HuRI Lab focuses on human-centered wearable robotic systems that closely interact with the human body.
Our research integrates wearable robots, sensing technologies, gait analysis, and human–machine interfaces to improve human mobility, rehabilitation outcomes, and accessibility. We pursue four interconnected research areas: exoskeleton systems, gait analysis, wearable force sensing, and assistive human–machine interfaces.
HuRI Lab develops wearable robotic systems—particularly hip-assistive and lower-limb exoskeletons—to support people with motor impairments in daily activities and clinical rehabilitation. Our work spans joint-torque-based assistance strategies, user interface design for powered exoskeletons, and real-world validation through laboratory and clinical studies. We draw on extensive experience with full-body exoskeleton platforms (WalkON Suit) to guide system-level design decisions that prioritize safety, intuitiveness, and long-term wearability.
We study human gait and movement patterns using wearable sensing systems to extract clinically and biomechanically meaningful information—without relying on laboratory-bound motion capture equipment. Our research covers gait event detection (e.g., initial contact timing), recognition of diverse locomotion modes including stair climbing, and data-driven estimation of joint kinematics from on-body sensors. These methods directly inform assistive control strategies and enable quantitative gait assessment in real-world settings, including applications for neurological conditions such as Parkinson's disease.
HuRI Lab designs novel wearable sensing hardware to capture the physical signals that govern human movement and human–robot interaction. Our work ranges from soft pneumatic three-axis force sensors and large-scale multi-axis soft sensor units to leaf-inspired FSR arrays embedded in insoles for mobile ground reaction force (GRF) estimation. These sensor systems provide the foundational data streams that power gait analysis, exoskeleton control, and human intent recognition, and are designed to be compact, robust, and deployable outside the laboratory.
This research area focuses on enabling rich, natural communication between humans and machines through wearable interfaces that detect gesture, muscle activity, and body posture. We develop pneumatic mechanomyography (pMMG) and pressure-sensor-based systems for high-accuracy hand gesture recognition, and explore their application to robot control, rehabilitation, and exercise monitoring.