Peiji Chen
Papers
3
Total Citations
8
H-Index
2
About
Peiji Chen is a rising researcher in the fields of rehabilitation robotics, human–machine interaction, and assistive device control. Their work focuses on advancing intuitive control strategies for multi-degrees-of-freedom myoelectric prosthetic hands, addressing the critical gap between commercial availability and user-friendly operation. Chen’s most cited paper (2023, 4 citations) proposes a conditional generative adversarial network (cGAN)-based finger position estimation method, enabling more natural and precise control of complex prosthetic movements. They have also developed a novel gravity compensation mechanism for orthogonal degrees of freedom using coupled springs, wires, and pulleys, validated on a humanoid waist platform (2025, 2 citations). Additionally, Chen introduced a strain gauge-based force myography (FMG) sensor for dual-modal measurement of muscle activity during hand gestures, combining sEMG and FMG for improved gesture recognition (2023, 2 citations). These contributions demonstrate a strong commitment to enhancing the functionality and usability of prosthetic and robotic systems, with potential applications in rehabilitation and human augmentation.
Research Focus
Key Achievements
Top Papers
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