Context-aware data processing can improve the quality of measurements from wireless health systems by integrating contextual information into the data processing flow. The paper presents an approach that focuses on calculating metabolic equivalent of task (MET) values for exergaming movements using wearable motion sensors. By considering the contextual factors of activity type and sensor location, regression models can be designed for each individual activity type, improving the averaged R2 value from 0.71 to as high as 0.84 compared to a general model. The different methods tested resulted in average R2 values ranging from 0.64 to 0.89 across activity types, with an average game play MET value of 7.93.