Kejia Dai

Papers

Total Citations

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About

Kejia Dai is a pioneering researcher in the field of physical human-robot interaction, with a specialized focus on replicating the nuanced dynamics of human touch through robotic systems. His work centers on developing variable viscoelastic and stiffness-controlled manipulators, designed to make robotic handshakes feel more natural and intuitive. Dai’s major contributions include the creation of a handshake manipulator that uses antagonized artificial muscles and a magneto-rheological (MR) fluid brake to mimic the variable viscoelasticity of human joints—a breakthrough that bridges the gap between mechanical precision and biological softness. His most-cited paper (2019, 17 citations) demonstrates this innovation, while his subsequent research (6 citations) validates the hypothesis that perceived handshake firmness correlates with elbow joint stiffness, using EMG signals for objective evaluation. By engineering systems that can adapt their physical properties in real-time, Dai is laying the groundwork for safer, more empathetic robots in healthcare, rehabilitation, and collaborative industry. His work stands out for its elegant fusion of soft robotics and sensory feedback, offering a tangible step toward machines that can truly “feel” during interaction.