Papers
1
Total Citations
5
H-Index
1
About
Pan Yao is a rising innovator in the field of human-machine interaction (HMI) and wearable robotics, with a focused expertise in surface electromyography (sEMG) signal processing and edge artificial intelligence. His most cited work, "sEMG-Based Wearable HMI System For Real-Time Robotic Arm Control With Edge AI" (2023), addresses a critical challenge in HMI: enabling real-time, intelligent gesture recognition on resource-constrained wearable devices. By integrating deep learning with edge computing, Yao’s research demonstrates how to achieve natural, low-latency control of robotic arms without relying on cloud processing, thereby enhancing user autonomy and system responsiveness. This contribution is pivotal for advancing assistive technologies, prosthetics, and industrial automation. With 5 citations since its publication, his work is gaining traction among researchers seeking practical, deployable solutions for real-time biosignal interpretation. Yao’s approach bridges the gap between high-accuracy deep learning models and the stringent computational limits of wearable hardware, marking him as a key contributor to the next generation of intuitive, AI-driven HMI systems.
Research Focus
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Top Papers
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