Classification of Physiological Data in Affective Exergames
Andreas Kamenz, Victoria Bibaeva, Arne Bernin, Sobin Ghose, Kai von Luck, FLORIAN M. VOGT, Larissa Müller · 2018
In this work, we present our approach to analyze physiological data in affective exergames by using deep learning algorithms. In previous works, we enhanced a cycling exercise machine to act as a game controller. During a case study, we then collected vision-based and physiological data of 25 participants who rode through a game environment that was designed to provoke emotions. In order to analyze the collected physiological data, we now propose an ensemble learning approach based on three distinct deep learning models: Multilayer Perceptron, Fully Convolutional Networks and Residual Networks. As a result, the proposed algorithms were able to enhance the quality of our event-based emotion analysis method introduced previously.