2014 · 37 citations · 11 references
MedicinePhysical MedicineRehabilitation MedicineFunctional AssessmentRehabilitationStroke RehabilitationFunctional Capacity AssessmentJoint Motion AnalysisPhysical Performance AssessmentPhysical RehabilitationShoulder RehabilitationUpper Extremity Function AssessmentAccelerometer SensorsCross-validationModel Performance EvaluationMovement AssessmentPrediction Model ValidationPredictive Model PerformanceStroke Upper Limb AssessmentSupport Vector Regression
A simple, objective and quantitative unsupervised outcome measure is considered vital in the home-based rehabilitation for stroke patients. The Fugl-Meyer Assessment (FMA) scale is widely utilized in the clinical practice, while not suitable in the home settings due to its subjective and time-consuming property. In this paper, a Support Vector Regression (SVR) based evaluation model was presented to automatically estimate the FMA scores for Shoulder-Elbow movement. The estimation was obtained by analyzing accelerometer data recorded during the performance of 4 tasks from Shoulder-Elbow FMA. A combined feature selection method based on ReliefF-SVR was implemented to simplify the calculation and improve the model performance. Twenty-four subjects were involved in this study and results showed that it was possible to achieve accurate estimation of Shoulder-Elbow FMA scores using the proposed model and a cross-validation prediction error value of 2.1273 was achieved.
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Support Vector Regression Machines
Harris Drucker, Christopher J. C. Burges, Linda Kaufman et al. · 1996 · 4.2K citations
Fugl-Meyer Assessment of Sensorimotor Function After Stroke
Katherine J. Sullivan, Julie K. Tilson, Steven Cen et al. · Stroke · 2010 · 444 citations
Gitendra Uswatte, Wai Leong Foo, Harry Olmstead et al. · Archives of Physical Medicine and Rehabilitation · 2005 · 238 citations