Automating Stroke Patient Evaluation Using Sensor Data and SVM

Abstract

Evaluation of post-stroke hemiplegic patients is an important aspect of rehabilitation, especially for assessing improvement of a patient's condition from a treatment. It is also commonly used to evaluate stroke patients during theraputic clinical trials [1]. The Fugl-Meyer Assessment is one of the most widely recognized and utilized measures of body function impairment for post-stroke patients [2]. We propose a method for automating the upper-limb portion of the Fugl-Meyer Assessment by gathering data from sensors monitoring the patient. Features are extracted from the data and processed by a Support Vector Machine (SVM). The output from the SVM returns a value that can be used to score a patient's upper limb functionality. This system will enable automatic and inexpensive stroke patient evaluation that can save up to 30 minutes per patient for a doctor, providing a huge time-saving service for doctors and stroke researchers.