title: Estimating physical activity energy expenditure with the Kinect Sensor in an exergaming environment.
abstract: Active video games that require physical exertion during game play have been shown to confer health benefits. Typically, energy expended during game play is measured using devices attached to players, such as accelerometers, or portable gas analyzers. Since 2010, active video gaming technology incorporates marker-less motion capture devices to simulate human movement into game play. Using the Kinect Sensor and Microsoft SDK this research aimed to estimate the mechanical work performed by the human body and estimate subsequent metabolic energy using predictive algorithmic models. Nineteen University students participated in a repeated measures experiment performing four fundamental movements (arm swings, standing jumps, body-weight squats, and jumping jacks). Metabolic energy was captured using a Cortex Metamax 3B automated gas analysis system with mechanical movement captured by the combined motion data from two Kinect cameras. Estimations of the body segment properties, such as segment mass, length, centre of mass position, and radius of gyration, were calculated from the Zatsiorsky-Seluyanov's equations of de Leva, with adjustment made for posture cost. GPML toolbox implementation of the Gaussian Process Regression, a locally weighted k-Nearest Neighbour Regression, and a linear regression technique were evaluated for their performance on predicting the metabolic cost from new feature vectors. The experimental results show that Gaussian Process Regression outperformed the other two techniques by a small margin. This study demonstrated that physical activity energy expenditure during exercise, using the Kinect camera as a motion capture system, can be estimated from segmental mechanical work. Estimates for high-energy activities, such as standing jumps and jumping jacks, can be made accurately, but for low-energy activities, such as squatting, the posture of static poses should be considered as a contributing factor. When translated into the active video gaming environment, the results could be incorporated into game play to more accurately control the energy expenditure requirements.
authors.affiliation: School of Sport Science, Exercise and Health, The University of Western Australia, Perth, Australia; School of Computing Science and Software Engineering, The University of Western Australia, Perth, Australia.
authors.forename: David
authors.initials: D
authors.lastname: Nathan
authors.affiliation: School of Computing Science and Software Engineering, The University of Western Australia, Perth, Australia.
authors.forename: Du Q
authors.initials: du Q
authors.lastname: Huynh
authors.affiliation: School of Sport Science, Exercise and Health, The University of Western Australia, Perth, Australia; Biomechanics Laboratory, Department of Kinesiology, The Pennsylvania State University, University Park, U.S.A.
authors.forename: Jonas
authors.initials: J
authors.lastname: Rubenson
authors.affiliation: School of Sport Science, Exercise and Health, The University of Western Australia, Perth, Australia.
authors.forename: Michael
authors.initials: M
authors.lastname: Rosenberg
mesh_terms: D000328:Adult; D004734:Energy Metabolism / Q000502:physiology*; D015444:Exercise / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology*; D005082:Physical Exertion / Q000502:physiology; D011187:Posture / Q000502:physiology; D011998:Recreation / Q000502:physiology*; D018910:Video Games; D055815:Young Adult