## Introduction The recognition of human activities with wearables has advanced significantly in recent years, but it is still faced with several challenges. Among these, the dependency on labels remains a major obstacle to achieving high accuracy. In this context, a new study presents innovative solutions to reduce label dependency and improve the performance of activity recognition. ## Methodology The researchers developed a weakly supervised framework that combines multiple machine learning techniques, including limited-label weak supervision and multi-tasking. The framework was tested on various activity recognition datasets and demonstrated competitive performance compared to traditional methods.