In this paper, we present a dataset called Toadstool that aims to contribute to the field of reinforcement learning, multimodal data fusion, and the possibility of exploring emotionally aware machine learning algorithms. Furthermore, the dataset can also be useful to researchers interested in facial expressions, biometric sensors, sentiment analysis, and game studies. The dataset consists of video, sensor, and demographic data collected from ten participants playing Super Mario Bros. The sensor data is collected through an Empatica E4 wristband, which provides high-quality measurements and is graded as a medical device. In addition to the dataset, we also present a set of baseline experiments which show that we can use video game frames together with the facial expressions to predict the blood volume pulse of the person playing the game. We believe that the presented dataset can be interesting for a manifold of researchers to explore different exciting questions.
Games are often defined as engines of experience, and they are heavily relying on emotions, they arouse in players. In this paper, we present a dataset called Toadstool as well as a reproducible methodology to extend on the dataset. The dataset consists of video, sensor, and demographic data collected from ten participants playing Super Mario Bros, an iconic and famous video game. The sensor data is collected through an Empatica E4 wristband, which provides highquality measurements and is graded as a medical device. In addition to the dataset and the methodology for data collection, we present a set of baseline experiments which show that we can use video game frames together with the facial expressions to predict the blood volume pulse of the person playing Super Mario Bros. With the dataset and the collection methodology we aim to contribute
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