Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition

TL;DR

This paper introduces ClusterCLHAR, a novel contrastive learning framework for human activity recognition that uses clustering to redefine negative samples, improving performance on benchmark datasets.

Contribution

It proposes a clustering-based negative selection method for contrastive learning in HAR, addressing the issue of class similarity in negative sampling.

Findings

Outperforms state-of-the-art methods on USC-HAD, MotionSense, and UCI-HAR datasets.

Achieves higher mean F1-score in self-supervised and semi-supervised learning.

Demonstrates the effectiveness of clustering-based negative selection in HAR.

Abstract

Contrastive learning has been applied to Human Activity Recognition (HAR) based on sensor data owing to its ability to achieve performance comparable to supervised learning with a large amount of unlabeled data and a small amount of labeled data. The pre-training task for contrastive learning is generally instance discrimination, which specifies that each instance belongs to a single class, but this will consider the same class of samples as negative examples. Such a pre-training task is not conducive to human activity recognition tasks, which are mainly classification tasks. To address this problem, we follow SimCLR to propose a new contrastive learning framework that negative selection by clustering in HAR, which is called ClusterCLHAR. Compared with SimCLR, it redefines the negative pairs in the contrastive loss function by using unsupervised clustering methods to generate soft labels that mask other samples of the same cluster to avoid regarding them as negative samples. We evaluate ClusterCLHAR on three benchmark datasets, USC-HAD, MotionSense, and UCI-HAR, using mean F1-score as the evaluation metric. The experiment results show that it outperforms all the state-of-the-art methods applied to HAR in self-supervised learning and semi-supervised learning.