A Novel Approach for Fall Risk Prediction Using the Inertial Sensor Data From the Timed-Up-and-Go Test in a Community Setting

• Published in: IEEE sensors journal Vol. 20; no. 16; pp. 9339 - 9350
• Main Authors: Hsu, Yu-Cheng, Zhao, Yang, Huang, Kuang-Hui, Wu, Ya-Ting, Cabrera, Javier, Sun, Tien-Lung, Tsui, Kwok-Leung
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: accelerometer; Berg Balance Scale; Community health care; data mining; Decision making; fall risk prediction; Feature extraction; Gait; gyroscope; Inertial platforms; Inertial sensing devices; Injury prevention; Legged locomotion; Measurement units; Regression analysis; Sensor phenomena and characterization; Sensors; Stroke; Support vector machines; time-up-and-go test
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.2987623

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.