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28 September 2015 In-vivo determination of chewing patterns using FBG and artificial neural networks
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Proceedings Volume 9634, 24th International Conference on Optical Fibre Sensors; 963427 (2015) https://doi.org/10.1117/12.2195642
Event: International Conference on Optical Fibre Sensors (OFS24), 2015, Curitiba, Brazil
Abstract
This paper reports the process of pattern classification of the chewing process of ruminants. We propose a simplified signal processing scheme for optical fiber Bragg grating (FBG) sensors based on machine learning techniques. The FBG sensors measure the biomechanical forces during jaw movements and an artificial neural network is responsible for the classification of the associated chewing pattern. In this study, three patterns associated to dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior studies were monitored, rumination and idle period. Experimental results show that the proposed approach for pattern classification has been capable of differentiating the materials involved in the chewing process with a small classification error.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Vinicius Pegorini, Leandro Zen Karam, Christiano Santos Rocha Pitta, Richardson Ribeiro, Tangriani Simioni Assmann, Jean Carlos Cardozo da Silva, Fábio Luiz Bertotti, Hypolito José Kalinowski, and Rafael Cardoso "In-vivo determination of chewing patterns using FBG and artificial neural networks", Proc. SPIE 9634, 24th International Conference on Optical Fibre Sensors, 963427 (28 September 2015); https://doi.org/10.1117/12.2195642
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