title: Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease.
abstract: BACKGROUND
  The Microsoft Kinect sensor (Kinect) is potentially a low-cost solution for clinical and home-based assessment of movement symptoms in people with Parkinson's disease (PD). The purpose of this study was to establish the accuracy of the Kinect in measuring clinically relevant movements in people with PD.
  
  
  METHODS
  Nine people with PD and 10 controls performed a series of movements which were measured concurrently with a Vicon three-dimensional motion analysis system (gold-standard) and the Kinect. The movements included quiet standing, multidirectional reaching and stepping and walking on the spot, and the following items from the Unified Parkinson's Disease Rating Scale: hand clasping, finger tapping, foot, leg agility, chair rising and hand pronation. Outcomes included mean timing and range of motion across movement repetitions.
  
  
  RESULTS
  The Kinect measured timing of movement repetitions very accurately (low bias, 95% limits of agreement <10% of the group mean, ICCs >0.9 and Pearson's r>0.9). However, the Kinect had varied success measuring spatial characteristics, ranging from excellent for gross movements such as sit-to-stand (ICC=.989) to very poor for fine movement such as hand clasping (ICC=.012). Despite this, results from the Kinect related strongly to those obtained with the Vicon system (Pearson's r>0.8) for most movements.
  
  
  CONCLUSIONS
  The Kinect can accurately measure timing and gross spatial characteristics of clinically relevant movements but not with the same spatial accuracy for smaller movements, such as hand clasping.
authors.affiliation: Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom.
authors.forename: Brook
authors.initials: B
authors.lastname: Galna
authors.affiliation: Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom.
authors.forename: Gillian
authors.initials: G
authors.lastname: Barry
authors.affiliation: Culture Lab, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom.
authors.forename: Dan
authors.initials: D
authors.lastname: Jackson
authors.affiliation: Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom.
authors.forename: Dadirayi
authors.initials: D
authors.lastname: Mhiripiri
authors.affiliation: Culture Lab, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom.
authors.forename: Patrick
authors.initials: P
authors.lastname: Olivier
authors.affiliation: Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom. Electronic address: lynn.rochester@ncl.ac.uk.
authors.forename: Lynn
authors.initials: L
authors.lastname: Rochester
mesh_terms: D000328:Adult; D016022:Case-Control Studies; D004551:Elbow Joint / Q000502:physiology; D005260:Female; D006225:Hand / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D035002:Lower Extremity / Q000502:physiology; D008297:Male; D008875:Middle Aged; D009068:Movement / Q000502:physiology*; D010300:Parkinson Disease / Q000503:physiopathology*; D011403:Pronation / Q000502:physiology; D012785:Shoulder Joint / Q000502:physiology; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D012984:Software; D013484:Supination / Q000502:physiology; D060726:Torso / Q000502:physiology
keywords: Accuracy; Microsoft Kinect; Parkinson's disease; Validity