PALM: Personalized Active Learning for mmWave-Based Activity Recognition

Human activity recognition (HAR) plays a crucial role in enhancing human safety and well-being, with applications ranging from dietary management to driver monitoring. Millimeter-wave (mmWave) radars have emerged as a promising technology for HAR due to their ability to capture fine-grained activities without the inconvenience associated with wearable sensors or the potential privacy issues posed by vision-based sensors. In this article, we introduce personalized active learning for mmWave (PALM). Built upon our previously proposed dynamic point cloud recognizer (DPR), PALM addresses the challenge of cold start for new users by utilizing uncertainty to selectively query the user about the most informative samples while training a personalized model. Experiments on our food intake activity dataset (FIAD) demonstrate that PALM attains 91.08% accuracy over a two-week active learning period, surpassing the baseline and alternative uncertainty quantification methods. Furthermore, leveraging transfer learning from our driver activity dataset (DAD), PALM achieves a 9.87% higher accuracy and 19.48% improvement in area under the curve (AUC) compared to the baseline model trained from scratch. These results highlight PALM’s effectiveness in personalizing HAR models while minimizing labeling effort, making it suitable for widespread deployment in real-world applications. In addition, we show that DPR outperforms state-of-the-art voxelization-based methods, achieving a 4.10% increase in accuracy while reducing memory consumption by 78.29% and inference time by 69.64%, leading to resource efficiency.