Mengqian Chen
Papers
2
Total Citations
10
H-Index
2
About
Mengqian Chen is a robotics researcher focused on human–robot interaction and assistive healthcare technologies. Her work centers on developing intelligent control systems for nursing-care and patient-transfer robots, with an emphasis on reducing physical strain on caregivers while ensuring patient safety. Chen’s most-cited paper, “Accurate and real-time human-joint-position estimation for a patient-transfer robot using a two-level convolutional neural network” (2021, 8 citations), introduces a deep learning approach that enables real-time, precise joint tracking—a critical component for safe robotic assistance. In her more recent work (2024, 2 citations), she advances a human–robot mechanics model that identifies hip torque during dual-arm transfers, addressing the challenge of accurate force estimation in adaptive robotic systems. By integrating neural networks with biomechanical modeling, Chen’s research bridges the gap between autonomous perception and physical human–robot collaboration. Her contributions are particularly relevant to the growing field of assistive robotics, where accurate sensing and control are essential for real-world deployment. Chen’s work lays important groundwork for safer, more responsive robotic caregivers in clinical and home settings.
Research Focus
Key Achievements
Top Papers
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- 2