Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical fiber Bragg grating sensors (FBG) that are processed by machine learning techniques. The FBG sensors measure the biomechanical strain during jaw movements, and a decision tree is responsible for the classification of the associated chewing pattern. In this study, patterns associated with food intake of dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior were monitored: rumination and idleness. Experimental results show that the proposed approach for pattern classification is capable of differentiating the five patterns involved in the chewing process with an overall accuracy of 94%.

In this paper, a novel and simple method to measure the phase difference between two sinusoidal signals is presented. Basically, the method consists of subtracting two sinusoidal signals with same frequencies and measuring the resulting signal amplitude: this amplitude being a minimum whenever there is a coincidence between both signal phases. In order to test this method, an adjustable phase reference signal has been generated using a direct digital synthesizer device. Employing this reference signal, it can be demonstrated that the phase difference between two signals can be evaluated with errors smaller than 0.3° for signals with frequencies up to 1.25 MHz.

Monitoring the ingestive behavior, especially rumination and eating, is important to assess animals’ health, welfare, needs, and to estimate production efficiency. In this paper, the myographic response of masticatory muscles was acquired from cows to directly access ingestive information (grazing and ruminating patterns) with high accuracy and time precision. Myographic signal processing is usually based on the same framework and optimal feature combinations have been extensively researched. However, recognition rates of hand-crafted feature based classifiers may be sensible to data deviation. In order to avoid the aforementioned issues and improve robustness, we propose an approach using sparse representation based classifiers which dismiss the need to select the best feature combination and classifier for the application. Also, we present a novel segmentation protocol to select chew related signal windows on cows. Four state-of-the-art multi-feature sets combined with linear discriminant analysis (LDA) were compared to a sparse representation based classifier called Fisher Discriminant Dictionary Learning (FDDL). Results suggest a significantly better performance of FDDL (p<0.05), achieving an accuracy rate of 90%, even in the presence of noise (0 - 20 dB), having a performance gain of 2.45% (SNR of 0 dB) in relation to the best hand-crafted feature set. When the noise was added just to the test set, all others classifiers remained under 75% (near 50%) for severe noise (0 - 10 dB), while FDDL stayed over 85%.

This paper presents the development of a displacement measurement system formed by a Linear Variable Differential Transformer (LVDT) and an electronic instrumentation circuit. A mechanical structure was constructed as a coil to be elaborated from a preliminary analysis of the response of the device. A circuit was developed for the excitation of the LVDT, analog processing and signal conditioning in order to obtain a proportional DC signal for the displacement in its core. The response of the measuring system was obtained for a total range of 80 mm of displacement. The measurement deviations were less than + 0.16 mm and - 0.15 mm.