Contrastive Learning for Multimodal Human Activity Recognition with Limited Labeled Data

TL;DR

This paper introduces CLMM, a contrastive learning framework that enhances multimodal human activity recognition with limited labeled data through a two-stage training process.

Contribution

The paper presents a novel two-stage contrastive learning approach with a CNN-DiffTransformer encoder and dual-branch architecture for improved recognition.

Findings

CLMM outperforms state-of-the-art methods in accuracy.

CLMM demonstrates faster convergence in experiments.

The framework effectively utilizes limited labeled data.

Abstract

Human activity recognition serves as the foundation for various emerging applications. In recent years, researchers have used collaborative sensing of multi-source sensors to capture complex and dynamic human activities. However, multimodal human activity sensing typically encounters highly heterogeneous data across modalities and label scarcity, resulting in an application gap between existing solutions and real-world needs. In this paper, we propose CLMM, a general contrastive learning framework for human activity recognition that achieves effective multimodal recognition with limited labeled data. CLMM employs a novel two-stage training strategy. In the first stage, CLMM employs a CNN-DiffTransformer encoder to capture cross-modal shared information by extracting local and global features. Meanwhile, a hard-positive samples weighting algorithm enhances gradient propagation to reinforce shared learning. In the second stage, a dual-branch architecture combining quality-guided attention and bidirectional gated units captures modality-specific information, while a primary-auxiliary collaborative training strategy fuses both shared and modality-specific information. Experimental results on three public datasets demonstrate that CLMM significantly improves state-of-the-art baselines in both recognition accuracy and convergence performance.