Sukyung Park
Korea Advanced Institute of Science and Technology · Engineering
About the Lab
Professor Sukyung Park's research lab specializes in biomechanics, wearable sensing, and neuro-motor control, focusing on developing intelligent systems for human movement analysis and health monitoring. The lab integrates artificial intelligence—particularly deep learning—with wearable inertial sensors and physiological modeling to estimate dynamic biomechanical variables such as ground reaction forces and joint kinetics in real-world settings. A key focus is on creating unobtrusive, wearable technologies for continuous posture and gait monitoring in clinical and sports applications, especially for aging populations and patients with neurological disorders like Parkinson’s disease. The lab also investigates sensory processing in the vestibular system to understand how the brain interprets ambiguous motion cues.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Recent studies have reported the application of artificial neural network (ANN) techniques on data of inertial measurement units (IMUs) to predict ground reaction forces (GRFs), which could serve as quantitative indicators of sports performance or rehabilitation. The number of IMUs and their measurement locations are often determined heuristically, and the rationale underlying the selection of these parameter values is not discussed. Using the dynamic relationship between the center of mass (CoM
Many differences in postural responses have been associated with age and Parkinson's disease (PD), but until now there has been no quantitative model to explain these differences. We developed a feedback control model of body dynamics that could reproduce the postural responses of young subjects, elderly subjects, and subjects with PD, and we investigated whether the postural impairments of subjects with PD can be described as an abnormal scaling of postural feedback gain. Feedback gains quantif
Kinetics data such as ground reaction forces (GRFs) are commonly used as indicators for rehabilitation and sports performance; however, they are difficult to measure with convenient wearable devices. Therefore, researchers have attempted to estimate accurately unmeasured kinetics data with artificial neural networks (ANNs). Because the inputs to an ANN affect its performance, they must be carefully selected. The GRF and center of pressure (CoP) have a mechanical relationship with the center of m
Golf swing segmentation with inertial measurement units (IMUs) is an essential process for swing analysis using wearables. However, no attempt has been made to apply machine learning models to estimate and divide golf swing phases. In this study, we proposed and verified two methods using machine learning models to segment the full golf swing into five major phases, including before and after the swing, from every single IMU attached to a body part. Proposed bidirectional long short-term memory-
Spinal disease is a common yet important condition that occurs because of inappropriate posture. Prevention could be achieved by continuous posture monitoring, but most measurement systems cannot be used in daily life due to factors such as burdensome wires and large sensing modules. To improve upon these weaknesses, we developed comfortable "smart wear" for posture measurement using conductive yarn for circuit patterning and a flexible printed circuit board (FPCB) for interconnections. The cond
We investigated how the nervous system processes ambiguous cues from the otolith organs by measuring roll tilt perception elicited by two motion paradigms. In one paradigm (tilt), eight subjects were sinusoidally tilted in roll with the axis of rotation near ear level. Stimulus frequencies ranged from 0.005 to 0.7 Hz, and the peak amplitude of tilt was 20 degrees . During this paradigm, subjects experienced a sinusoidal variation of interaural gravitational force with a peak of 0.34 g. The secon
Research Areas
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