Using Kinect body joint detection system to predict energy expenditures during physical activities

The purpose of this study was to explore the potential of Kinect body joint detection to facilitate the calculation of energy expenditure during exergame exercises. Two Kinect-based biomechanical models - mechanical energy (KineticE) and work (WorkE) were employed to estimate the energy expenditure during four Wii™ exergame session. Consequently, two stepwise regression models were developed from nineteen participants' data and then validated by five holdout participants. The data collected using an accelerometer (r = 0.835, p < 0.001) had the highest correlation as compared to that of the WorkE (r = 0.805, p < 0.001) and KineticE (r = 0.466, p < 0.001) correlations with the reference indirect calorimetry using Quark activity energy expenditure (QuarkAEE). The regression results show that KineticE and the weight of the participant were significant factors for mechanical energy prediction (AEEKinetic). However, according to the work prediction equation (AEEWork), only WorkE was significant. The new energy prediction models showed significant agreement with the standard QuarkAEE (AEEKinect, r = 0.641, p = 0.02; AEEWork, r = 0.793, p < 0.001), and they were comparable to accelerometer predictions (r = 0.682, p = 0.001). The findings indicate that Kinect can be a potentially viable alternative to measure energy expenditures. The models can be applied with higher accuracy, especially when the activity demands high body movements.