Automatic Stroke Screening on Mobile Application: Features of Gyroscope and Accelerometer for Arm Factor in FAST

This study focuses on automatic stroke-screening of the arm factor in the FAST (Face, Arm, Speech, and Time) stroke screening method. The study provides a methodology to collect data on specific arm movements, using signals from the gyroscope and accelerometer in mobile devices. Fifty-two subjects were enrolled in this study (20 stroke patients and 32 healthy subjects). Given in the instructions of the application, the patients were asked to perform two arm movements, Curl Up and Raise Up. The two exercises were classified into three parts: curl part, raise part, and stable part. Stroke patients were expected to experience difficulty in performing both exercises efficiently on the same arm. We proposed 20 handcrafted features from these three parts. Our study achieved an average accuracy of 61.7%-74.2% and an average area under the ROC curve (AUC) of 66.2%-81.5% from the combination of both exercises. Compared to the FAST method used by examiners in a previous study (Kapes et al., 2014) that showed with an accuracy of 69%-77% for every age group, our study showed promising results for early stroke identification, giving that our study is based only on the arm factor.