  We introduce a novel multimodal mobile database called HuMIdb (Human Mobile
Interaction database) that comprises 14 mobile sensors acquired from 600 users.
The heterogeneous flow of data generated during the interaction with the
smartphones can be used to model human behavior when interacting with the
technology. Based on this new dataset, we explore the capacity of smartphone
sensors to improve bot detection. We propose a CAPTCHA method based on the
analysis of the information obtained during a single drag and drop task. We
evaluate the method generating fake samples synthesized with Generative
Adversarial Neural Networks and handcrafted methods. Our results suggest the
potential of mobile sensors to characterize the human behavior and develop a
new generation of CAPTCHAs.
