| dc.description.abstract | Stress has become a relevant disease in today's society, due to a number of factors linked to the context of contemporary life. This imbalance impacts both the personal and professional spheres of individuals because it is associated with the development of several pathologies. The evidence of the state of stress can be identified through different physiological changes, and wearable sensors can be used to measure these signals automatically. Machine Learning approaches have been used for the automatic identification of stress patterns based on the use of data generated by wearable sensors monitoring physiological signals. Despite positive results, these initiatives present a gap in the combined use of several physiological signals and in the use of biological markers for the annotation of data. In order to explore possibilities to describe a model for classifying stress with multiple physiological signals, experiments were developed with different signal combinations (EMG, EDA and ECG) using different machine learning algorithms, using three different datasets (BeWell, WESAD and Training2017). According to experiments carried out in the context of multisignals, the best result was using ECG and EMG when processed with Gaussian Naïve Bayes, obtaining an accuracy of 90%. | en |
