This paper describes an application of machine learning for predicting whether a user is engaged in art appreciation to develop AI audio guide systems that can automatically control when guidance is provided. Although many studies on intelligent audio guides in museums have been done, there are few that have tried to develop AI audio guide systems that can begin to play audio guides automatically when visitors are engaged in art appreciation. In this paper, we determine the timing at which to begin an audio guide by classifying two classes, that is, whether the user is engaged in art appreciation or not, which is identified at the museum. We apply supervised machine learning for time-series data to the classification. We conducted experiments with participants in a real museum and collected labeled time-series data of participants heads’ postures and movements as training data. Then, we applied a classification learning algorithm for time-series data to predict when participants were involved in painting appreciation, executed model selection, and experimentally evaluated the models with the collected data. Since the results showed a good accuracy of over 82%, we confirmed that our machine learning-based approach to real-time identification of painting appreciation is promising for AI audio guide systems.
