Physiological Anxiety Recognition

Anxiety is currently increasing in human daily life. Studies aimed to deepen the understanding of it, to minimize its negative impact on people's lives, have gained significant importance. In this context, the main focus of this work is to study the use of several physiological signals (electrocardiogram, electrodermal activity, and blood volume pulse) to predict the level of anxiety felt by a subject using four different approaches. These involve the use of a different number of features that are selected as more informative, and by training two classification models, with different properties. For the chosen approaches, the obtained results are compared and analysed to understand which performs better, i.e., which has a greater ability in anxiety recognition, being the anxiety level of each participant assessed through the application of the STICSA questionnaire. When selecting 20 features to train a Linear Discriminant Analysis model, an accuracy and precision of over 70% were achieved. This strategy presented the best performance as this model surpasses, for all the used metrics, the results obtained when using the models based on a decision tree. The encouraging obtained results sustained the feasibility of the use of simultaneous different physiological signals to train models for predicting the level of anxiety.