title: Suitability of Kinect for measuring whole body movement patterns during exergaming.
abstract: Exergames provide a challenging opportunity for home-based training and evaluation of postural control in the elderly population, but affordable sensor technology and algorithms for assessment of whole body movement patterns in the home environment are yet to be developed. The aim of the present study was to evaluate the use of Kinect, a commonly available video game sensor, for capturing and analyzing whole body movement patterns. Healthy adults (n=20) played a weight shifting exergame under five different conditions with varying amplitudes and speed of sway movement, while 3D positions of ten body segments were recorded in the frontal plane using Kinect and a Vicon 3D camera system. Principal Component Analysis (PCA) was used to extract and compare movement patterns and the variance in individual body segment positions explained by these patterns. Using the identified patterns, balance outcome measures based on spatiotemporal sway characteristics were computed. The results showed that both Vicon and Kinect capture >90% variance of all body segment movements within three PCs. Kinect-derived movement patterns were found to explain variance in trunk movements accurately, yet explained variance in hand and foot segments was underestimated and overestimated respectively by as much as 30%. Differences between both systems with respect to balance outcome measures range 0.3-64.3%. The results imply that Kinect provides the unique possibility of quantifying balance ability while performing complex tasks in an exergame environment.
authors.affiliation: INCAS(3), Assen, The Netherlands; Center for Human Movement Sciences, University of Groningen, University Medical Center Groningen, The Netherlands. Electronic address: mikevandiest@incas3.eu.
authors.forename: Mike
authors.initials: M
authors.lastname: van Diest
authors.affiliation: INCAS(3), Assen, The Netherlands.
authors.forename: Jan
authors.initials: J
authors.lastname: Stegenga
authors.affiliation: INCAS(3), Assen, The Netherlands.
authors.forename: Heinrich J
authors.initials: HJ
authors.lastname: Wörtche
authors.affiliation: Center for Rehabilitation, University of Groningen, University Medical Center Groningen, The Netherlands.
authors.forename: Klaas
authors.initials: K
authors.lastname: Postema
authors.affiliation: Center for Rehabilitation, University of Groningen, University Medical Center Groningen, The Netherlands; Dept of Biomechanical Engineering, University of Twente, Enschede, The Netherlands.
authors.forename: Gijsbertus J
authors.initials: GJ
authors.lastname: Verkerke
authors.affiliation: Center for Human Movement Sciences, University of Groningen, University Medical Center Groningen, The Netherlands.
authors.forename: Claudine J C
authors.initials: CJ
authors.lastname: Lamoth
mesh_terms: D000328:Adult; D000465:Algorithms; D005260:Female; D005528:Foot / Q000502:physiology; D006225:Hand / Q000502:physiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D009068:Movement / Q000502:physiology*; D011187:Posture / Q000502:physiology*; D060726:Torso / Q000502:physiology; D018910:Video Games; D055815:Young Adult
keywords: Balance quantification; Exergame; Fall prevention; Principal Component Analysis