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Data from: Projecting the new body: How body image evolves during learning to walk with a wearable robot

Lee, I-Chieh1; Min, Huan1; Liu, Ming1; Huang, He1

Published Jan 08, 2026 on Dryad. https://doi.org/10.5061/dryad.6hdr7srf6

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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 bodies. While these devices can empower the wearer’s motor performance, there is limited understanding of how wearers update perception of body image (a conscious, subjective experience of one’s own body), especially the image in dynamic movements, while learning to use these devices. This study aimed to fill the gap by examining changes in body image as individuals learned to walk with a robotic leg over multi-day training. We measured gait performance and perceived body image 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 actual motion. Our result confirmed that motor learning improved both physical and perceived gait patterns 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/control of the prosthesis. Additionally, the perceptual overestimation at 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 wearable robots and for developing embodied assistive technologies. In this shared database, we included several key features, which we analyzed in the paper, 1) walking speeds in different training trials; 2) sum of principal angles, which represents the similarity among the gait of participants and normal gait; 3) mean and standard deviation of the SCoMo; 4) participants' own confidence about their own gait interpretion; 5) mutltiple gait features, which are used to define the gait performance, such as stance duration on both legs, symmetry index based on stance time and step length.