Automatic Recognition of Psychophysiological States Based on Multimodal Data Analysis
The development of methods for automatic recognition of human psychophysiological states based on multimodal data analysis represents one of the priority directions in contemporary technical science. It finds broad applications in telemedicine systems, human-computer interfaces, educational technologies, and digital mental health services. Computational approaches that enable the detection of various emotional states, as well as stress and depression, based on audio and physiological signals under conditions of limited access to expert diagnostics, high privacy requirements, and multilingual diversity are of particular importance.
At the current stage of the discipline, the primary focus lies on deep learning models, which demonstrate high accuracy when large volumes of annotated data are available. However, such methods have a number of significant limitations: they are characterized by high computational complexity and lack interpretability, which restricts their use in tasks related to psychophysiological assessment, especially in clinical or user-sensitive scenarios.
Thus, the relevance of this research is determined by the necessity to create computationally efficient and interpretable methods for multimodal data analysis that ensure high-quality recognition of psychophysiological states and suitability for practical use in various applied scenarios.
