Yi‐Li Tseng

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About

Yi-Li Tseng is a researcher at the forefront of assistive robotics and human–machine interaction, with a primary focus on developing intuitive, bio-signal-driven control systems. Her most cited work, "EMG-based Control Scheme with SVM Classifier for Assistive Robot Arm" (2018, 20 citations), represents a significant contribution to the field by demonstrating how electromyography (EMG) signals can be harnessed to achieve high-accuracy, anthropomorphic control of robotic manipulators. By integrating a Support Vector Machine (SVM) classifier into the control scheme, Tseng’s approach enables assistive robot arms to interpret and respond to human muscle activity with remarkable precision, directly addressing the central challenge of creating seamless, naturalistic interfaces for users with motor impairments. This work has been influential in advancing the practical application of machine learning in rehabilitation robotics, offering a scalable framework for translating physiological signals into reliable, real-time motion commands. Tseng’s research continues to bridge the gap between biological intent and mechanical action, making her a key contributor to the development of more responsive and accessible assistive technologies.