Zhang and Okada. Photograph courtesy of San Francisco State University/Paul Asper.
Professors at San Francisco State University (SF State) have teamed up to develop a prosthetic arm that better understands and interprets the EMG signals necessary for elaborate arm, wrist, hand, and finger movements. The project, spearheaded by Kazunori Okada, PhD, an associate professor of computer science, and Xiaorong Zhang, PhD, an assistant professor of engineering, is the first to receive funding from SF State’s Ken Fong Translational Research Fund, which has backed the venture with a $20,000 grant.
Today’s EMG control technology, which is based on single-channel EMG recordings on multiple muscles, can only recognize simple static motions such as hand open or closed due to the lack of meaningful neuromuscular information that can be captured. Yet the complexity of arms and hands requires that multiple muscles fire at varied intensities and often in a set sequence to perform basic tasks.
“Coming up with a good replacement for a lost arm is difficult,” said Okada. “There are still muscles and nerves at the end of the arm, but how they coordinate together is very complex and reading their signals is not an easy thing.”
“Existing design only allows a few simple motions, like opening or closing a hand,” Zhang said. “These are static motions. But if you were to grab a glass of water and drink it, that’s a sequence of motions that is continuous and dynamic.”
To solve the challenge of allowing more complex movements, the project will build on a concept by Zhang that envisions a grid of signal readers capable of capturing richer neural information across both space and time. She and Okada will begin their research by capturing EMG signals from live subjects using electrode grids and analyzing how effective they are in representing the proportional and dynamic muscle activities of hand gestures. As the signals are collected, a computer program will be built to not just read and interpret them but also test out solutions, learn from mistakes and adapt. Zhang will also use her expertise in embedded computer system design to develop a high-performance, real-time computing system to address the computational challenges of applying grid sensing to real-time prosthetic control.
Okada and Zhang acknowledge that such a finished product is likely a long way in the future. For now, they are focused on collecting data and building computer software that analyzes that data and points them toward a solution.
