Single-Channel sEMG Dictionary Learning Classification of Ingestive Behavior on Cows

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%.