title: Spectral analyses of wrist motion in individuals poststroke: the development of a performance measure with promise for unsupervised settings.
abstract: BACKGROUND
  Upper extremity use in daily life is a critical ingredient of continued functional recovery after cerebral stroke. However, time-evolutions of use-dependent motion quality are poorly understood due to limitations of existing measurement tools.
  
  
  OBJECTIVE
  Proof-of-concept study to determine if spectral analyses explain the variability of known temporal kinematic movement quality (ie, movement duration, number of peaks, jerk) for uncontrolled reach-to-grasp tasks.
  
  
  METHODS
  Ten individuals with chronic stroke performed unimanual goal-directed movements using both hands, with and without task object present, wearing accelerometers on each wrist. Temporal and spectral measures were extracted for each gesture. The effects of performance condition on outcome measures were determined using 2-way, within subject, hand (nonparetic vs paretic) × object (present vs absent) analysis of variance. Regression analyses determined if spectral measures explained the variability of the temporal measures.
  
  
  RESULTS
  There were main effects of hand on all 3 temporal measures and main effects of object on movement duration and peaks. For the paretic limb, spectral measures explain 41.2% and 51.1% of the variability in movement duration and peaks, respectively. For the nonparetic limb, spectral measures explain 40.1%, 42.5%, and 27.8% of the variability of movement duration, peaks, and jerk, respectively.
  
  
  CONCLUSIONS
  Spectral measures explain the variability of motion efficiency and control in individuals with stroke. Signal power from 1.0 to 2.0 Hz is sensitive to changes in hand and object. Analyzing the evolution of this measure in ambient environments may provide as yet uncharted information useful for evaluating long-term recovery.
authors.affiliation: 1University of Tennessee, Knoxville, TN, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Wade
authors.forename: Christina
authors.initials: C
authors.lastname: Chen
authors.forename: Carolee J
authors.initials: CJ
authors.lastname: Winstein
mesh_terms: D000203:Activities of Daily Living; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001696:Biomechanical Phenomena; D003627:Data Interpretation, Statistical; D005260:Female; D018737:Hand Strength / Q000502:physiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D020521:Stroke / Q000503:physiopathology*; D013647:Task Performance and Analysis; D014953:Wrist / Q000503:physiopathology*
keywords: accelerometry; hemiplegia; kinematics; motion sensing; stroke rehabilitation; upper extremity