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

TL;DR

This paper introduces a novel graph neural network framework that leverages biomechanical knowledge and adversarial learning to improve cross-user human activity recognition, achieving state-of-the-art results.

Contribution

It presents a new Edge-Enhanced Graph-Based Adversarial Domain Generalization framework that encodes biomechanical invariants and enhances generalization across users in HAR.

Findings

Achieves state-of-the-art performance on OPPORTUNITY and DSADS datasets.

Effectively encodes biomechanical invariants for cross-user generalization.

Demonstrates robustness to unseen users through adversarial training.

Abstract

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.