Active Domain Adaptation for mmWave-based HAR via Renyi Entropy-based Uncertainty Estimation

TL;DR

This paper introduces mmADA, an active domain adaptation framework for mmWave-based human activity recognition that uses uncertainty estimation and contrastive learning to adapt models efficiently across different users and environments.

Contribution

The paper presents a novel active domain adaptation method using Renyi entropy and contrastive learning to improve mmWave HAR performance with minimal labeled data.

Findings

Achieves over 90% accuracy in cross-domain settings

Outperforms five baseline methods in adaptation tasks

Demonstrates robustness across unseen users and environments

Abstract

Human Activity Recognition (HAR) using mmWave radar provides a non-invasive alternative to traditional sensor-based methods but suffers from domain shift, where model performance declines in new users, positions, or environments. To address this, we propose mmADA, an Active Domain Adaptation (ADA) framework that efficiently adapts mmWave-based HAR models with minimal labeled data. mmADA enhances adaptation by introducing Renyi Entropy-based uncertainty estimation to identify and label the most informative target samples. Additionally, it leverages contrastive learning and pseudo-labeling to refine feature alignment using unlabeled data. Evaluations with a TI IWR1443BOOST radar across multiple users, positions, and environments show that mmADA achieves over 90% accuracy in various cross-domain settings. Comparisons with five baselines confirm its superior adaptation performance, while further tests on unseen users, environments, and two additional open-source datasets validate its robustness and generalization.