Papers
4
Total Citations
15
H-Index
2
About
Jun-Kyu Park is a researcher at the forefront of human-robot interaction and smart fitness technology, with a focus on enhancing safety and performance through deep learning and sensor innovation. His work spans two key areas: fault diagnosis for human coexistence robots (HCRs) and vision-based motion tracking for interactive fitness systems. Park’s major contributions include developing a convolutional neural network (CNN) method for proactive fault detection in industrial robots, using time-series data generation and image encoding to prevent safety issues—a critical advancement for collaborative robotics. In fitness technology, he pioneered a smart trampoline system that estimates three-dimensional foot positions from footprint shadows using deep learning, enabling quantitative analysis of jumping exercises and gamified rehabilitation. His 2022 paper on this topic has garnered 6 citations, reflecting its relevance in the post-COVID digital fitness boom. Park has also explored soft tactile sensors for safe human-machine interaction, using vision-based dome sensors and ready-made media. With a total of 15 citations across his most-cited works, his research is shaping safer, smarter, and more engaging systems for both industrial and home environments.
Research Focus
Key Achievements
Top Papers
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