Hongjing Teng

Nanjing University of Science and Technology

Papers

Total Citations

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About

Hongjing Teng is a researcher at the forefront of assistive robotics and human–machine interaction, with a primary focus on developing intelligent navigation and motion estimation systems for vulnerable populations. Her work centers on two key areas: enhancing mobility for visually impaired individuals through robotic navigation aids, and improving human motion estimation for rehabilitation and performance enhancement. In her highly cited 2024 paper, Teng introduced a novel walking path generation method for the Smart Cane—a Robotic Navigation Assistance Device (RNA)—that leverages a Linear Inverse Pendulum Model (LIPM) to incorporate bipedal constraints, significantly improving indoor navigation safety and efficiency for visually impaired users. This work has already garnered 11 citations, reflecting its immediate impact on assistive technology. Additionally, her research on estimating elbow joint angles using a Graph Convolution Neural Network (GCNN) that fuses electromyography (EMG) and inertial measurement unit (IMU) data addresses a longstanding challenge in achieving high-precision joint angle measurement from surface EMG. This contribution, with 3 citations, holds promise for advancing human–robot interaction in rehabilitation robotics. Teng’s innovative integration of biomechanical modeling with machine learning positions her as a rising leader in creating more responsive and adaptive assistive devices.