Consistency Based Weakly Self-Supervised Learning for Human Activity Recognition with Wearables

TL;DR

This paper introduces a weakly self-supervised learning framework for human activity recognition using wearable sensors, which effectively leverages unlabeled data to improve activity classification and clustering.

Contribution

The paper proposes a novel two-stage weakly self-supervised approach that learns from data nature and fine-tunes with few-shot similarity information, reducing reliance on labeled data.

Findings

Achieves comparable performance to fully supervised methods on benchmark datasets.

Effectively groups similar activities in embedding space without extensive labels.

Enhances clustering accuracy in human activity recognition tasks.

Abstract

While the widely available embedded sensors in smartphones and other wearable devices make it easier to obtain data of human activities, recognizing different types of human activities from sensor-based data remains a difficult research topic in ubiquitous computing. One reason for this is that most of the collected data is unlabeled. However, many current human activity recognition (HAR) systems are based on supervised methods, which heavily rely on the labels of the data. We describe a weakly self-supervised approach in this paper that consists of two stages: (1) In stage one, the model learns from the nature of human activities by projecting the data into an embedding space where similar activities are grouped together; (2) In stage two, the model is fine-tuned using similarity information in a few-shot learning fashion using the similarity information of the data. This allows downstream classification or clustering tasks to benefit from the embeddings. Experiments on three benchmark datasets demonstrate the framework's effectiveness and show that our approach can help the clustering algorithm achieve comparable performance in identifying and categorizing the underlying human activities as pure supervised techniques applied directly to a corresponding fully labeled data set.