Morgan Miller

Papers

Total Citations

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About

Morgan Miller is a researcher at the forefront of human-robot interaction (HRI), with a focused expertise in using electromyography (EMG) signals for intuitive robotic control. Her most cited work, "Real-Time Classification of Hand Motions Using Electromyography Collected from Minimal Electrodes for Robotic Control" (2021, 12 citations), demonstrates a key contribution: proving that accurate, real-time classification of hand gestures is achievable with a sparse electrode setup. By leveraging machine learning algorithms, Miller’s research directly addresses the challenge of making robotic prosthetics and assistive devices more accessible and less cumbersome, reducing the hardware burden without sacrificing performance. This work has significant implications for the development of low-cost, user-friendly interfaces for amputees and rehabilitation patients. Her approach—prioritizing minimal sensor arrays while maintaining high classification accuracy—represents a practical step toward democratizing advanced HRI technology. Miller’s research continues to bridge the gap between complex neural signal processing and real-world robotic applications, positioning her as an emerging voice in the field of non-invasive neural control.