Abstract
Post-stroke patients usually suffer from a higher fall risk. Identifying potential fallers and giving them proper attention could reduce their chance of a fall that results in severe injuries and decreased quality of life. In this study, we introduced a novel approach for fall risk prediction that evaluates Short-form Berg Balance Scale scores via inertial measurement unit data measured from a 3-meter timed-up-and-go test. This approach used sensor technology and was thus easy to implement, and allowed a quantitative analysis of both gait and balance. The results showed that elastic net logistic regression achieved the best performance with 85% accuracy and 88% area under the curve compared with support vector machine, least absolute shrinkage and selection operator (LASSO), and stepwise logistic regression. This paper provides a framework for using sensor-based features together with a feature-selection strategy for screening and predicting the fall risk of post-stroke patients in a convenient setup with high accuracy. The findings of this study will not only enable the assessment of fall risk among post-stroke patients in a cost-effective manner but also provide decision-making support for community care providers and medical professionals in the form of sensor-based data on gait performance.