Clinical Gait Analysis: Comparing Explicit State Duration HMMs Using a Reference-Based Index
Bibliographic record
Abstract
In clinical gait analysis, the gait of a patient is recorded with optical motion capture and compared with a healthy reference group. High-dimensional gait datasets are difficult to interpret; machine learning can provide guidance regarding the most relevant gait phases and joint angles for visual analysis and quantify the difference between healthy and pathological gait. We propose an explicit state duration hidden Markov model (HMM) modeling the timeseries data of a subject or a group and the use of a reference-based measure that compares the most likely observations in each state. Based on this stochastic framework, the similarity between healthy and pathological gait can be quantified for each state, each joint angle, and each subject. This concept also includes an overall gait index useful for group comparison or the assessment of an individual's gait. For visualization, joint angle timeseries can be generated from the explicit state duration HMM. The accuracy of the explicit state duration HMM and the performance of the reference-based measures are evaluated on a dataset including strides of healthy subjects and patients suffering from arthritis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".