Active contrastive coding reducing label effort for sensor-based human activity recognition

Abstract

Human activity recognition (HAR) plays a crucial role in remotely monitoring the health of the elderly. Human annotation is time-consuming and expensive, especially for abstract sensor data. Contrastive learning can extract robust features from weakly annotated data to promote the development of sensor-based HAR. However, current research mainly focuses on the exploration of data augmentation methods and pre-trained models, disregarding the impact of data quality on label effort for fine-tuning. This paper proposes a novel active contrastive coding model that focuses on using an active query strategy to evenly select small, high-quality samples in downstream tasks to complete the update of the pre-trained model. The proposed uncertainty-based balanced query strategy mines the most indistinguishable hard samples according to the data posterior probability in the unlabeled sample pool, and imposes class balance constraints to ensure equilibrium in the labeled sample pool. Extensive experiments have shown that the proposed method consistently outperforms several state-of-the-art baselines on four mainstream HAR benchmark datasets (UCI, WISDM, MotionSense, and USCHAD). With approximately only 10% labeled samples, our method achieves impressive F1-scores of 98.54%, 99.34%, 98.46%, and 87.74%, respectively.