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Automated fall detection devices for individuals who use wheelchairs to minimize the consequences of falls are lacking. This study aimed to develop and train a fall detection algorithm to differentiate falls from wheelchair mobility activities using machine learning techniques. Thirty, healthy, ambulatory, young adults simulated falls from a wheelchair and performed other wheelchair-related mobility activities in a laboratory. Neural Network classifiers were used to train the algorithm developed based on data retrieved from accelerometers mounted at the participant's wrist, chest, and head. Results indicate excellent accuracy to differentiate between falls and wheelchair mobility activities. The sensors mounted at the wrist, chest, and head presented with an accuracy of 100%, 96.9%, and 94.8%, respectively, using data from 258 falls and 220 wheelchair mobility activities. This pilot study indicates that a fall detection algorithm developed in a laboratory setting based on fall accelerometer patterns can accurately differentiate wheelchair-related falls and wheelchair mobility activities. This algorithm should be integrated into a wrist-worn devices and tested among individuals who use a wheelchair in the community.

A reliable fall detection device is crucial to minimize long-term consequences of falls among wheelchair users. This study examines the sensitivity of Apple Watch to detect intentional falls from a wheelchair. Twenty-five able bodied (age: 21.9 ± 2.5 years) participated in a protocol in which they intentionally fell out of a wheelchair in a laboratory setting. Each participant wore an Apple Watch Series 5 and performed 3 falls in the forward, right and left sideways, and backward directions onto a crash pad totaling 12 falls each. The Apple Watch was manually checked after each fall to determine if the device registered a fall. From 300 fall trials captured, the Apple Watch detected 14 falls showing a sensitivity of 4.7%, a false negative rate of 95.3%. Logistic regression showed that participant's height, impact force, lower limb functioning, and fall direction are parameters that may influence the ability of the Apple Watch to detect falls from a wheelchair. The Apple Watch fall detection feature presented with a very poor sensitivity to detect intentional falls from a wheelchair among able bodied young adults. Due to the high incidence and consequences of falls, a reliable fall detection device specific for wheelchair users is warranted.

Background: Falls are a concern for older adults who use wheelchairs and scooters. Many wheelchair and scooter users require assistance to recover from a fall and often lie on the ground waiting for assistance for 10 min or more. An automated fall detection device may facilitate communication with care partners and expedite recovery; however, there is limited research on the specifications and features of an automated fall detection device preferred by older adults who use wheelchair and scooter., Objective: To examine the desired specifications, perceived ease of use and perceived usefulness of an automated fall detection device desired by older adults who use a wheelchair or scooter through semi-structured interviews., Methods: Fifteen full-time wheelchair and scooter users (9 females; age: 68 ± 5 years) were interviewed from July to November 2020. Interviews were transcribed, coded, and analyzed., Results: Preferred features include wireless charging, a watch form, ability to change the individual who is contacted in the event of a fall, and the ability to disable a notification in the event of a false alarm. Participants felt that an automated fall detection device would be useful and easy to use., Conclusions: Older adults who use a wheelchair or scooter indicated the need for an automated fall detection device to facilitate recovery from a fall. Participants reported challenges with previous fall detection devices and the need for specific design requirements to facilitate ongoing use. Participants' insights inform the design of a fall detection device to maximize usability and prevent technology abandonment., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The aim of this study was to investigate the feasibility and preliminary validity and reliability of remote sitting balance assessment. Seven wheelchair users (mean age: 42.7 ± 19.74 years) participated in an in-person and remote sitting balance assessment. The assessments were compared to investigate the concurrent validity of the remote assessment. Reliability of remote assessment was evaluated using intraclass coefficient correlation (ICC) and the Bland-Altman. No significant differences were observed between the mean scores of in-person and remote administrations of the clinical tests (P's > 0.05). High to very high agreement was found between in-person and remote assessments (ICC = 0.88-0.982, P < 0.05). The agreement was confirmed by Bland-Altman graph analysis. Preliminary results indicate remote sitting balance assessment is feasible to perform, valid, and reliable., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)