SEAL: Sensing Efficient Active Learning on Wearables through Context-awareness

In this paper, we introduce SEAL, a co-optimization framework designed to enhance both sensing and querying strategies in wearable devices for mHealth applications. Employing Reinforcement Learning (RL), SEAL strategically utilizes user contextual information and the machine learning model's confidence levels to make efficient decisions. This innovative approach is particularly significant in addressing the challenge of battery drain due to continuous physiological signal sensing, such as Photoplethysmography (PPG). Our framework demonstrates its effectiveness in a stress monitoring application, achieving a substantial reduction of 76% in the volume of PPG signals collected, while only experiencing a minor 6% decrease in user-labeled data quality. This balance showcases SEAL's potential in optimizing data collection in a way that is considerate of both device constraints and data integrity.