Functional Score Estimation of Post-Stroke Assessment Test from Wearable Inertial Sensor Data
نویسندگان
چکیده
We present an approach to wearable sensor-based assessment of functional ability (FA) of individuals post stroke. We make use of one on-body inertial measurement unit (IMU) to automate the FA scoring of the Wolf Motor Function Test (WMFT). WMFT is an assessment instrument used to determine the functional capabilities of individuals post stroke. It comprises of 17 tasks15 of which are rated according to performance time and quality of motion. We present signal processing and machine learning tools to estimate the WMFT FA scores of the 15 tasks from IMU data. We treat this as a classification problem in multidimensional feature space and use a supervised learning approach.
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