of 9 minutes (range: 1-45 min) on the floor and 80% require assistance to recover. Remaining on the ground for an extended period of time after a fall is associated with an increased risk of future injurious falls, long term care admissions, and death. Automated fall detection devices can provide timely assistance and minimize the consequences of a long lie. Although used widely in ambulatory populations, current automated fall detection devices do not accurately detect falls from WC/S. In response, our team developed a system to detect falls among older adults who use WC/S and increase awareness of fall circumstances. A custom algorithm was developed to detect falls with high accuracy…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems · Balance, Gait, and Falls Prevention · Assistive Technology in Communication and Mobility
