Affordable erehabilitation monitoring platform
Bibliographic record
Abstract
People who have suffered a motor function disability need to practice appropriate rehabilitation treatments. Motion sensors such as accelerometer and gyroscope in fact are increasingly being embedded in wearable computing devices and can provide a quantitative measure of the human movement for assessment. In this paper, we present a low-cost eRehabilitation platform employing efficient algorithms to provide high accuracy feedback. The provided online rehabilitation service is removing the traditional face-to-face services by using cutting-edge mobile and sensors technologies. It allows doctors to give the patients qualitative feedback and track their progress over time. This system considers the variability in movement speed and accurate angle measurements. To this end, the golden standard pattern collected under physiotherapist supervision is compared with the patient's exercises based on Dynamic Time Warping (DTW) algorithm. The experiments were conducted in a laboratory with different subjects, and results confirm that low-cost MEMS technology achieves an acceptable accuracy level in real-time rehabilitation monitoring. We also address different encountered issues and discuss how to efficiently tackle with them.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".