목록분류 전체보기 (67)
KUCM Bionics Lab.
Abstract Accurate prediction of intended gait speed is crucial for facilitating volitional muscle activity during robot-assisted gait training. Considering that most patients find it difficult to perform gait at relatively high speeds, it is also imperative that the prediction model performs well at slow gait speed. Furthermore, ample time for signal processing should be given prior to robot ass..
Ph.D. in Mechanical Engineering (KAIST, 1998) Research Interests: medical robot (especially lower-limb rehabilitation robot), medical AI, and Human-robot-interface
Abstract Regulation of cell signaling through physical stimulation is an emerging topic in biomedicine. Background: While recent advances in biophysical technologies show capabilities for spatiotemporal stimulation, interfacing those tools with biological systems for intact signal transfer and noncontact stimulation remains challenging. Here, we describe the use of a magnetic torque stimulation ..
M.S in Biomedical engineering (Korea University, 2022) Research Interests: Wearable Robot, Gait Rehabilitation, AI
Abstract The lack of patient effort during robot-assisted gait training (RAGT) is thought to be the main factor behind unsatisfactory rehabilitative efficacy among hemiparetic stroke patients. A key milestone to implement patient-driven RAGT is to predict gait intent prior to actual joint movement. Here, the authors propose a method of predicting step speed intent via surface electromyogram (EMG..
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