title: Robot-based assessment of motor and proprioceptive function identifies biomarkers for prediction of functional independence measures.
abstract: BACKGROUND
  Neurological impairments following stroke impact the ability of individuals to perform daily activities, although the relative impact of individual impairments is not always clear. Recovery of sensorimotor function following stroke can vary widely, from complete recovery to modest or minimal improvements, across individuals. An important question is whether one can predict the amount of recovery based on initial examination of the individual. Robotic technologies are now being used to quantify a range of behavioral capabilities of individuals post-stroke, providing a rich set of biomarkers of sensory and motor dysfunction. The objective of the present study is to use mathematical models to identify which biomarkers best predict the ability of subjects with stroke to perform daily activities before and after rehabilitation.
  
  
  METHODS
  The Functional Independence Measure (FIM) was quantified approximately 2 weeks and three months post-stroke in 61 ischemic and 24 hemorrhagic subjects with stroke. At 2 weeks post-stroke, subjects also completed clinical assessments and robotic assessments of sensory and motor function. A computational search algorithm, known as Fast Orthogonal Search, was used to identify the robotic and clinical biomarkers that best estimated Functional Independence Measures.
  
  
  RESULTS
  Clinical and robot-based biomarkers were statistically similar at predicting FIM scores at 2 weeks (r = 0.817 vs. 0.774, respectively) and 3 months (r = 0.643 vs. 0.685, respectively). Importantly, robot-based biomarkers highlighted that parameters related to proprioception were influential for predicting FIM scores at 2 weeks, whereas biomarkers related to bimanual motor function were influential for predicting FIM scores at 3 months.
  
  
  CONCLUSIONS
  The present study provides a proof of principle on the use of robot-based biomarkers of sensory and motor dysfunction to estimate present and future FIM scores. The addition of other behavioral tasks will likely increase the accuracy of these predictions, and potentially help guide rehabilitation strategies to maximize functional recovery.
authors.affiliation: School of Computing, Queen's University, Kingston, ON, Canada.
authors.forename: Sayyed Mostafa
authors.initials: SM
authors.lastname: Mostafavi
authors.affiliation: School of Computing, Queen's University, Kingston, ON, Canada.
authors.forename: Parvin
authors.initials: P
authors.lastname: Mousavi
authors.affiliation: Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
authors.forename: Sean P
authors.initials: SP
authors.lastname: Dukelow
authors.affiliation: Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada. steve.scott@queensu.ca.|Providence Care, St. Mary's of the Lake Hospital, Kingston, ON, Canada. steve.scott@queensu.ca.|Centre for Neuroscience Studies, Queen's University, Kingston, K7L 3N6, ON, Canada. steve.scott@queensu.ca.
authors.forename: Stephen H
authors.initials: SH
authors.lastname: Scott
mesh_terms: D000328:Adult; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D011434:Proprioception; D020127:Recovery of Function; D012371:Robotics / Q000379:methods*; D000071939:Stroke Rehabilitation