Abstract
The rapid advances in machine learning (ML) and information fusion have made it possible to use machines/computers with the ability of understanding, recognition, and analysis of human emotion, mood and stress, and related mental diseases. The recognition methods based on physiological modalities are the most performant. Wearable technologies enable non-invasive long-term data gathering and analysis. The number of mental health issues are correlated with emotional states and can be possibly detected by similar methods to general emotion recognition. The scientific interest in the recognition of mental disorders is growing, and most of the available studies are uni-modal based on either ECG or EEG sensor data, while some recent studies also utilize multiple modalities and sensor fusion.
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