Domain-Adversarial Anatomical Graph Networks for Cross-User Human Activity Recognition

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. Introduction Human Activity Recognition (HAR) using wearable sensors has transformative applications in healthcare, sports, and smart environments. However, deploying HAR systems across diverse users faces a fundamental challenge: cross-user variability. Differences in body morphology (e.g., limb length, muscle mass) and movement styles (e.g., gait patterns) lead to significant distribution shifts in sensor data. For instance, accelerometer readings from a wrist sensor during "drinking from a cup" can vary substantially between users due to differences in arm motion and grip style. Traditional machine learning models, which assume identical training and testing distributions, often fail to generalize under such shifts. Conventional HAR methods typically involve feature extraction followed by classification using models such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs) [1, 2]. Corresponding author Email addresses: xye685@aucklanduni.ac.nz (Xiaozhou Ye), kevin.wang@auckland.ac.nz (Kevin I-Kai Wang) Preprint submitted to Information Fusion May 13, 2025 To address this limitation, recent research has explored domain adaptation and transfer learning techniques [3]. However, these approaches rely heavily on labeled target-user data, which is often impractical to obtain in real-world scenarios. Domain generalization offers a promising alternative by handling scenarios where no data from the target user(s) is available during training [4, 5]. Despite its potential, most current domain generalization methods focus primarily on aligning user-specific features without considering shared biomechanical patterns that persist across users [6]. Even though users differ in attributes like gender, weight, and height, certain anatomical correlations between body parts remain consistent across individuals.