Jinsong Han

Yizhe Lv, Tingting Zhang, Zhijian Wang et al.

Recent advancements in millimeter-wave (mmWave) radar have demonstrated its potential for human action recognition and pose estimation, offering privacy-preserving advantages over conventional cameras while maintaining occlusion robustness, with promising applications in human-computer interaction and wellness care. However, existing mmWave systems typically employ fixed-position configurations, restricting user mobility to predefined zones and limiting practical deployment scenarios. We introduce mmEgoHand, a head-mounted egocentric system for hand pose estimation to support applications such as gesture recognition, VR interaction, skill digitization and assessment, and robotic teleoperation. mmEgoHand synergistically integrates mmWave radar with inertial measurement units (IMUs) to enable dynamic perception. The IMUs actively compensate for radar interference induced by head movements, while our novel end-to-end Transformer architecture simultaneously estimates 3D hand keypoint coordinates through multi-modal sensor fusion. This dual-modality framework achieves spatial-temporal alignment of mmWave heatmaps with IMUs, overcoming viewpoint instability inherent in egocentric sensing scenarios. We further demonstrate that intermediate hand pose representations substantially improve performance in downstream task, e.g., VR gesture recognition. Extensive evaluations with 10 subjects performing 8 gestures across 3 distinct postures -- standing, sitting, lying -- achieve 90.8% recognition accuracy, outperforming state-of-the-art solutions by a large margin. Dataset and code are available at https://github.com/WhisperYi/mmVR.

Fei Wang, Tingting Zhang, Wei Xi et al.

Wi-Fi sensing has emerged as a powerful non-intrusive technology for recognizing human activities, monitoring vital signs, and enabling context-aware applications using commercial wireless devices. However, the performance of Wi-Fi sensing often degrades when applied to new users, devices, or environments due to significant domain shifts. To address this challenge, researchers have proposed a wide range of generalization techniques aimed at enhancing the robustness and adaptability of Wi-Fi sensing systems. In this survey, we provide a comprehensive and structured review of over 200 papers published since 2015, categorizing them according to the Wi-Fi sensing pipeline: experimental setup, signal preprocessing, feature learning, and model deployment. We analyze key techniques, including signal preprocessing, domain adaptation, meta-learning, metric learning, data augmentation, cross-modal alignment, federated learning, and continual learning. Furthermore, we summarize publicly available datasets across various tasks,such as activity recognition, user identification, indoor localization, and pose estimation, and provide insights into their domain diversity. We also discuss emerging trends and future directions, including large-scale pretraining, integration with multimodal foundation models, and continual deployment. To foster community collaboration, we introduce the Sensing Dataset Platform (SDP) for sharing datasets and models. This survey aims to serve as a valuable reference and practical guide for researchers and practitioners dedicated to improving the generalizability of Wi-Fi sensing systems.