
Ho Jung Choi
Ph.D. Student in Mechanical Engineering, admitted Summer 2019
All Publications
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Perceived Intensities of Normal and Shear Skin Stimuli Using a Wearable Haptic Bracelet
IEEE ROBOTICS AND AUTOMATION LETTERS
2022; 7 (3): 6099-6106
View details for DOI 10.1109/LRA.2021.3140132
View details for Web of Science ID 000785670500003
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Deep Learning Classification of Touch Gestures Using Distributed Normal and Shear Force
IEEE. 2022: 3659-3665
View details for DOI 10.1109/IROS47612.2022.9981457
View details for Web of Science ID 000908368202109
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Exploratory Hand: Leveraging Safe Contact to Facilitate Manipulation in Cluttered Spaces
IEEE ROBOTICS AND AUTOMATION LETTERS
2021; 6 (3): 5159-5166
View details for DOI 10.1109/LRA.2021.3068941
View details for Web of Science ID 000645056800009
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Dynamically Reconfigurable Tactile Sensor for Robotic Manipulation
IEEE ROBOTICS AND AUTOMATION LETTERS
2020; 5 (2): 2562–69
View details for DOI 10.1109/LRA.2020.2972881
View details for Web of Science ID 000526521500019
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Using force data to self-pace an instrumented treadmill and measure self-selected walking speed.
Journal of neuroengineering and rehabilitation
2020; 17 (1): 68
Abstract
Self-selected speed is an important functional index of walking. A self-pacing controller that reliably matches walking speed without additional hardware can be useful for measuring self-selected speed in a treadmill-based laboratory.We adapted a previously proposed self-pacing controller for force-instrumented treadmills and validated its use for measuring self-selected speeds. We first evaluated the controller's estimation of subject speed and position from the force-plates by comparing it to those from motion capture data. We then compared five tests of self-selected speed. Ten healthy adults completed a standard 10-meter walk test, a 150-meter walk test, a commonly used manual treadmill speed selection test, a two-minute self-paced treadmill test, and a 150-meter self-paced treadmill test. In each case, subjects were instructed to walk at or select their comfortable speed. We also assessed the time taken for a trial and a survey on comfort and ease of choosing a speed in all the tests.The self-pacing algorithm estimated subject speed and position accurately, with root mean square differences compared to motion capture of 0.023 m s -1 and 0.014 m, respectively. Self-selected speeds from both self-paced treadmill tests correlated well with those from the 10-meter walk test (R>0.93,p<1×10-13). Subjects walked slower on average in the self-paced treadmill tests (1.23±0.27 ms-1) than in the 10-meter walk test (1.32±0.18 ms-1) but the speed differences within subjects were consistent. These correlations and walking speeds are comparable to those from the manual treadmill speed selection test (R=0.89,p=3×10-11;1.18±0.24 ms-1). Comfort and ease of speed selection were similar in the self-paced tests and the manual speed selection test, but the self-paced tests required only about a third of the time to complete. Our results demonstrate that these self-paced treadmill tests can be a strong alternative to the commonly used manual treadmill speed selection test.The self-paced force-instrumented treadmill well adapts to subject walking speed and reliably measures self-selected walking speeds. We provide the self-pacing software to facilitate use by gait researchers and clinicians.
View details for DOI 10.1186/s12984-020-00683-5
View details for PubMedID 32493426