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

Yunkang Zhang, Ziyu Wu, Zhen Liang, Fangting Xie, Quan Wan, Mingjie Zhao, Xiaohui Cai

TL;DR

A novel user identification method based on contrastive learning is proposed to facilitate user identification on both new and existing devices respectively, employing supervised contrastive learning in the feature space to promote domain unification.

Abstract

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.

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

TL;DR

A novel user identification method based on contrastive learning is proposed to facilitate user identification on both new and existing devices respectively, employing supervised contrastive learning in the feature space to promote domain unification.

Abstract

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.
Paper Structure (17 sections, 6 equations, 5 figures, 3 tables)

This paper contains 17 sections, 6 equations, 5 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. Related Work
  3. Transfer Learning
  4. Few-Shot Learning
  5. Contrastive Learning
  6. DataSet
  7. Limited Data-based User Identification
  8. Overview
  9. Design Details
  10. Experiment
  11. User Identification on ChrPressure Dataset
  12. User Identification on BedPressure Dataset
  13. Ablation Study
  14. The Number of Postures
  15. The Sample Number of Each Posture
  16. ...and 2 more sections

Figures (5)

  • Figure 1: Experimental setup and pressure data information
  • Figure 2: Contrastive learning-based user identification framework
  • Figure 3: Confusion matrix for ResNet-34+Nil (left) and Ours (right)
  • Figure 4: User identification accuracy under different number of postures
  • Figure 5: User identification accuracy under different size of each posture