Accurate 3D hand pose and pressure sensing is essential for immersive human-computer interaction, yet simultaneously achieving both in mobile scenarios remains a significant challenge. We present WristP2, a camera-based wrist-worn system that estimates 3D hand pose and per-vertex pressure from a single wide-FOV RGB frame in real time. A ViT (Vision Transformer) backbone with joint-aligned tokens predicts Hand–VQ–VAE codebook indices for mesh recovery, while an extrinsics-conditioned branch jointly estimates per-vertex pressure. On a self-collected dataset of 133,000 frames (20 subjects; 48 on-plane and 28 mid-air gestures), WristP2 attains MPJPE (Mean Per-Joint Position Error) of 2.9 mm, Contact \(\operatorname{IoU}\) of 0.712, \(\operatorname{Vol.IoU}\) of 0.618, and foreground pressure MAE of 10.4 g. Across three user studies, WristP2 delivers touchpad-level efficiency in mid-air pointing and robust multi-finger pressure control on an uninstrumented desktop. In a real-world large-display Whac-A-Mole task, WristP2 also enables higher success ratio and lower arm fatigue than head-mounted camera-based baselines. These results position WristP2 as an effective, mobile solution for versatile pose- and pressure-based interaction.

As wireless sensor networks (WSN) is resource-constrained and vulnerable, a secure data gathering scheme is desirable. Compressive data gathering based on compressive sensing (CS) embodies its superiority to reduce the communication energy and prolong the network life. However, Compressive data gathering is prone to attack due to the open environment. In this paper, we propose a novel compressive data gathering scheme based on embedding mechanism. To implement privacy-preserving, the selected nodes determined by the sink embed the blinding factors, while the unselected nodes exploit the confusion factors. This scheme ensures low communication overhead at senor nodes and no aggravating complexity at the sink. Extensive evaluations and comparisons validate the effectiveness of our design.