Towards an anxiety and stress recognition system for academic environments based on physiological features. (July 2020) - Rodríguez-Arce, Jorge; Lara-Flores, Liliana; Portillo-Rodríguez, Otniel; Martínez-Méndez, Rigoberto
Highlights: The physiological features to predict stress and anxiety in academic environments are identified. A stress-inducing protocol was proposed, and 21 physiological features of 5 signals were analyzed. Classification accuracy of stress by 91% (Kappa=0.84) using the KNN classifier and 4 features. Classification accuracy of anxiety by 95% (Kappa=0.90) using the SVM classifier and 3 features. Future applications of the results in the topic were identified. Abstract: Background and Objective: Traditional methods to determine stress and anxiety in academic environments consist of the application of questionnaires, but the main disadvantage is that the results depend on the students' self-perception. Being able to detect anxiety-related stress levels in a simple and objective way contributes greatly to dealing with low performance and school drop-out by students. Methods: The main contribution of this study is to identify the physiological features that could be used as predictors of stressful activities and states of anxiety in academic environments using an Arduino board and low-cost sensors. A test with 21 students was conducted, and a stress-inducing protocol was proposed and 21 physiological features of five signals were analyzed. In addition, the State-Trait Anxiety Inventory (STAI) was used to assess the level of anxiety for each student. Four classifiers were compared to find the physiological feature subset that provides the best accuracy to identify states of stress and anxiety. Results: The stress due to activities performed by students can be identified with an accuracy greater than 90% (Kappa = 0.84) using the k-Nearest Neighbors classifier, using data from heart rate, skin temperature and oximetry signals and four physiological features. Meanwhile, the identification of anxiety was achieved with an accuracy greater than 95% (Kappa = 0.90) using the SVM classifier with data from the galvanic skin response (GSR) signal and three physiological features. Conclusions: The results provide a clue that anxiety detection in academic environments could be done using the analysis of physiological signals instead of STAI test scores. Besides, the results suggest that physiological features could be used to develop stress recognition systems to help teachers to identify the stressful tasks in an academic environment or to develop anxiety recognition systems to help students to control their level of anxiety when they are performing either academic tasks or exams.