Poster Abstract: Synchronous Automatic Training for Wearable Sensors via Knowledge Distillation

Since wearable sensors often have high substitutability, it is imperative that newly added wearable sensors have automatic training capabilities. In this paper, we propose a novel automatic model training method called Synchronous Automatic Training (SAT) for wearable sensors, which adopts a knowledge distillation technique to fully exploit the knowledge of the existing trained sensors and a synchronous mechanism to ensure the knowledge is transferred to the untrained sensors to assist the automatic model construction. The automatically trained model by using our proposed SAT method has higher accuracy than existing methods.