# Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR

arXiv: https://arxiv.org/abs/2311.12674 · HTML: https://mlsanity.com/paper/2311.12674/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [Exploring Contrastive Learning in Human Activity Recognition for Healthcare](https://mlsanity.com/paper/2011.11542/) (2020)
- [IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and Text](https://mlsanity.com/paper/2210.14395/) (2022)
- [ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition](https://mlsanity.com/paper/2202.00758/) (2022)
- [Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes](https://mlsanity.com/paper/2111.07089/) (2021)

## Built on by (most cited first, 2 of 2)

- [PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2503.17978/) (2025)
- [ALS-HAR: Harnessing Wearable Ambient Light Sensors to Enhance IMU-based Human Activity Recogntion](https://mlsanity.com/paper/2408.09527/) (2024)

## Abstract (arXiv)

Machine learning algorithms are improving rapidly, but annotating training data remains a bottleneck for many applications. In this paper, we show how real data can be used for self-supervised learning without any transformations by taking advantage of the symmetry present in the activities. Our approach involves contrastive matching of two different sensors (left and right wrist or leg-worn IMUs) to make representations of co-occurring sensor data more similar and those of non-co-occurring sensor data more different. We test our approach on the Opportunity and MM-Fit datasets. In MM-Fit we show significant improvement over the baseline supervised and self-supervised method SimCLR, while for Opportunity there is significant improvement over the supervised baseline and slight improvement when compared to SimCLR. Moreover, our method improves supervised baselines even when using only a small amount of the data for training. Future work should explore under which conditions our method is beneficial for human activity recognition systems and other related applications.