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

arXiv: https://arxiv.org/abs/2408.07282 · HTML: https://mlsanity.com/paper/2408.07282/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables](https://mlsanity.com/paper/1604.08880/) (2016)
- [Clustering with Deep Learning: Taxonomy and New Methods](https://mlsanity.com/paper/1801.07648/) (2018)
- [Deep convolutional acoustic word embeddings using word-pair side information](https://mlsanity.com/paper/1510.01032/) (2015)
- [Deep Activity Recognition Models with Triaxial Accelerometers](https://mlsanity.com/paper/1511.04664/) (2015)
- [Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using Wearables](https://mlsanity.com/paper/2308.03805/) (2023)
- [Siamese Networks for Weakly Supervised Human Activity Recognition](https://mlsanity.com/paper/2307.08944/) (2023)
- [Unsupervised Embedding Learning for Human Activity Recognition Using Wearable Sensor Data](https://mlsanity.com/paper/2307.11796/) (2023)

## Built on by (most cited first, 1 of 1)

- [Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches](https://mlsanity.com/paper/2512.19713/) (2025)

## Abstract (arXiv)

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.