An outdoor dual-task study on cognitive-motor interference during exoskeleton-assisted walking
Bibliographic record
Abstract
Introduction Controlling a powered lower-limb exoskeleton may increase the demand for cognitive resources due to mechanical constraints and assisting torques that disrupt natural movement. Method This study employed a dual-task paradigm to investigate cognitive-motor interferences and short-term familiarization effects in an outdoor walking experiment with twenty healthy adults using a powered lower-limb exoskeleton. Results and discussion In contrast to unassisted walking, performing a serial subtraction task during exoskeleton-assisted walking led to a slight increase in gait velocity and a reduction in stride time variability. This suggests that externalizing attention may facilitate the coordination with external rhythmic cues provided by the exoskeleton. Concurrently, cognitive performance, measured by correct response rates, declined during exoskeleton-assisted walking, indicating a posture-first strategy. Short-term familiarization during exoskeleton-assisted walking reduced perceived workload and improved cognitive performance, yet cognitive performance remained lower than in both the seated control condition and unassisted walking. This suggests that walking with the exoskeleton continues to require significant attentional resources. These findings emphasize the necessity of evaluating the cognitive fit of exoskeletons to ensure safe human-exoskeleton interaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".