Optimizing activity data collection with gamification points using uncertainty based active learning

Abstract

A big challenge for activity data collection is unavoidable to rely on users and to keep them motivated to provide labels. In this paper, we propose the idea of exploiting gamication points to motivate the users for activity data collection by using an uncertainty based active learning approach to evaluate those points. The novel idea behind this is that we approximate the score of the unlabeled examples according to the current model’s uncertainty in its prediction of the corresponding activity labels, and using that score as gamication points. Thus, the users are motivated by getting gamication points as feedback based on their data annotation quality. 1,236 activity labels with smartphone sensors that we collected help to validate our proposed method. By evaluating with the dataset, the results show our proposed method has improvements in data quality, data quantity, and user engagement that reect the improvement in activity data collection.