Emotion Detection Using Noninvasive Low-cost Sensors
ILC-CNR - Aula Seminari IBF SG 5
Emotion recognition from biometrics is relevant to a wide range of application domains, including healthcare and software development.
Existing approaches usually adopt multi-electrodes sensors that could be expensive or uncomfortable to be used in real-life situations.
We investigate whether we can reliably recognize high vs. low emotional valence and arousal by relying on noninvasive low-cost EEG, EMG, and GSR sensors.
We report the results of an empirical study involving 19 subjects in a laboratory setting for emotion elicitation.
We achieved state-of-the-art classification performance for both valence and arousal even in a cross-subject classification setting, which eliminates the need for individual training and tuning of classification models.
Furthermore, we will discuss our ongoing work on the recognition of affective and cognitive states of software engineers during their daily programming tasks.
University of Bari “A. Moro” - Department of Computer Science - Collaborative Development Group