Human-centered intelligent systems
Human–AI Skill Learning through Multimodal Sensing and Embodied Intelligence
I am a Ph.D. candidate at the Human-Centered Intelligent Systems Lab at the Gwangju Institute of Science and Technology, advised by Prof. SeungJun Kim.
My HCI research asks how multimodal human demonstrations can become adaptive feedback for learning physical skills. Using racket sports as a testbed, I combine wearable sensing, movement modeling, and human–AI interaction to build systems that support practice.
Research trajectory
Selected Publications
Five works on sensing, modeling, and coaching physical skills, with a broader application of multimodal human sensing.
MultiSenseBadminton: Wearable Sensor-Based Biomechanical Dataset for Evaluation of Badminton Performance
Scientific Data 11, 343
A multimodal wearable biomechanical dataset for capturing and evaluating badminton performance.
LEGOLAS: Learning & Enhancing Golf Skills through LLM-Augmented System
CHI Late-Breaking Work
An LLM-augmented system for understanding and supporting golf skill learning.
Engagnition: A Multi-Dimensional Dataset for Engagement Recognition of Children with Autism Spectrum Disorder
Scientific Data 11, 299
A multidimensional dataset for recognizing the engagement of children with autism spectrum disorder.
Education
Last updated: 2026-09-24