  Precision livestock farming optimizes livestock production through the use of
sensor information and communication technologies to support decision making,
proactively and near real-time. Among available technologies to monitor
foraging behavior, the acoustic method has been highly reliable and repeatable,
but can be subject to further computational improvements to increase precision
and specificity of recognition of foraging activities. In this study, an
algorithm called Jaw Movement segment-based Foraging Activity Recognizer
(JMFAR) is proposed. The method is based on the computation and analysis of
temporal, statistical and spectral features of jaw movement sounds for
detection of rumination and grazing bouts. They are called JM-segment features
because they are extracted from a sound segment and expect to capture JM
information of the whole segment rather than individual JMs. Two variants of
the method are proposed and tested: (i) the temporal and statistical features
only JMFAR-ns; and (ii) a feature selection process (JMFAR-sel). The JMFAR was
tested on signals registered in a free grazing environment, achieving an
average weighted F1-score of 93%. Then, it was compared with a state-of-the-art
algorithm, showing improved performance for estimation of grazing bouts (+19%).
The JMFAR-ns variant reduced the computational cost by 25.4%, but achieved a
slightly lower performance than the JMFAR. The good performance and low
computational cost of JMFAR-ns supports the feasibility of using this algorithm
variant for real-time implementation in low-cost embedded systems. The method
presented within this publication is protected by a pending patent application:
AR P20220100910.
