A Low Power Wireless Data Acquisition Device to Monitor Gait Patterns for Children With Toe Walking During Daily Activities
Bibliographic record
Abstract
Clinical gait analysis is the accepted “gold standard” for evaluating an individual’s walking pattern. However, in certain conditions such as idiopathic toe walking (ITW), the degree of voluntary control that a subject may elicit upon their walking pattern in a gait laboratory may not truly reflect their gait during daily activities. Therefore, a battery-powered, wireless data acquisition system was developed to record daily walking patterns to assist in the assessment of treatment outcomes in this patient population. The device was developed to be small (30×50×12 mm3), light-weight (15 g), easy to install, reliable, and consumed little power. It could be mounted across the laces of the shoe, while forces and walking activities were recorded to investigate the percentage of toe walking during the assessment. Laboratory tests were performed and preliminary clinical trials at a gait laboratory were done on six normal gait walkers. These volunteers also try to walk on their toes to simulate the toe walking at the gait laboratory. The system was able to track the gait pattern and determine the percentage of toe walking relative to normal gait. Three boys and one girl were diagnosed with ITW then participated into this study. A total of 4 sets thirty-three 10 min data sessions (5.5 h) were collected outside the laboratory. The results showed that the test subjects walked on their toes 70±4% of the total walking time, which was higher than that they performed 64±5% at the gait laboratory. This preliminary study shows promising results that the system should be able to use for clinical assessment and evaluation of children with ITW.
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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) | 0.000 | 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) | 0.003 | 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".