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

TL;DR

This paper introduces a contrastive learning approach for user identification on smart textiles that achieves high accuracy with limited data, reducing the need for extensive device-specific datasets.

Contribution

It presents a novel contrastive learning-based method with dual branches for user identification across devices using minimal data, addressing data scarcity in smart textile applications.

Findings

Achieves 79.05% accuracy in user identification across devices.

Requires only 2 postures for training, reducing data collection effort.

Outperforms baseline models by 2.62% on average.

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.