Strategic User Identification Increases the Impact of Wearable Airbags in High-Fall Risk Populations with Neurological Disease.

Abstract

Falls are a major health concern among older adults, particularly those with neurological conditions such as Parkinson's disease or stroke. Wearable airbags are a promising new technology that may help mitigate fall-related injuries. These devices use motion sensors, pre-impact fall detection algorithms, and CO₂-powered airbags to cushion the impact of a fall. However, this technology is only needed for people with a high risk of falls, and it is only useful if the pre-impact fall detection algorithm successfully detects the fall. Our prior work showed that some individuals benefit more from one pre-impact fall detection algorithm than another due to their unique movement characteristics and biomechanics. This study aims to determine who may be suitable users for this technology by predicting future fall risk and categorizing users as 'responders' or 'non-responders' based on their predicted algorithm performance. We recruited 22 participants with neurological conditions in a six-month study design. Using baseline physical assessments, survey scores, and principal component analysis, we trained a logistic regression model that distinguished high-risk 'fallers' from 'non-fallers' with an average F1 score of 0.76. The model also identified 'responder' individuals, whose fall patterns were accurately detected, achieving an F1 score of 0.75. These findings suggest that identifying high fall risk users whose falls are best identified by a fall detection algorithm can enhance device effectiveness and maximize benefits for users.