title: Automated extraction and validation of children's gait parameters with the Kinect.
abstract: BACKGROUND
  Gait analysis for therapy regimen prescription and monitoring requires patients to physically access clinics with specialized equipment. The timely availability of such infrastructure at the right frequency is especially important for small children. Besides being very costly, this is a challenge for many children living in rural areas. This is why this work develops a low-cost, portable, and automated approach for in-home gait analysis, based on the Microsoft Kinect.
  
  
  METHODS
  A robust and efficient method for extracting gait parameters is introduced, which copes with the high variability of noisy Kinect skeleton tracking data experienced across the population of young children. This is achieved by temporally segmenting the data with an approach based on coupling a probabilistic matching of stride template models, learned offline, with the estimation of their global and local temporal scaling. A preliminary study conducted on healthy children between 2 and 4 years of age is performed to analyze the accuracy, precision, repeatability, and concurrent validity of the proposed method against the GAITRite when measuring several spatial and temporal children's gait parameters.
  
  
  RESULTS
  The method has excellent accuracy and good precision, with segmenting temporal sequences of body joint locations into stride and step cycles. Also, the spatial and temporal gait parameters, estimated automatically, exhibit good concurrent validity with those provided by the GAITRite, as well as very good repeatability. In particular, on a range of nine gait parameters, the relative and absolute agreements were found to be good and excellent, and the overall agreements were found to be good and moderate.
  
  
  CONCLUSIONS
  This work enables and validates the automated use of the Kinect for children's gait analysis in healthy subjects. In particular, the approach makes a step forward towards developing a low-cost, portable, parent-operated in-home tool for clinicians assisting young children.
authors.affiliation: Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA. samotiian@mix.wvu.edu.
authors.forename: Saeid
authors.initials: S
authors.lastname: Motiian
authors.affiliation: Department of Pediatrics, West Virginia University School of Medicine, Morgantown, WV, USA. ppergami@hsc.wvu.edu.
authors.forename: Paola
authors.initials: P
authors.lastname: Pergami
authors.affiliation: Department of Biology, West Virginia University, Morgantown, WV, USA. keguffey@mix.wvu.edu.
authors.forename: Keegan
authors.initials: K
authors.lastname: Guffey
authors.affiliation: Division of Physical Therapy, West Virginia University School of Medicine, Morgantown, WV, USA. cmancinelli@hsc.wvu.edu.
authors.forename: Corrie A
authors.initials: CA
authors.lastname: Mancinelli
authors.affiliation: Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA. gidoretto@mix.wvu.edu.
authors.forename: Gianfranco
authors.initials: G
authors.lastname: Doretto
mesh_terms: D001331:Automation; D002675:Child, Preschool; D003201:Computers; D005260:Female; D005528:Foot / Q000502:physiology; D005684:Gait; D006801:Humans; D007596:Joints / Q000502:physiology; D008297:Male; D012815:Signal Processing, Computer-Assisted; D018910:Video Games