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Monitoring biological rhythms through the dynamic model identification of an oyster population

Hafiz Ahmed 1 Rosane Ushirobira 1 Denis Efimov 1 Damien Tran 2 Mohamedou Sow 2 Pierre Ciret 2 Jean-Charles Massabuau 2 
1 NON-A - Non-Asymptotic estimation for online systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : The measurements of valve activity in a population of bivalves under natural environmental conditions (16 oysters in the Bay of Arcachon, France) are used for a physiological model identification. A nonlinear auto-regressive exogenous (NARX) model is designed and tested. The method to design the model has two parts. 1) Structure of the model: The model takes into account the influence of environmental conditions using measurements of the sunlight intensity, the moonlight, tide levels, precipitation and water salinity levels. A possible influence of the internal circadian/circatidal clocks is also analyzed. 2) Least square calculation of the model parameters. Through this study, it is demonstrated that the developed dynamical model of the oyster valve movement can be used for estimating normal physiological rhythms of permanently immersed oysters and can be considered for detecting perturbations of these rhythms due to changes in the water quality, i.e. for ecological monitoring.
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Submitted on : Monday, October 26, 2015 - 10:05:35 AM
Last modification on : Tuesday, December 6, 2022 - 12:42:13 PM
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Hafiz Ahmed, Rosane Ushirobira, Denis Efimov, Damien Tran, Mohamedou Sow, et al.. Monitoring biological rhythms through the dynamic model identification of an oyster population. IEEE transactions on systems, man, and cybernetics, 2017, 47 (6), ⟨10.1109/TSMC.2016.2523923⟩. ⟨hal-01220311⟩



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