Projecting the New Body: How Body Image Evolves During Learning to Walk with a Wearable Robot

TL;DR

This study investigates how body image perception evolves during gait training with a wearable robot, revealing that perception and actual gait improve together but discrepancies persist, highlighting the importance of sensory feedback.

Contribution

It extends human motor learning theory to wearable robots, demonstrating co-evolution of perceived and actual gait and identifying perceptual overestimation as a limiting factor.

Findings

Motor learning improved gait towards normal patterns.

Perceived body image co-evolved with actual gait improvements.

Persistent perceptual discrepancies suggest need for sensory feedback enhancements.

Abstract

Advances in wearable robotics challenge the traditional definition of human motor systems, as wearable robots redefine body structure, movement capability, and perception of their own bodies. We measured gait performance and perceived body images via Selected Coefficient of Perceived Motion, SCoMo, after each training session. Based on human motor learning theory extended to wearer-robot systems, we hypothesized that learning the perceived body image when walking with a robotic leg co-evolves with the actual gait improvement and becomes more certain and more accurate to the actual motion. Our result confirmed that motor learning improved both physical and perceived gait pattern towards normal, indicating that via practice the wearers incorporated the robotic leg into their sensorimotor systems to enable wearer-robot movement coordination. However, a persistent discrepancy between perceived and actual motion remained, likely due to the absence of direct sensation and control of the prosthesis from wearers. Additionally, the perceptual overestimation at the later training sessions might limit further motor improvement. These findings suggest that enhancing the human sense of wearable robots and frequent calibrating perception of body image are essential for effective training with lower limb wearable robots and for developing more embodied assistive technologies.