Automating the Fugl-Meyer Assessment with Supervised Machine Learning

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 a necessary step to perform during clinical trials. The Fugl-Meyer Assessment (FMA) is one of the most widely recognized and utilized measures of body function impairment for post-stroke patients. We propose a method for automating the upper-limb portion of the FMA by gathering data from sensors monitoring the patient. Features are extracted from the data and processed by a machine learning algorithm. The output from the machine learning algorithm returns a value that can be used to score a patient’s upper limb functionality. The machine learning algorithms tested in our system were Support Vector Machines (SVM) and Backpropagation Neural Networks (BNN). This system will enable automating and inexpensive stroke patient evaluation that can save up to 30 minutes per patient for a doctor, providing a time-saving service for doctors and stroke researchers. ⓒ 2015 DGIST