TY - JOUR
T1 - Dynamic switched non-parametric identification of the human physiological response under virtual reality stimuli
AU - Hernández-Melgarejo, Gustavo
AU - Fuentes-Aguilar, Rita Q.
AU - Garcia-Gonzalez, Alejandro
AU - Luviano-Juárez, Alberto
N1 - Publisher Copyright:
Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0)
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Non-parametric identification
KW - Physiological signals
KW - Switching systems
KW - Uncertain dynamic systems
KW - Virtual reality
UR - http://www.scopus.com/inward/record.url?scp=85107539463&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2020.12.1968
DO - 10.1016/j.ifacol.2020.12.1968
M3 - Artículo de la conferencia
AN - SCOPUS:85107539463
SN - 1474-6670
VL - 53
SP - 7878
EP - 7884
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
T2 - 21st IFAC World Congress 2020
Y2 - 12 July 2020 through 17 July 2020
ER -