👩🏻🎓 About
I am a Ph.D. student in the Department of Applied Artificial Intelligence at Hanyang University. My research focuses on intelligent haptic interfaces, sensorimotor skill learning, and AI-powered multimodal accessibility systems for blind and low-vision users.
I am particularly interested in how adaptive haptic feedback can enhance dexterous skill acquisition. In parallel, I develop multimodal accessibility systems that combine tactile displays, audio interaction, and AI-based content understanding to support independent information access for BLVI users.
Through the integration of haptics, AI, and user-centered design, I aim to build intelligent interactive systems that improve learning efficiency, performance, and accessibility.
🎉Latest News🎉
🎉 Our paper "Amplifying Force-feedback Cues for Enhancing Dexterous Skill Transfer" got accepted to ICRA 2026!
🎓 Education
Advisor: Prof. Yongjae Yoo
Lab: Multimodal Intelligence & Interaction Laboratory (MILab)
🧐 Research
- Intelligent Haptic Interfaces
- Sensorimotor Skill Transfer in VR
- AI-driven Adaptive Feedback
- Multimodal Accessibility Systems for BLVI
- Human-AI Interaction & Human-Centered AI
📝 Publications
International Conferences
Do Vibrotactile-Rendered Virtual Walking Sensations Reduce VR Cybersickness?
Do Vibrotactile-Rendered Virtual Walking Sensations Reduce VR Cybersickness?
This study investigates whether vibrotactile virtual walking cues can reduce cybersickness and improve comfort during immersive XR experiences.
DOI
To Guide Or To Disturb - How To Teach Dexterous Skills Using AI?
To Guide Or To Disturb - How To Teach Dexterous Skills Using AI?
This project compares AI-driven haptic guidance and disturbance strategies for teaching dexterous skills, and analyzes how adaptive feedback influences learning efficiency.
DOI
Towards Effective Sensorimotor Skill Transfer in VR Engraving Arts
Towards Effective Sensorimotor Skill Transfer in VR Engraving Arts
This work presents an initial comparison between haptic guidance and disturbance for VR engraving, focusing on motor learning transfer across repeated trials.
DOI
A Comparison between Vibrotactile Error-Correction Feedback on Upper and Lower Body in the VR Snowboard Balancing Task
A Comparison between Vibrotactile Error-Correction Feedback on Upper and Lower Body in the VR Snowboard Balancing Task
This paper compares upper-body and lower-body vibrotactile error-correction feedback to improve balance control performance in a VR snowboard task.
DOI
Domestic Conferences
AI-Based Real-Time Audio-Tactile Chart Accessibility System
AI-Based Real-Time Audio-Tactile Chart Accessibility System
This system combines AI-based chart understanding, speech interaction, and tactile output to support accessible data exploration for visually impaired users.
DOI
Audiovisual Feature Extraction for Multimodal Haptic Rendering in 4D Films Based on User Preferences
Audiovisual Feature Extraction for Multimodal Haptic Rendering in 4D Films Based on User Preferences
This work extracts multimodal audiovisual features and maps them to haptic rendering parameters for personalized 4D film experiences.
DOI
Design of a Four-Directional Wind Feedback System Using Video-Based Object Tracking
Design of a Four-Directional Wind Feedback System Using Video-Based Object Tracking
This project introduces a wind-feedback interface driven by video-based object tracking to deliver directional environmental cues in immersive media.
DOI
Segmentation and Learning of Carving Knife's Force-Position Data in Engraving Arts for Guidance Force-feedback Rendering
Segmentation and Learning of Carving Knife's Force-Position Data in Engraving Arts for Guidance Force-feedback Rendering
This project models force-position trajectories in engraving arts and applies segmented learning to improve guidance force-feedback rendering quality.
DOI
VR Balance Enhancement System Using Real-time CoP-based Haptic Feedback
VR Balance Enhancement System Using Real-time CoP-based Haptic Feedback
This paper presents a real-time center-of-pressure driven haptic feedback system for improving balance control in VR training scenarios.
DOI
A Preliminary Study for Data Capturing and VR System to Model and Transfer Dexterous Skills for Sculpture Arts
A Preliminary Study for Data Capturing and VR System to Model and Transfer Dexterous Skills for Sculpture Arts
This demo presents a data-capture and VR transfer pipeline for sculpture-related dexterous skill modeling and training.
DOI
🎖️ Awards & Honors
- Best Paper Award, Korea Computer Congress 2024 (Korean Institute of Information Scientists and Engineers, June 2024)
- Encouragement Award, IC-PBL+ Graduate Championship (Hanyang University, August 2024)
- Grand Prize, Future Mobility Track, Student Idea Competition (Korean Haptics Society and Hyundai Motor Company, November 2023)
💗 Service & Leadership
- Student Volunteer, World Haptics Conference (WHC 2025)
😎 You can also find me here! 😎
Contact
Jiyoung Park
Ph.D. Student, Department of Applied Artificial Intelligence
Hanyang University