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

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.