Toadstool: A Dataset for Training Emotional Intelligent Machines Playing Super Mario Bros
| Autor: | Petter Jakobsen, Steven Alexander Hicks, Michael Riegler, Farzan Majeed Noori, Mathias Lux, Hugo Lewi Hammer, Enrique Garcia-Ceja, Henrik Svoren, Vajira Thambawita, Pål Halvorsen |
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| Rok vydání: | 2020 |
| Předmět: |
Facial expression
Information retrieval Data collection Artificial neural network Computer science Emotional intelligence 05 social sciences 02 engineering and technology Blood volume pulse 050105 experimental psychology Multimedia datasets Machine learning 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Set (psychology) Video game Neural networks Emotional machines |
| Zdroj: | MMSys |
| Popis: | 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 high-quality 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 to research on emotionally aware machine learning algorithms, focusing on reinforcement learning and multimodal data fusion. We believe that the presented dataset can be interesting for a manifold of researchers to explore exciting new interdisciplinary questions. |
| Databáze: | OpenAIRE |
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