How to Carry Loads Economically: Analysis Based on a Predictive Biped Model
Bibliographic record
Abstract
Carrying heavy loads costs additional energy during walking and leads to fatigue of the user. Conventionally, the load is fixed on the body. Some recent studies showed energy cost reduction when the relative motion of the load with respect to the body was allowed. However, the influences of the load's relative motion on the user are still not fully understood. We employed an optimization-based biped model, which can generate human-like walking motion to study the load-carrier interaction. The relative motion can be achieved by a passive mechanism (such as springs) or a powered mechanism (such as actuators), and the relative motion can occur in the vertical or fore-aft directions. The connection between the load and body is added to the biped model in four scenarios (two types × two directions). The optimization results indicate that the stiffness values affect energy cost differently and the same stiffness value in different directions may have opposite effects. Powered relative motion in either direction can potentially reduce energy cost but the vertical relative motion can achieve a higher reduction than fore-aft relative motion. Surprisingly, powered relative motion only performs marginally better than the passive conditions at similar peak interaction force levels. This work provides insights into developing more economical load-carrying methods and the model presented may be applied to the design and control of wearable load-carrying devices.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".