Jonathan D. Stallings
Papers
1
Total Citations
3
H-Index
1
About
Jonathan D. Stallings is a researcher at the intersection of biomedical engineering and machine learning, with a primary focus on advancing prosthetic control systems. His work centers on improving the functionality and usability of robotic hand prosthetics through sophisticated signal processing and pattern recognition techniques. Stallings’ most cited paper, “Functional Variable Selection for EMG-based Control of a Robotic Hand Prosthetic” (2018), addresses a critical limitation in current prosthetic technology: the extensive training required for pattern recognition algorithms and their poor predictive performance under novel conditions. By developing methods to select the most informative features from forearm electromyogram (EMG) signals, his research aims to reduce training burdens and enhance the robustness of prosthetic control. Though his citation count is still growing, his contributions are notable for tackling a practical barrier to widespread prosthetic adoption. Stallings’ work holds promise for improving the quality of life for amputees, and his focus on functional variable selection represents a meaningful step toward more intuitive and reliable neural-machine interfaces.
Research Focus
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Top Papers
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