Technological Surrogate Physiotherapy to Improve Knee Health Through Exercise: Human-Computer Interaction to Build Trust and Acceptance Notwithstanding Pain
Bibliographic record
Abstract
A machine-learning system is constructed to alleviate chronic knee pain through exercise and muscle strengthening. Three user-focused features are offered: video-based exercise demonstrations, real-time posture analysis and feedback, and performance and progress tracking. This system, which functions as an artificially-intelligent “technological surrogate physiotherapist,” applies human-computer incentive compatibility and joint learning-by-doing to reify and strengthen motivation, trust and acceptance and to increase effectiveness and efficacy, initial exacerbation of knee pain notwithstanding. In a 3-week experiment involving 60 individuals carrying chronic knee pain, positive and statistically significant outcomes were recorded regarding the Western Ontario and McMaster Universities Osteoarthritis Index physical function (p = 0.001), quality of life (EQ-5D-5L: < 0.001; EQ VAS: p = 0.004), exercise engagement (p < 0.001), system usability, and system acceptance. Technology-based solutions hold significant promise for improving future clinical practice by reducing professional resource demand and increasing the accessibility and caregiver-patient incentive compatibility under physiological healthcare.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".