A portable measurement system for the evaluation of human gait
Bibliographic record
Abstract
A tool has been developed which can be used to evaluate human gait in a more detailed manner. Its purpose is to record data from an individual during gait then categorize and analyze the intrinsic phases with neuro-fuzzy techniques. The system is simple to use, adaptive, highly mobile, and does not require calibration. The hardware consists of four accelerometers and four force sensitive resistors to record data during walking which is then prepared and collected by a digital device and PDA computer. The gait data is passed into an intelligent fuzzy inference system managed by custom defined fuzzy rules to be classified into four stance phases (heel strike, flat foot, heel lift, toe push-off), and three swing phases (initial flexion, terminal flexion, and terminal extension). After the fuzzy system was trained using data from five healthy subjects, the system's representative gait classification root mean squared error dropped from 0.2975 to 0.1200, showing a much improved ability to categorize human gait, despite its varied nature. The system represents a robust tool, which can be used in a clinical environment for the analysis of human gait in rehabilitative applications such as rule based control generation for functional electrical stimulation, and gait quality analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".