Abstract: A system and method are provided for accurately recognizing actions and estimating action intensity. To enable the system to deal with the uncertainty and varied nature inherent in action recognition and intensity indexing, the system is a hybrid system that combines the concept of fuzzy logic and deep recurrent neural networks. The methodology is an attentive neuro-fuzzy system designed to recognize qualitative differences in human actions and to self-adapt to different intensities. The model of the system and method utilizes recurrent neural networks to detect actions from spatio-temporal patterns of human poses, in tandem with an adaptive fuzzy inference system to learn the various human motions used to perform actions with different intensities and then estimate the action's intensity. The integrated model can successfully learn the unique way a specific action with a certain intensity is performed and can estimate the intensity of the respective action.