Abstract
Computational Ethology is the study of the animal behavior using advances in the field of Computer Vision and Artificial Intelligence. This field of research allows scientists to analyse and characterise behaviors and find out the differences that exist between distinct diseases or disorders for pharmacological studies. In this work we will analyse the data recorded with a multisensor system composed of a top video camera and a piezoelectric pressure sensor that records the movements of an animal. Specifically, this work aims to answer the research question of whether it is possible to differentiate phenotype of an animal model using transfer learning over the pressure signal alone. To do this, the piezoelectric signal will be analysed in the frequency domain by computing its spectrogram, and we segment the chunks corresponding to the locomotion events, previously detected. Convolutional neural models previously trained will be used for classification by applying a transfer learning approach. The results show that an accuracy of more than 96% is obtained and the confirmation that it is possible to classify phenotypes with the data obtained with pressure sensors.
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Notes
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Wild-type gene is a term used to describe a gene when it is found in its natural, non-mutated (unchanged) form.
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Fmr1-knockout (Fmr1-KO) mice may be useful for studying behavioral and synaptic abnormalities associated with Fragile X Syndrome.
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Acknowledgments
This work has been partially supported by grant PRE2018-085294 funded by MCIN/AEI/10.13039 /501100011033 and by “ESF Investing in your future” through the project TIN2017-85827-P, with the grant PRE2018-085294, PID2020-116346GB-I00, and grant IT1689-22 as university research group of excellence from the Basque Government. We gratefully acknowledge the data shared by Prof. Leinekugel to test our approach.
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Aguilar-Moreno, M., Graña, M. (2023). Phenotype Discrimination Based on Pressure Signals by Transfer Learning Approaches. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_12
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