Toward Optimal Sampling Rate Selection and Unbiased Classification for Precise Animal Activity Recognition

ArXi:2604.00517v1 Announce Type: cross With the rapid advancements in deep learning techniques, wearable sensor-aided animal activity recognition (AAR) has nstrated promising performance, thereby improving livestock management efficiency as well as animal health and welfare monitoring. However, existing research often prioritizes overall performance, overlooking the fact that classification accuracies for specific animal behavioral categories may remain unsatisfactory. This issue typically stems from suboptimal sampling rates or class imbalance problems.