Dawn Dahn

Papers

Total Citations

H-Index

About

Dawn Dahn’s research lies at the intersection of human-robot interaction and deep learning, with a focus on enabling intuitive, vision-based control of robotic systems. Her most cited work, “Gesture Recognition for Robotic Control Using Deep Learning” (2017, 7 citations), explores how convolutional neural networks can interpret real-time gestures—such as “move forward,” “turn left,” and “stop”—from a camera feed, offering a natural alternative to traditional interfaces. This study demonstrates a practical framework for using small, task-specific gesture sets to command vehicles, addressing key challenges in robustness and latency. While her citation count is modest, Dahn’s contribution is notable for its early application of deep learning to gesture-driven robotics, a field that has since expanded rapidly. Her work provides a foundation for researchers interested in accessible, non-contact control methods, particularly in assistive technology or autonomous systems. By bridging computer vision and robotics, Dahn highlights the potential of neural networks to simplify human-machine communication, making her research a valuable reference for students exploring real-world deep learning deployments.