A novel method for sports shoe testing based on a modified prosthetic foot test machine: Poster presented at International Calgary Running Symposium, August 14-17, 2014. Celebrating the Retirement of Dr. Benno M. Nigg
Bibliographic record
Abstract
The study determines the capability and adaption of a dynamic heel-to-toe walking endurance test for prosthetic feet (ISO 22675:2006) to mimic running kinetics and kinematics. The data used for this study was based on captured human running motion from a subject (m, 27 y, 72 kg) in a motion lab. A specific step was selected and processed to be feed into the test machine software. The test machine (Shore Western KS2-07) was modified for three dimensional motions and performed with a prosthetic foot model. A Qualisys motion capture system tracks the motion of the shoe as well as the machine. Forces and moments are acquired with a 6-DoF load cell. Furthermore machine sensor data (shank angle, axial height, acceleration, force) is recorded for comparison. The machine is force controlled and consequently adapts after several steps to the preferred force in shank direction (Fz). Other forces and moments are produced as results of the test setup. As stance time for heel-to-toe running was 0.24 s, the test was performed at different ratios (20% to 80%) of the real time motion to determine the capability of the machine controls to adapt to the motion. The test shows the capability of this approach to reproduce kinetics and kinematics of heel-to-toe running motion. At 60% of real time running speed (0.304 s stance time) forces and moments were reproducible. Based on initial results, machine performance relevant factors were analyzed (e.g. 3D-tilt table shape) to minimize hydraulic piston stroke and allow for the 3D motion.
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How this classification was reachedexpand
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; both teacher heads agree on what is shown here.
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".