Sangsoo Park
Papers
4
Total Citations
44
H-Index
4
About
Sangsoo Park is a leading researcher at the intersection of rehabilitation robotics, human–machine interaction, and assistive technology. His work centers on decoding human movement intent and reducing physical strain for both users and caregivers. Park’s most impactful contribution is the development of a convolutional neural network (CNN) for classifying upper-limb electromyogram (EMG) signals during reaching-to-grasping tasks, a breakthrough that directly advances intuitive control of prosthetic hands (23 citations). He also pioneered the Exoskeleton Usability Questionnaire (EUQ), the first standardized tool for evaluating lower-limb industrial exoskeletons across mobility, adjustability, handling, and safety—a resource now critical for design validation. In caregiving contexts, Park demonstrated that lift-assist devices significantly reduce caregiver posture risk and muscle load during patient transfers, and he built a quantitative workload assessment system comparing manual care to robot-aided care. His research is distinguished by its translational focus: from neural decoding algorithms to practical usability instruments. With a growing citation record and a clear trajectory toward safer, more effective assistive systems, Park is shaping the future of human-centered robotic support for disability and aging populations.
Research Focus
Key Achievements
Top Papers
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