用少量数据实现智能纺织品用户识别，提升跨设备兼容性。
Contrastive Learning-based User Identification with Limited Data on Smart Textiles
- 基于对比学习构建双分支模型，统一不同设备的特征空间。
- 仅需2种姿势数据，在12种坐姿场景下达79.05%识别准确率。
- 适合新设备快速部署，减少数据采集负担，适用于医疗与家居场景。
压力敏感型智能纺织品广泛应用于医疗、运动监测和智能家居领域。集成压力传感阵列的设备有望实现全面场景覆盖与多设备协同。然而，身份识别这一基础功能受限于设备间压力分布差异，需依赖大量设备专属数据集。为此，我们提出一种基于对比学习的新型用户识别方法。设计两个并行分支，分别针对新旧设备进行用户识别，采用监督对比学习在特征空间中促进域统一。面对新设备时，无需大规模数据采集，仅需少量简单姿势数据即可完成识别。在两个含8名受试者的压力数据集（BedPressure 和 ChrPressure）上实验表明，使用仅包含2种姿势的数据集，即可在12种坐姿场景下实现用户识别，平均准确率达79.05%，较最优基线模型提升2.62%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。