Overground Walking With a Transparent Exoskeleton Shows Changes in Spatiotemporal Gait Parameters
Bibliographic record
Abstract
Lower-limb gait training (GT) exoskeletons have been successfully used in rehabilitation programs to overcome the burden of locomotor impairment. However, providing suitable net interaction torques to assist patient movements is still a challenge. Previous transparent operation approaches have been tested in treadmill-based GT exoskeletons to improve user-robot interaction. However, it is not yet clear how a transparent lower-limb GT system affects user's gait kinematics during overground walking, which unlike treadmill-based systems, requires active participation of the subjects to maintain stability. In this study, we implemented a transparent operation strategy on the ExoRoboWalker, an overground GT exoskeleton, to investigate its effect on the user's gait. The approach employs a feedback zero-torque controller with feedforward compensation for the exoskeleton's dynamics and actuators' impedance. We analyzed the data of five healthy subjects walking overground with the exoskeleton in transparent mode (ExoTransp) and non-transparent mode (ExoOff) and walking without exoskeleton (NoExo). The transparent controller reduced the user-robot interaction torque and improved the user's gait kinematics relative to ExoOff. No significant difference in stride length is observed between ExoTransp and NoExo (p = 0.129). However, the subjects showed a significant difference in cadence between ExoTransp (50.9± 1.1 steps/min) and NoExo (93.7 ± 8.7 steps/min) (p = 0.015), but not between ExoTransp and ExoOff (p = 0.644). Results suggest that subjects wearing the exoskeleton adjust their gait as in an attention-demanding task changing the spatiotemporal gait characteristics likely to improve gait balance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".