Fine grain emotional intelligent recognition method for athletes based on multi physiological information fusion

Aiming to solve the problems of low accuracy in collecting multiple physiological information, low recognition rate of fine-grained emotions, and long recognition time in traditional recognition methods, a fine grain emotional intelligent recognition method for athletes based on multi physiological information fusion is proposed. Various physiological information of athletes are collected using ECG sensors, EMG sensors, EDA sensors, as well as airflow sensors to acquire signals such as electrocardiogram, electromyogram, skin conductance, and respiration. The collected information is denoised, and the denoised information is then fused using the Bayesian method. Fuzzy neural networks are used to extract fine-grained emotional characteristics of athletes, and the results of fine-grained emotion recognition are obtained by combining with base classifiers. Experimental results show that the average accuracy of multi-physiological information collection of the proposed method is 97.2%, the average recognition rate is 97.5%, and the average recognition time is 1.41s.