title: Robotic pilot study for analysing spasticity: clinical data versus healthy controls.
abstract: BACKGROUND
  Spasticity is a motor disorder that causes significant disability and impairs function. There are no definitive parameters that assess spasticity and there is no universally accepted definition. Spasticity evaluation is important in determining stages of recovery. It can determine treatment effectiveness as well as how treatment should proceed. This paper presents a novel cross sectional robotic pilot study for the primary purpose of assessment. The system collects force and position data to quantify spasticity through similar motions of the Modified Ashworth Scale (MAS) assessment in the Sagittal plane. Validity of the system is determined based on its ability to measure velocity dependent resistance.
  
  
  METHODS
  Forty individuals with Acquired Brain Injury (ABI) and 45 healthy individuals participated in a robotic pilot study. A linear regression model was applied to determine the effect an ABI has on force data obtained through the robotic system in an effort to validate it. Parameters from the model were compared for both groups. Two techniques were performed in an attempt to classify between healthy and patients. Dynamic Time Warping (DTW) with k-nearest neighbour (KNN) classification is compared to a time-series algorithm using position and force data in a linear discriminant analysis (LDA).
  
  
  RESULTS
  The system is capable of detecting a velocity dependent resistance (p<0.05). Differences were found between healthy individuals and those with MAS 0 who are considered to be healthy. DTW with KNN is shown to improve classification between healthy and patients by approximately 20 % compared to that of an LDA.
  
  
  CONCLUSIONS
  Quantitative methods of spasticity evaluation demonstrate that differences can be observed between healthy individuals and those with MAS of 0 who are often clinically considered to be healthy. Exploiting the time-series nature of the collected data demonstrates that position and force together are an accurate predictor of patient health.
authors.affiliation: University of Guelph, 50 Stone Road East, N1G 2W1, Guelph, ON, Canada. sethn@uoguelph.ca.
authors.forename: Nitin
authors.initials: N
authors.lastname: Seth
authors.affiliation: Hamilton Health Sciences, Regional Rehabilitation Centre, 300 Wellington Rd North, L8L 0A4, Hamilton, ON, Canada. johnsden@hhsc.ca.
authors.forename: Denise
authors.initials: D
authors.lastname: Johnson
authors.affiliation: University of Guelph, 50 Stone Road East, N1G 2W1, Guelph, ON, Canada. gwtaylor@uoguelph.ca.
authors.forename: Graham W
authors.initials: GW
authors.lastname: Taylor
authors.affiliation: University of Guelph, 50 Stone Road East, N1G 2W1, Guelph, ON, Canada. ballen@uoguelph.ca.
authors.forename: O Brian
authors.initials: OB
authors.lastname: Allen
authors.affiliation: University of Guelph, 50 Stone Road East, N1G 2W1, Guelph, ON, Canada. habdulla@uoguelph.ca.
authors.forename: Hussein A
authors.initials: HA
authors.lastname: Abdullah
mesh_terms: D000328:Adult; D000465:Algorithms; D001930:Brain Injuries / Q000150:complications*; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009128:Muscle Spasticity / Q000175:diagnosis* / Q000209:etiology; D010865:Pilot Projects; D012371:Robotics / Q000379:methods*