Analysis of Foot-Ground Interaction for Assessment of Gait Using Lab View
Bibliographic record
Abstract
Injuries caused by falls are common among athletes. The field of sports mechanics and training are moving towards implementing green technology to evaluate the performance and training of the athletes. There is a need to understand the gait pattern of an individual for better understanding and planning. Gait analyses performed within a laboratory imposes restriction over an individual’s performance, which can influence the gait pattern intended to study. A shoe-integrated sensor system provides the freedom to move the subject freely, while at the same time provides information related to the gait pattern and fall events occur. MEMS sensors integrated shoes are developed to study the gait pattern for wellness monitoring among elders. The fall is detected by comparing the amount of the pressure distribution in the respective sensors with the accelerometer placed around the ankle. In this work, the presence of the abnormality in the gait pattern and the abnormal point are analysed. A shoe system with sensor design and interface with LabVIEW are developed. In case 1, the heel pressure is 35mV, the toe pressure is 9.2mV, acceleration value is 1.3mV and fall is no fall.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".