User-optimized activity recognition for exergaming

This paper presents SoccAR, a wearable exergame with fine-grain activity recognition; the exergame involves high-intensity movements as the basis for control. A multiple model approach was developed for a generalized, large, multiclass recognition algorithm, with an F Score of a leave-one-subject-out cross-validation greater than 0.9 using various features, models, and kernels to the underlying support vector machine (SVM). The exergaming environment provided an opportunity for user-specific optimization, where the expected movement can assist in better identifying a particular user’s movements when incorrectly predicted; a single model SVM with a radial basis function kernel improved 12.5% with this user optimization.