mlsanityData to 13 Oct 2026

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

arXiv 2409.07488 ↗First posted 6 Sep 2024eess.SP cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 19 of its 34 references that are in the map. First posted on arXiv 6 Sep 2024.

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References · 19

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7 more
  1. 2021Multimodal Contrastive Training for Visual Representation Learningcited by 122
  2. 2021Weakly Supervised Contrastive Learningcited by 81
  3. 2021Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognitioncited by 59
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  7. 2020Deep Multitask Learning for Pervasive BMI Estimation and Identity Recognition in Smart Bedscited by 3

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