Hae-June Park

Papers

Total Citations

H-Index

About

Hae-June Park is a researcher at the forefront of robotic prosthetics and human-machine interaction, with a focus on integrating deep learning into assistive technologies. Their most-cited work, "Grasping Time and Pose Selection for Robotic Prosthetic Hand Control Using Deep Learning Based Object Detection" (2022), has garnered 9 citations, demonstrating early impact in a rapidly evolving field. This research addresses a critical challenge in prosthetic control: enabling intuitive, real-time grasping by leveraging object detection algorithms to optimize hand pose and timing. By bridging computer vision and robotic manipulation, Park’s contributions enhance the autonomy and responsiveness of prosthetic devices, directly improving quality of life for amputees. Their work stands out for its practical application of AI to reduce cognitive load on users, allowing for more natural interaction with everyday objects. While early in their career, Park’s focus on deep learning-driven control systems positions them as a promising innovator in assistive robotics, with potential to influence future developments in bionic limb technology and human-robot collaboration.