Zhiping Lai
Papers
2
Total Citations
6
H-Index
2
About
Zhiping Lai is a researcher advancing the frontiers of intelligent surgical systems and human–machine interaction. Their work centers on two key areas: surgical instrument segmentation and surface-electromyography (sEMG)-based gesture recognition. In surgical vision, Lai tackled the challenge of segmenting instruments in robot-assisted surgery—a task far more complex than natural scene segmentation due to variable lighting, tissue occlusion, and instrument similarity. Their 2021 paper, "Semantic Segmentation of Surgical Instruments based on Enhanced Multi-scale Receptive Field," introduced a novel multi-scale feature extraction approach that significantly improved segmentation accuracy, earning 4 citations and laying groundwork for safer, more autonomous surgical robots. In rehabilitation robotics, Lai’s 2022 work, "GTGR-Net: Graph Attentional-Temporal Network for Surface-Electromyography-Based Gesture Recognition," pioneered a graph-based deep learning model that captures both spatial correlations and temporal dynamics of sEMG signals. This innovation enables more reliable hand gesture recognition for active rehabilitation, directly benefiting patients using hand rehabilitation robots. With 2 citations already, this work signals growing impact in assistive technology. Lai’s contributions bridge computer vision and biomedical engineering, offering practical solutions for precision surgery and patient-centered rehabilitation.
Research Focus
Key Achievements
Top Papers
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