S2C-HAR: A Semi-Supervised Human Activity Recognition Framework Based on Contrastive Learning

Abstract

Human activity recognition (HAR) has emerged as a critical element in various domains, such as smart healthcare, smart homes, and intelligent transportation, owing to the rapid advancements in wearable sensing technology and mobile computing. Nevertheless, existing HAR methods predominantly rely on deep supervised learning algorithms, necessitating a substantial supply of high-quality labeled data, which significantly impacts their accuracy and reliability. Considering the diversity of mobile devices and usage environments, the quest for optimizing recognition performance in deep models while minimizing labeled data usage has become a prominent research area. In this paper, we propose a novel semi-supervised HAR framework based on contrastive learning named S²C-HAR, which is capable of generating accurate pseudo-labels for unlabeled data, thus achieving comparable performance with supervised learning with only a few labels applied. First, a contrastive learning model for HAR (CLHAR) is designed for more general feature representations, which contains a contrastive augmentation transformer pre-trained exclusively on unlabeled data and fine-tuned in conjunction with a model-agnostic classification network. Furthermore, based on the FixMatch technique, unlabeled data with two different perturbations imposed are fed into the CLHAR to produce pseudo-labels and prediction results, which effectively provides a robust self-training strategy and improves the quality of pseudo-labels. To validate the efficacy of our proposed model, we conducted extensive experiments, yielding compelling results. Remarkably, even with only 1% labeled data, our model achieves satisfactory recognition performance, outperforming state-of-the-art methods by approximately 5%.