Developing a Pipeline to Convert Marker Less Motion Capture Data from Theia3D into Open Sim for Advanced Biomechanical Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Marker less motion capture technologies, such as Theia3D, have become popular for biomechanical analysis by eliminating the need for physical markers, therefore improving the ease of data collection and post-processing. A major limitation to this technology is the lack of tools that integrate marker less motion capture output into more advanced biomechanical analysis software, such as Open Sim. The absence of a workflow between these software prevents researchers from performing advanced biomechanical analyses using Open Sim’s modelling capabilities Thus, the main objective of this research was to develop a pipeline to convert the output from Theia3D into a compatible format for analysis in Open Sim. A subroutine was developed in Python to convert the Theia3D outputs into a format suitable for Open Sim. The test data included marker less motion data from treadmill running collected in Theia3D by 8 Sony cameras. The output of this data was in an .mot file that was processed to develop a file compatible in Open Sim. This process involved 4 key steps: (a) extracting kinematic data from Theia3D, (b) restructuring matrices to match Open Sim’s input requirements, (c) generating plots to visualize the motion, and (d) producing a compatible file that allowed for model scaling in Open Sim. The tool was tested by processing the Theia3D dataset, and successfully converted the marker less motion capture data into an Open Sim-compatible format that allowed for analysis. This tool provides an advantage to biomechanical researchers by integrating marker less motion capture data into Open Sim, expanding its applications of motion analysis. Currently, this tool has only been tested on a limited dataset, and future work will focus on optimizing the conversion algorithm and expanding compatibility with different movement patterns to enhance the usability and reliability of marker less motion capture data in Open Sim.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it