Paper Info
Title
Gamification for High-Quality Dataset in Mobile Activity Recognition.
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.
Year
Venue
Field
2018
MobiCASE
Training set,Activity recognition,Labeling Activity,Computer security,Computer science,Artificial intelligence,Cheating,Machine learning
DocType
Citations 
PageRank 
Conference
1
0.37
References 
Authors
6
2
Name
Order
Citations
PageRank
Nattaya Mairittha124.77
Sozo Inoue217658.17