Auto-Augmentation Contrastive Learning for Wearable-based Human Activity Recognition

TL;DR

This paper introduces AutoCL, an end-to-end auto-augmentation contrastive learning framework for wearable-based human activity recognition, reducing manual augmentation efforts and improving recognition accuracy.

Contribution

The paper presents a novel AutoCL method that automatically learns data augmentation strategies within a contrastive learning framework for HAR, enhancing performance without manual augmentation tuning.

Findings

AutoCL outperforms state-of-the-art methods on four HAR datasets.

AutoCL effectively learns augmentation strategies in an end-to-end manner.

AutoCL improves recognition accuracy by reducing augmentation manual effort.

Abstract

For low-semantic sensor signals from human activity recognition (HAR), contrastive learning (CL) is essential to implement novel applications or generic models without manual annotation, which is a high-performance self-supervised learning (SSL) method. However, CL relies heavily on data augmentation for pairwise comparisons. Especially for low semantic data in the HAR area, conducting good performance augmentation strategies in pretext tasks still rely on manual attempts lacking generalizability and flexibility. To reduce the augmentation burden, we propose an end-to-end auto-augmentation contrastive learning (AutoCL) method for wearable-based HAR. AutoCL is based on a Siamese network architecture that shares the parameters of the backbone and with a generator embedded to learn auto-augmentation. AutoCL trains the generator based on the representation in the latent space to overcome the disturbances caused by noise and redundant information in raw sensor data. The architecture empirical study indicates the effectiveness of this design. Furthermore, we propose a stop-gradient design and correlation reduction strategy in AutoCL to enhance encoder representation learning. Extensive experiments based on four wide-used HAR datasets demonstrate that the proposed AutoCL method significantly improves recognition accuracy compared with other SOTA methods.