We presented a wireless Artificial Intelligent (AI)-powered IoT Sensors (AIIS) for laboratory mice motion recognition utilizing embedded micro-inertial measurement units (uIMUs). Based on the AIIS, we have demonstrated a small-animal motion tracking and recognition system that could recognize different combinations of behavior samples, including sleeping, walking, rearing, digging, shaking, grooming, drinking and scratching of mice in cages with accuracies of ∼76.23% to 96.35%. The key advantage of this AIIS-based system is to enable high throughput behavioral monitoring of multiple to a large group of laboratory animals, in contrast to traditional video tracking systems that usually track only single or a few animals at a time. The system collects motion data (i.e., three axes linear accelerations and three axes angular velocities) from the IoT sensors attached to different mice, and classifies these data into different behaviors using machine learning algorithms such as SVM. One of the challenging problems for data analysis is that the distribution of behavior samples is extremely imbalanced. To address this problem, an iteration of sample and feature selection is applied to improve the SVM performance. A combination of oversampling and undersampling is used to handle imbalanced classes, and feature selection provides the optimal number of features.