Unsupervised discriminative feature alignment for domain adaptation in WiFi-based human activity recognition

Abstract

WiFi-based human activity recognition (HAR) models often perform well in a single environment but experience significant accuracy drops in new environments due to variations in spatial settings, human movement, and physical factors. This study introduces an unsupervised domain adaptation (UDA) method, feature alignment-based domain adversarial neural network (FA-DANN), to address these challenges. FA-DANN integrates feature alignment with domain adversarial neural networks (DANN) to improve cross-environment HAR performance. The method is evaluated on three public WiFi datasets—GJWiFi, OPERAnet, and SHARP-2—using 5-fold cross-validation. Without domain adaptation, the baseline CNN-ABiLSTM model achieves an average F1-score of only 15.65%, whereas FA-DANN improves this average F1-score to 92.67%, demonstrating substantial gains. In cross-domain evaluations, leave-one-subject-out cross-validation (LOSOCV) assesses generalization to unseen subjects and environments. FA-DANN outperforms existing DANN-based and Gaussian feature alignment methods, achieving a 19.98% F1-score improvement over state-of-the-art models. Ablation studies further analyze the impact of the decoder and domain classifier components on adaptation. By explicitly aligning target-domain features with source-domain distributions, FA-DANN enables WiFi-based HAR models to generalize across new environments without requiring labeled data, offering a scalable, cost-effective solution for real-world deployment.