Machine Learning Prediction of Just Dance Exergame Enjoyment from Mobile Sensor Data.

Many young adults do not exercise enough, choosing instead to spend time on electronic media (e.g., smartphone, Internet). Exergames, which gamify physical activity, have been shown to be effective at increasing physical activity in an enjoyable way. For exergames to remain effective, sustained user engagement is key. However, sustaining long-term engagement in games (including exergames) is a challenging research problem – 95% of all new game players stop playing within 3 months, and 85% of new players stop after just one day. We posit that if detected early, waning player exergame enjoyment can be countered by recommending new, more enjoyable games before the player quits playing. In this paper, we investigate machine learning to predict user enjoyment of the Just Dance exergame by analyzing data gathered from the player’s smartphone. Specifically, "ground truth" scores for the players’ enjoyment obtained from the Immersive Experience Questionnaire (IEQ) E-scores are inferred from user behaviors such as increased excitement and gameplay frequency. These, in turn, are predicted by data gathered by the phone’s sensors – accelerometer, gyroscope and game features. Analysis of data from a user study shows the Naive Bayes classification algorithm achieves the best results, achieving 75% accuracy for binary classification (enjoying vs. not enjoying the exergame) of enjoyment E-scores. The most predictive features were the energy in the 0.5 to 3 GHz range, windowed energy in the 0.5 to 3 Hz range and radio spectral peak using a Discrete Cosine Transform (DCT). Our results are preliminary but encouraging and we plan to improve on our results by collecting more data and utilizing state-of-the-art neural networks approaches.