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Projecting the new body: How body image evolves during learning to walk with a wearable robot
I-Chieh Lee1,2, Huan Min1,2,3, Ming Liu1,2
1Lampe Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695, USA.
PNAS Nexus
|February 19, 2026
Summary
Learning to walk with a robotic leg improves gait and body perception, but a gap between perceived and actual motion persists. Enhancing sensory feedback and calibrating body image are key for effective wearable robot training.
Area of Science:
- Robotics
- Neuroscience
- Human-Computer Interaction
Background:
- Wearable robots challenge traditional definitions of human motor systems.
- Limited understanding exists on how wearable robots affect users' body image during dynamic movements.
Purpose of the Study:
- To examine changes in perceived body image while learning to walk with a robotic leg.
- To investigate the co-evolution of body image and gait performance during multi-day training.
Main Methods:
- Measured gait performance and perceived body image using the coefficient of perceived motion.
- Applied human motor learning theory to wearer-robot systems.
- Conducted multi-day training sessions with individuals using a robotic leg.
Main Results:
- Motor learning improved both physical and perceived gait patterns, integrating the robotic leg into the sensorimotor system.
- A persistent discrepancy between perceived and actual motion was observed, likely due to lack of prosthesis sensation.
- Perceptual overestimation in later sessions may hinder further motor improvement.
Conclusions:
- Wearable robot training improves coordination but highlights the need for enhanced sensory feedback and body image calibration.
- Addressing the perception-action gap is crucial for optimizing training and developing effective embodied assistive technologies.
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