Dynamic switched non-parametric identification of the human physiological response under virtual reality stimuli ⁎⁎The first author wants to thank CONACYT for the scholarship and financial support 613305. Also this article was partially supported by SIP-IPN under the grant 20201675.

In this work, it is proposed a Switched Differential Neural Networks structure (SDNN) to model the human physiological response in a virtual stimuli scenario. Two physiological variables are assessed: electrocardiography and electrodermal activity, which provide a reflex response after stimuli. The proposed approach is focused on the representation of two discrete primary states, relaxation and stress as the response of the virtual stimuli. A switched dynamic approach is set, in which the trigger of an stimuli generates a change in the heartbeat rate as well as in the skin conductivity, constructing the switch between the mentioned states. The SDNN allows to obtain a model structure whose dynamics corresponds to the rate of change of the physiological variables, given as result a particular class of uncertain switched systems. The proposed non-parametric identification in this switched structure is implemented and experimentally assessed showing appropriate convergence rates in, both, switching regions and the continuous states.