Abstract
This paper presents a gamification concept for getting high-quality user-annotated datasets in the context of mobile activity recognition, as well as a cheating detection algorithm. The novel idea behind this concept is that users are motivated by getting feedback about the quality of their labeling activity as rewards or gamification element. For that, the collected sensor data and labels are used as training data for a machine learning algorithm for determining the dataset quality based on the resulting accuracy. By using the proposed method, the results show that the gamification elements increase the quantity (labels from the proposed method is higher than the naive by at least 305) and the quality (the accuracy of the proposed data outperformed the original data by at least 4.3%) of the labels. Besides, the cheating detection algorithm could detect cheating with the accuracy of more than 70% that is fascinating work.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Mairittha, N., Inoue, S. (2018). Gamification for High-Quality Dataset in Mobile Activity Recognition. In: Murao, K., Ohmura, R., Inoue, S., Gotoh, Y. (eds) Mobile Computing, Applications, and Services. MobiCASE 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 240. Springer, Cham. https://doi.org/10.1007/978-3-319-90740-6_14
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DOI: https://doi.org/10.1007/978-3-319-90740-6_14
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