# Contrastive Learning-based User Identification with Limited Data on Smart Textiles

arXiv: https://arxiv.org/abs/2409.07488 · HTML: https://mlsanity.com/paper/2409.07488/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 6 Sep 2024.

## Builds on (most cited first, 19 of 19)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [A Brief Review of Domain Adaptation](https://mlsanity.com/paper/2010.03978/) (2020)
- [Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning](https://mlsanity.com/paper/2011.01403/) (2020)
- [Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/1909.00589/) (2019)
- [Multimodal Contrastive Training for Visual Representation Learning](https://mlsanity.com/paper/2104.12836/) (2021)
- [Weakly Supervised Contrastive Learning](https://mlsanity.com/paper/2110.04770/) (2021)
- [Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2109.09559/) (2021)
- [Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model](https://mlsanity.com/paper/2011.11499/) (2020)
- [COCOA: Cross Modality Contrastive Learning for Sensor Data](https://mlsanity.com/paper/2208.00467/) (2022)
- [Unsupervised Speech Segmentation and Variable Rate Representation Learning using Segmental Contrastive Predictive Coding](https://mlsanity.com/paper/2110.02345/) (2021)
- [Deep Multitask Learning for Pervasive BMI Estimation and Identity Recognition in Smart Beds](https://mlsanity.com/paper/2006.10453/) (2020)

## Abstract (arXiv)

Pressure-sensitive smart textiles are widely applied in the fields of healthcare, sports monitoring, and intelligent homes. The integration of devices embedded with pressure sensing arrays is expected to enable comprehensive scene coverage and multi-device integration. However, the implementation of identity recognition, a fundamental function in this context, relies on extensive device-specific datasets due to variations in pressure distribution across different devices. To address this challenge, we propose a novel user identification method based on contrastive learning. We design two parallel branches to facilitate user identification on both new and existing devices respectively, employing supervised contrastive learning in the feature space to promote domain unification. When encountering new devices, extensive data collection efforts are not required; instead, user identification can be achieved using limited data consisting of only a few simple postures. Through experimentation with two 8-subject pressure datasets (BedPressure and ChrPressure), our proposed method demonstrates the capability to achieve user identification across 12 sitting scenarios using only a dataset containing 2 postures. Our average recognition accuracy reaches 79.05%, representing an improvement of 2.62% over the best baseline model.