Richard Weng

Papers

Total Citations

H-Index

About

Richard Weng is a researcher at the forefront of human-robot interaction (HRI), with a focused expertise in electromyography (EMG)-based control systems. His work centers on developing intuitive, non-invasive interfaces that allow humans to control robotic devices using muscle-generated electrical signals. Weng's most notable contribution is his 2021 paper, "Real-Time Classification of Hand Motions Using Electromyography Collected from Minimal Electrodes for Robotic Control," which has garnered 12 citations. In this study, he demonstrated that machine learning algorithms could accurately classify hand gestures in real time using data from just a few EMG electrodes—a breakthrough that significantly reduces hardware complexity while maintaining high performance. This work addresses a critical bottleneck in HRI: making robotic control accessible and practical for everyday use, from prosthetics to industrial automation. By proving that minimal sensor setups can yield robust control signals, Weng has paved the way for more affordable and user-friendly robotic interfaces. His research continues to bridge the gap between biological signals and machine learning, promising to transform how humans interact with robots in both clinical and assistive contexts.