pyomeca: An Open-Source Framework for Biomechanical Analysis
Bibliographic record
Abstract
Biomechanics is defined as the study of the structure and function of biological systems by means of the methods of mechanics While musculoskeletal biomechanics branches into several subfields, the data used are remarkably similar. The processing, analysis and visualization of these data could therefore be unified in a software package. Most biomechanical data characterizing human and animal movement appear as temporal waveforms representing specific measures such as muscle activity or joint angles. These data are typically multidimensional arrays structured around labels with arbitrary metadata (Figure Existing software solutions share some limitations. Some of them are proprietary (Damsgaard, Rasmussen, Christensen, Surma, & Zee, 2006) or based on closed-source programming language (Dixon, Loh, Michaud-Paquette, & Pearsall, 2017; Muller, Pontonnier, Puchaud, & Dumont, 2019). Others do not leverage labels and metadata pyomeca is a Python package designed to address these limitations. It provides basic operations useful in the daily workflow of a biomechanical researcher such as reading, writing, filtering and plotting, but also more advanced biomechanical routines geared towards rigid body mechanics and signal processing. By offering a single, efficient and flexible implementation, pyomeca standardizes these procedures, freeing up valuable research time, thereby allowing researchers to focus on the scientific research questions at hand.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.038 |
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".