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Deep Learning (DL) requires lots of time and data, resulting in highcomputational demands. Recently, researchers employ Active Learning (AL) andonline distillation to enhance training efficiency and real-time modeladaptation. This paper evaluates a set of query strategies to achieve the besttraining results. It focuses on Human Pose Estimation (HPE) applications,assessing the impact of selected frames during training using two approaches: aclassical offline method and a online evaluation through a continual learningapproach employing knowledge distillation, on a popular state-of-the-art HPEdataset. The paper demonstrates the possibility of enabling training at theedge lightweight models, adapting them effectively to new contexts inreal-time.

On the Query Strategies for Efficient Online Active Distillation