User-optimized activity recognition for exergaming
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2015 Elsevier B.V. All rights reserved. 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.
Pervasive and Mobile Computing
author list (cited authors)
Mortazavi, B. J., Pourhomayoun, M., Lee, S. I., Nyamathi, S., Wu, B., & Sarrafzadeh, M.
complete list of authors
Mortazavi, Bobak J||Pourhomayoun, Mohammad||Lee, Sunghoon Ivan||Nyamathi, Suneil||Wu, Brandon||Sarrafzadeh, Majid