Sungkyunkwan University · Engineering
Professor Mun-Taek Choi's research lab specializes in intelligent robotics and human-robot interaction, focusing on robust person tracking, trajectory prediction, and autonomous navigation in dynamic environments. The lab develops advanced deep learning and sensor fusion frameworks—integrating LIDAR, RGB-D cameras, and wearable sensors—for real-time human motion analysis, gait classification, and affective state detection in aging populations. Key research directions include robot following under occlusion and illumination changes, spacecraft attitude control under uncertainty, and machine learning for geriatric mental health monitoring using low-cost wearable devices. The lab emphasizes real-world applicability through robust, generalizable models that integrate multimodal data and online learning.
Figures are computed from collected data and may differ slightly.
Human following is one of the fundamental functions in human-robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well as navigates with obstacle avoidance while following the target to the destination. People are detected and tracked using a deep learning algorithm, called Single Shot MultiBox Detector, and the target person is identified by extracting the color fe
There is no distinct clinical classification of post-stroke hemiplegic gaits. However, in contrast to previous studies, more optimal gait types with a high classification performance fully utilizing the kinematic features were identified in this study.
The ability to predict a person’s trajectory and recover a target person in the event the target moves out of the field of view of the robot’s camera is an important requirement for mobile robots designed to follow a specific person in the workspace. This paper describes an extended work of an online learning framework for trajectory prediction and recovery, integrated with a deep learning-based person-following system. The proposed framework first detects and tracks persons in real time using t
The identification of geriatric depression and anxiety is important because such conditions are the most common comorbid mood problems that occur in older adults. The goal of this study was to build a machine learning framework that identifies geriatric mood disorders of depression and anxiety using low-cost activity trackers and minimal geriatric assessment scales. We collected activity tracking data from 352 mild cognitive impairment patients, from 60 to 90 in age, by having them wear activity
A control system to perform spacecraft attitude maneuvers in the presence of mass parameter uncertainties and disturbances is proposed. The control law consisting of a neural network and linear control logic is designed for the spacecraft to follow a pre-specified reference trajectory. Lyapunov stability theory is used to obtain closed-loop stability conditions. These stability conditions serve as constraints imposed on the on-line tuning of the network adjustable weights in the neural network.
It is challenging for a mobile robot to follow a specific target person in a dynamic environment, comprising people wearing similar-colored clothes and having the same or similar height. This study describes a novel framework for a person identification model that identifies a target person by merging multiple features into a single joint feature online. The proposed framework exploits the deep learning output to extract four features for tracking the target person without prior knowledge making
Using big data analysis, we discovered the possibility of detecting disabilities earlier than clinical diagnoses, which would allow us to take appropriate action to prevent disabilities.
Rehabilitation of gait function in post-stroke hemiplegic patients is critical for improving mobility and quality of life, requiring a comprehensive understanding of individual gait patterns. Previous studies on gait analysis using unsupervised clustering often involve manual feature extraction, which introduces limitations such as low accuracy, low consistency, and potential bias due to human intervention. This cross-sectional study aimed to identify and cluster gait patterns using an end-to-en
In this paper, we introduce the human search and identification in the diverse environment without human operation or intervention. A supervisory control system is developed to search and identify a specific enrolled person in an undiscovered room under a wide variety of conditions such as many different illumination conditions, human poses, distances, and indoor configurations. We also consider a substantial exploration with the detector for human evidences that makes path planning suitable for
<sec> <title>BACKGROUND</title> Early detection of childhood developmental delays is very important for the treatment of disabilities. </sec> <sec> <title>OBJECTIVE</title> To investigate the possibility of detecting childhood developmental delays leading to disabilities before clinical registration by analyzing big data from a health insurance database. </sec> <sec> <title>METHODS</title> In this study, the data from children, individuals aged up to 13 years (n=2412), from the Sample Cohort 2.0
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