Towards Approximating Personality Cues Through Simple Daily Activities

The goal of this work is to investigate the potential of making use of simple activity and motion patterns in a smart environment for approximating personality cues via machine learning techniques. Towards this goal, we present a novel framework for personality recognition, inspired by both computer vision and psychology. Results show a correlation between several behavioral features and personality traits, as well as insights of which type of everyday tasks induce stronger personality display. We experiment with the use of support vector machines, random forests and gaussian process classification achieving promising predictive ability, related to personality traits. The obtained results show consistency to a good degree, opening the path for applications in psychology, game industry, ambient assisted living, and other fields.keywordspersonality recognitionbehavior analysismachine learningpersonality traits