Quasi-passive shoulder exoskeleton with enhanced assistance variability to adapt to frequent load changes
Bibliographic record
Abstract
To develop an effective quasi-passive (QP) exoskeleton, maximizing its range of variable assistive torques while minimizing the energy required for this variation is crucial. However, achieving this goal has proven challenging so far owing to the common trade-off between the range of variable assistive torques and the energy required for torque variation. Additionally, the shoulder’s standby posture complicates the search for viable solutions. To tackle this issue, we derived design principles based on elastic potential energy field. Utilizing these principles, we developed a QP shoulder exoskeleton called adjustable shoulder exoskeleton (AD Exo), which successfully achieved a wide range of variable assistive torques with significantly reduced required energy for variation. Benchtop tests demonstrated a remarkable enhancement in variability, with a range of assistance spanning 6.37 Nm achieved with a variation energy of 0.9 J. In human trials, AD Exo significantly minimized the average percentage of maximum voluntary contraction in shoulder muscles. Compared to the condition without the exoskeleton (NE), the average muscle activation was reduced by 25% at the adjusted assistance (ADJ), 7.5% at the low assistance (LOW), and 6.7% at the high assistance (HIGH), respectively. Considering repetitive and long-term tasks, this reduction in muscle activity can accumulate, making AD Exo highly effective for alleviating shoulder muscle burden and fatigue. Furthermore, kinematic motions of wearers and actual assistive torque delivered to wearers were examined to analyze the underlying the assistive effect of the exoskeleton.
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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 | 0.000 |
| 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 | 0.000 | 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".