Handcrafted Features Versus Learned Representations for IMU-Based Goat Behavior Classification: Accuracy, Validation...

Comparisons of behavior classification pipelines rarely vary one design factor at a time or report computational cost. Six-axis inertial measurement unit data were recorded at 25 Hz from collar-mounted sensors on 19 Korean black goats, with labels from a semi-automatic procedure verified against synchronized video. Six models classifying rumination, eating, and other behavior were compared on identical 10 s windows, varying feature source, classifier, extractor architecture, and training objective one at a time, under leave-one-subject-out (LOSO) and stratified 5-fold cross-validation (CV). A 24-dimensional learned representation matched a 250-dimensional handcrafted feature set (Macro F1 0.810 versus 0.814 under LOSO, 0.876 versus 0.876 under 5-fold CV), and two structurally unrelated extractors reached this parity. Replacing the supervised training objective with unsupervised reconstruction reduced Macro F1 by 0.094-0.127. Window-level estimates exceeded LOSO estimates by 0.044-0.095, and between-subject variability was roughly five times the spread among the five supervised models. Sampling at 10-15 Hz retained most of the accuracy at 40-60% lower data volume, and the learned pipelines cut inference time by up to 36-fold in this implementation. These operating points apply to sustained behavioral episodes under the conditions studied; where accuracy cannot separate models, computational cost should govern the choice.