Effects of Mass Distribution and Configuration on the Energetic Losses at Impacts of Bipedal Walking Systems
Bibliographic record
Abstract
Understanding the dynamics of human walking is a complex task due to the interaction of the musculoskeletal and the central nervous systems. Nevertheless, the use of simple models can provide useful insight into the mechanical aspects of bipedal locomotion. Such models exploit the observations that human walking significantly relies on passive dynamics and inverted pendulum-like behaviour. The mechanical analysis of walking involves the study of the finite motion single support phase and the impulsive motion of the impacts that occur at heel strike. Such impacts are dominant events because they represent a sudden topology transition and moreover, they are the main cause of energy consumption during the gait cycle. The aim of this work is to gain insight into the dynamics and energetics of heel strike. We use a concept that decouples the dynamics of the biped to the spaces of admissible and constrained motions at the topology transition. This approach is then applied to a straight-legged biped with upper body. Detailed analysis and discussions are presented to quantify the effects of the mass distribution and the impact configuration on the energetics of walking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".