Analysis of Big Data in Running Biomechanics: Application of Multivariate Analysis and Machine Learning Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Much of the biomechanical research over the past 20 years has investigated the influence of potential injury risk factors in isolation. More likely, multiple biomechanical and clinical variables interact with one another and operate as combined risk factors to the point that traditional biomechanical analysis techniques (that is, using discrete variables, such as peak angles, together with a statistical hypothesis test, such as analysis of variance) cannot capture the complexity of these relationships. To identify these complex associations, advanced multivariate analysis and machine learning methods are necessary. However, to build accurate classification and prediction models, an adequate number of samples is needed, which grows exponentially with the number of variables used in the analysis. Therefore, to directly meet this need we have developed the infrastructure and established a worldwide and growing network of clinical and research partners all linked through the world's first automated 3-dimensional (3D) data collection and analysis system: 3D GAIT. Similarly, traditional data analytics may not be able to handle these large volumes of data. Hence, the appropriate multivariate analysis and machine learning methods must be developed. This paper begins with a brief introduction to our 3D data collection system, followed by a discussion of existing multivariate and machine learning methods that can be applied to big data analytics. Next, we provide a comprehensive overview of our proposed methods for 3D kinematic data during running from our database. Finally, important challenges and future research directions are presented.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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