Papers
7
Total Citations
65
H-Index
5
About
Lingjie Kong is a researcher at the forefront of integrating advanced machine vision, robotics, and flexible sensor technologies to solve critical challenges in construction automation and wearable health monitoring. Their work is defined by two major thrusts: revolutionizing concrete construction through intelligent robotic systems, and developing high-performance flexible sensors for human motion detection. In the construction domain, Kong has pioneered attention-enhanced machine vision and temporal fusion strategies for precise, real-time monitoring of concrete vibration quality, with their 2023 paper on attention-enhanced machine vision already garnering 20 citations. They have further advanced this field by developing efficient, collision-avoidance path planning for construction vibration robots using Euclidean signed distance fields. Concurrently, Kong has made significant contributions to flexible electronics, notably designing a double-layer corrugated structure that dramatically enhances the linear range of piezoresistive sensors (2025, 14 citations), and an ultra-thin triboelectric sensor for gait monitoring. Their work on DeepSim, a reinforcement learning toolkit for ROS and Gazebo, also demonstrates a commitment to open-source tools that empower other researchers. With a rapidly growing citation impact, Kong is establishing themselves as a key innovator in smart construction and wearable sensing.
Research Focus
Key Achievements
Top Papers
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