Capacitive Pressure Sensitive Smart Shoe Insole for Gait Improvement of Cerebral Palsy Patients
Bibliographic record
Abstract
Gait analysis is one of the decision-making factors for treating children with Cerebral Palsy (CP) disease. CP is a group of congenital disorders due to abnormal brain development at the fetal stage, affecting motor functions and movement. About 1,000 children are born with CP each year, and 1,500 children are diagnosed with CP between 4 to 12 years of age. A crucial point is improving the posture through gait analysis using smart insole systems. Here, an insole is designed with capacitive pressure sensors using black carbon in a disposable, non-rechargeable, alkaline 9 V battery. These sensors possess mark-able characteristics of achieving 99% linearity between weight and output capacitance value of the sensor, minimum hysteresis loss of 2 %, negligible time for the response of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$110 \mu$</tex> s to attain 90 % output value from 10 %, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$161 \mu ~\mathrm{s}$</tex> to recover back 10 % when the input is removed. The virtual reality effect is presented with a Graphical User Interface of Python with real-time insole system values and color according to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4}$</tex> different pressure values. Overall, it will help to improve the biomechanics of CP patients through gait analysis of different phases.
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.
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 | 0.000 | 0.000 |
| 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".