Short-term anxiety recognition from blood volume pulse signal

In this paper we focus our attention on the development of a virtual reality exposure system to induce anxiety and a strategy for recognizing it. We describe anxiety detection in short term from blood volume pulse measurement; detailing data collection, features extraction and classification. We built a model of anxiety detection using support vector machines and evaluate it on the collected data. Results show that the choice of relevant features allowed good anxiety recognition.