A transition point: Assistance magnitude is a critical parameter when providing assistance during walking with an energy-removing exoskeleton or biomechanical energy harvester
Bibliographic record
Abstract
Researchers and engineers have developed exoskeletons capable of reducing the energetic cost of walking by decreasing the force their users' muscles are required to produce while contracting. The metabolic effect of assisting concentric and isometric muscle contractions depends, in part, on assistance magnitude. We conducted human treadmill experiments to explore the effects of assistance magnitude on the biomechanics and energetics of walking with an energy-removing exoskeleton designed to assist eccentric muscle contractions. Our results demonstrate that the assistance magnitude of an energy-removing device significantly affects the energetics, muscle activity, and biomechanics of walking. Under the moderate assistance magnitude condition, our device reduced the metabolic cost of walking below that of normal walking by 3.4% while simultaneously producing 0.29 W of electricity. This reduction in the energetic cost of walking was also associated with an 8.9% decrease in hamstring activity. Furthermore, we determined that there is an assistance magnitude threshold that, when crossed, results in the device transitioning from assisting to hindering its user. This transition is marked by significant increases in muscle activity and the metabolic cost of walking. These results could aid in the future design of exoskeletons and biomechanical energy harvesters, as well as adaptive control systems, that identify user-specific control parameters associated with minimum energy expenditure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".