用解剖关系建图，让动作识别模型跨用户更准。
Domain-Adversarial Anatomical Graph Networks for Cross-User Human Activity Recognition
- 基于解剖关联构建图网络，融合三类生物力学关系。
- 在两个数据集上达到当前最优，对新用户泛化强。
- 适合做跨用户动作识别的工程师和研究者参考。
跨用户人体动作识别（HAR）因传感器位置、身体动态和行为模式差异仍面临重大挑战。传统方法难以捕捉跨用户保持不变的生物力学特征，限制了泛化能力。本文提出边缘增强型图基对抗域泛化框架（EEG-ADG），将解剖相关性知识融入统一图神经网络架构中。通过联合建模三类生物力学驱动的关系——互联单元、对应单元与横向单元——该方法编码域不变特征，并利用变分边特征提取器处理用户特异性差异。梯度反转层（GRL）实现对抗式域泛化，确保对未见用户的鲁棒性。在OPPORTUNITY与DSADS数据集上的大量实验表明，该方法性能达当前最优。本工作将生物力学原理与图基对抗学习相结合，通过信息融合技术构建统一且通用的跨用户HAR模型。
原文摘要 · Abstract (English)
Cross-user variability in Human Activity Recognition (HAR) remains a critical challenge due to differences in sensor placement, body dynamics, and behavioral patterns. Traditional methods often fail to capture biomechanical invariants that persist across users, limiting their generalization capability. We propose an Edge-Enhanced Graph-Based Adversarial Domain Generalization (EEG-ADG) framework that integrates anatomical correlation knowledge into a unified graph neural network (GNN) architecture. By modeling three biomechanically motivated relationships together-Interconnected Units, Analogous Units, and Lateral Units-our method encodes domain-invariant features while addressing user-specific variability through Variational Edge Feature Extractor. A Gradient Reversal Layer (GRL) enforces adversarial domain generalization, ensuring robustness to unseen users. Extensive experiments on OPPORTUNITY and DSADS datasets demonstrate state-of-the-art performance. Our work bridges biomechanical principles with graph-based adversarial learning by integrating information fusion techniques. This fusion of information underpins our unified and generalized model for cross-user HAR.
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