Hojin Lee received a Ph.D. degree from Haptics and Virtual Reality (HVR) Laboratory at Pohang University of Science and Technology (POSTECH) under the supervision of Prof. Seungmoon Choi. Specifically, he focused on the development and evaluation of the expert driving skill modeling and training (education) to novice drivers in virtual environments using artificial neural networks and haptic assistance. In his Ph.D. phase, he has participated in other research topics as well: such as mid-air laser haptics, haptic memorization methods, English education using haptic feedback, haptic refrigerator prototyping, etc.
He has a strong interest in the implementation and validation of haptic technologies and applications, for various relevant research areas including human skill modeling / learning / transfer, human computer interaction / human robot interaction, perception / psychophysics, cognitive science, and virtual reality.
M.Sc. & Ph.D. Integrated., March 2010 ~ February 2019,
in Computer Science and Engineering at Pohang University of Science and Technology (POSTECH), Republic of Korea. Thesis Title:
Human-like Haptic Assistance: Performance-based Haptic Assistance Using Neural Networks for Driving Skill Enhancement and Training
B.Sc., March 2006 ~ February 2010,
in Computer Science and Engineering at Pohang University of Science and Technology (POSTECH), Republic of Korea.
Work-in-progress poster presented at EuroHaptics, Leiden, The Netherlands, September 2020 (misc)
We introduce our recent study on the characterization of a commercial magnetic levitation haptic interface (MagLev 200, Butterfly Haptics LLC) for realistic high-bandwidth interactions. This device’s haptic rendering scheme can provide strong 6-DoF (force and torque) feedback without friction at all poses in its small workspace. The objective of our study is to enable the device to accurately render realistic multidimensional vibrotactile stimuli measured from a stylus-like tool. Our approach is to characterize the dynamics between the commanded wrench and the resulting translational acceleration across the frequency range of interest. To this end, we first custom-designed and attached a pen-shaped manipulandum (11.5 cm, aluminum) to the top of the MagLev 200’s end-effector for better usability in grasping. An accelerometer (ADXL354, Analog Devices) was rigidly mounted inside the manipulandum. Then, we collected a data set where the input is a 30-second-long force and/or torque signal commanded as a sweep function from 10 to 500 Hz; the output is the corresponding acceleration measurement, which we collected both with and without a user holding the handle. We succeeded at fitting both non-parametric and parametric versions of the transfer functions for both scenarios, with a fitting accuracy of about 95% for the parametric transfer functions. In the future, we plan to find the best method of applying the inverse parametric transfer function to our system. We will then employ that compensation method in a user study to evaluate the realism of different algorithms for reducing the dimensionality of tool-based vibrotactile cues.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems