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About

Chenyun Dai is a leading researcher in neurorobotics and human-machine interfaces, with a primary focus on decoding neural drive from high-density surface electromyography (sEMG) for rehabilitation and biometric applications. Their seminal work on estimating muscle force and finger joint angles from neural signals in stroke survivors has advanced robotic therapy for impaired hand function, addressing the critical challenge of robust movement decoding in hemispheric stroke. Dai’s research has garnered over 96 citations, with their 2018 study on muscle force estimation in stroke survivors receiving 36 citations, highlighting its impact on neurorehabilitation. They pioneered a transfer learning-based cross-subject model for continuous finger joint angle estimation, enabling seamless adaptation to new users without extensive recalibration—a breakthrough for multi-user myoelectric control. Additionally, Dai explored sEMG-based biometrics for personal identification, leveraging individual differences in muscle signals for secure authentication. Their editorial work on haptic feedback for neurorobotics underscores their leadership in integrating sensory feedback into assistive technologies. With recent innovations in flexible inter-user calibration for hand gesture recognition, Dai continues to push boundaries, making neural interfaces more intuitive and accessible for rehabilitation and human-machine interaction.