박숙영 교수
Sukyung Park
KAIST 기계공학과 · 공학
연구실 소개
박숙영 교수의 연구실은 웨어러블 센서와 인공지능 기반의 운동 생체역학 분석을 핵심으로 하며, 관절 운동, 자세 조절, 보행 및 골프 스윙과 같은 운동 패턴의 정량적 분석을 목표로 합니다. 특히 관성측정장치(IMU)와 신경근육 조절 모델을 활용해 보행 중 지면반력력과 중심질량의 동역학을 예측하고, 노인 및 파킨슨병 환자의 자세 조절 이상을 기계학습 기반 모델로 해석합니다. 또한 유연하고 편안한 스마트 웨어러블 기반 자세 모니터링 기술을 개발하여 일상생활에서의 체력 향상과 예방의학에 기여하고자 합니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
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
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