Running Injury Clinic Kinematic Dataset
Bibliographic record
Abstract
OverviewBiomechanics dataset of human subjects (N=1798) walking and or running on a treadmill. Data include 3D marker positions over trials ranging from 25-60 seconds. Also included are demographic information and calculated variables of interest (step with, stride rate, peak knee flexion angle, etc...), sample processing code, and data analysis tutorials.<br>Data DescriptionContained within this dataset are 4 categories of files. They consist of datafiles (.JSON format ->2506 files), metadata (.CSV format ->2 files), Matlab processing code (.M, .MAT format -> 8 files) and Matlab tutorial files (.M, .MLX, .MAT format -> 8 files). All code which accompanies this dataset (processing and tutorials) can be found in the "supplementary_materials.zip" file.Data files are contained within the zipped folder "ric_data" which itself a contains series of folders with names representing the subject ID. Each subject ID folder contains timestamped datafile(s) in ".json" format with each containing walking and/or running data from a single collection session.<br>MethodsThree-dimensional (3D) marker trajectory data were captured using either a 3-camera or an 8-camera VICON motion capture system (Bonita or MX3+, Vicon Motion Systems Oxford, UK) while participants walked or ran on a treadmill. Spherical retro-reflective markers were placed on anatomical landmarks and rigid plates with clusters of 3-4 markers were placed on each of seven lower body segments as per Pohl et al. (Gait Posture. 2010;32(4):559-563.). The marker-set consisted of seven rigid segments and followed International Society of Biomechanics standards. To allow for unobstructed movement during running, anatomical markers were removed following a one second static trial where subjects stood upon a template with their feet positioned straight ahead and 0.3m apart with arms crossed over their chest. Following a warmup period of 2-5mins, kinematic data were collected for approximately 60 seconds while participants walked and then ran at a self-selected speed.Data were collected at the University of Calgary Running Injury Clinic as part of research studies or as part of clinical practice between 2009 and 2017. All subjects provided informed consent and all data were collected under approval from the University of Calgary's Conjoint Health Research Ethics Board (CHREB) (Ethics IDs: E–21705, E–22194, E–24339).<br>More detailsMore details regarding this dataset can be found in the README file. This file contains more detailed descriptions of the contents of the datafiles, processing code, and tutorial code.<br>LicensingThe data is protected under a CC BY 4.0 license. All scripts and functions are protected under a permissive MIT license which can be found in the file LICENSE.txt.
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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 | 0.000 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.699 | 0.986 |
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".