Xiaojun Guo

About

Xiaojun Guo is a pioneering researcher at the intersection of soft electronics, human–computer interaction, and intelligent sensing. His work is defined by two complementary thrusts: developing stretchable, skin-like ionotronic sensors for wearable health monitoring, and advancing deep learning–driven 3D perception for noncontact human–machine interfaces. Guo’s most cited paper, “Integrated Soft Ionotronic Skin with Stretchable and Transparent Hydrogel–Elastomer Ionic Sensors for Hand-Motion Monitoring” (2019, 125 citations), introduced a breakthrough approach using ionic hydrogels to create transparent, skin-conformal sensors that mimic biological skin’s mechanotransduction—a foundational contribution to soft robotics and prosthetics. More recently, he has pioneered the fusion of graph deep learning with massive point cloud segmentation for coal mining automation in dusty, low-visibility environments (2023, 78 citations), demonstrating the versatility of his sensing and AI expertise. His 2025 work on integrating 3D deep learning for hand point cloud segmentation directly addresses urgent needs in noncontact rehabilitation and pandemic-resilient interfaces. By bridging soft materials science with robust computer vision, Guo is shaping the future of intelligent, adaptive systems that sense, interpret, and respond to human motion in real-world conditions.

Research Focus

Key Achievements

Top Papers

1. 1
   Integrated Soft Ionotronic Skin with Stretchable and Transparent Hydrogel–Elastomer Ionic Sensors for Hand-Motion Monitoring

   125 citations · 2019
2. 2
   Coal resources under carbon peak: Segmentation of massive laser point clouds for coal mining in underground dusty environments using integrated graph deep learning model

   78 citations · 2023
3. 3
   Integrating 3-D Deep Learning for Hand Point Cloud Segmentation to Enhance Biosensor-Based Human–Computer Interaction

   2 citations · 2025

Key Collaborators

Contact & Links