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In this paper we list the sensors commonly available in modern smartphonesand provide a general outlook of the different ways these sensors can be usedfor modeling the interaction between human and smartphones. We then provide ataxonomy of applications that can exploit the signals originated by thesesensors in three different dimensions, depending on the main informationcontent embedded in the signals exploited in the application: neuromotorskills, cognitive functions, and behaviors/routines. We then summarize arepresentative selection of existing research datasets in this area, withspecial focus on applications related to user authentication, including keyfeatures and a selection of the main research results obtained on them so far.Then, we perform the experimental work using the HuMIdb database (Human MobileInteraction database), a novel multimodal mobile database that includes 14mobile sensors captured from 600 participants. We evaluate a biometricauthentication system based on simple linear touch gestures using a SiameseNeural Network architecture. Very promising results are achieved withaccuracies up to 87% for person authentication based on a simple and fast touchgesture.

Smartphone Sensors for Modeling Human-Computer Interaction: General Outlook and Research Datasets for User Authentication