Diffusion Model-based Contrastive Learning for Human Activity Recognition

TL;DR

This paper introduces a diffusion model-based contrastive learning framework for WiFi CSI-based human activity recognition, addressing limited data and generalization issues with novel augmentation and adaptive weighting techniques.

Contribution

The paper proposes a diffusion model tailored for time series augmentation and an adaptive weighting algorithm to improve CSI-based activity recognition.

Findings

Significant performance improvements over state-of-the-art methods.

Effective data augmentation reduces data distortion.

Adaptive weighting enhances sample importance differentiation.

Abstract

WiFi Channel State Information (CSI)-based activity recognition has sparked numerous studies due to its widespread availability and privacy protection. However, when applied in practical applications, general CSI-based recognition models may face challenges related to the limited generalization capability, since individuals with different behavior habits will cause various fluctuations in CSI data and it is difficult to gather enough training data to cover all kinds of motion habits. To tackle this problem, we design a diffusion model-based Contrastive Learning framework for human Activity Recognition (CLAR) using WiFi CSI. On the basis of the contrastive learning framework, we primarily introduce two components for CLAR to enhance CSI-based activity recognition. To generate diverse augmented data and complement limited training data, we propose a diffusion model-based time series-specific augmentation model. In contrast to typical diffusion models that directly apply conditions to the generative process, potentially resulting in distorted CSI data, our tailored model dissects these condition into the high-frequency and low-frequency components, and then applies these conditions to the generative process with varying weights. This can alleviate data distortion and yield high-quality augmented data. To efficiently capture the difference of the sample importance, we present an adaptive weight algorithm. Different from typical contrastive learning methods which equally consider all the training samples, this algorithm adaptively adjusts the weights of positive sample pairs for learning better data representations. The experiments suggest that CLAR achieves significant gains compared to state-of-the-art methods.