Новая схема применения автоматической классификации для анализа социально-экономических систем
An automated real-time classification of human functional states is an important problem for stress resistance evaluation, supervision over operators of critical infrastructure, automated teaching and phobia therapy. In this paper we propose a novel method for binary classification of functional states based on the integrated analysis of (peripheral) physiological parameters: galvanic skin response, respiratory rate, electrocardiographic data, body temperature, electromyographic data, photoplethysmographic data, muscle contraction. The method is based on Gradient Boosted Trees algorithm. A testing of the method showed that in case of stress vs. calm wakefulness differentiation a reliability of the method exceeds 80%.
Automated classification of a human functional state is an important problem, with applications including stress resistance evaluation, supervision over operators of critical infrastructure, teaching and phobia therapy. Such classification is particularly efficient in systems for teaching and phobia therapy that include a virtual reality module, and provide the capability for dynamic adjustment of task complexity. In this paper, a method for automated real-time binary classification of human functional states (calm wakefulness vs. stress) based on discrete wavelet transform of EEG data is considered. It is shown that an individual tuning stage of the classification algorithm — a stage that allows the involvement of certain information on individual peculiarities in the classification, using very short individual learning samples, significantly increases classification reliability. The experimental study that proved this assertion was based on a specialized scenario in which individuals solved the task of detecting objects with given properties in a dynamic set of flying objects.