Ecological Interface Design for Knee and Hip Automatic Physiotherapy Assistant and Rehabilitation System
Bibliographic record
Abstract
In this paper, we report on a recent interface design and evaluation process for a new knee and hip automatic physiotherapy assistant and rehabilitation system (ARS). Interface design was concurrent with the development of ARS. The ARS has the potential to improve the automation of rehabilitation treatments, by providing quantitative measures of a patient’s motion. However, the complexity of rehabilitation information available to the therapist has increased with this additional information. We applied Ecological Interface Design (EID) to understand the domain of physiotherapy and the role of the automation. Results of a Work Domain Analysis (WDA) revealed new functions and constraints in rehabilitation now accessible through the ARS, and provided the design requirements for interface design. A novel interface was designed which is currently undergoing evaluation to see if it improves the quality and experience of physiotherapy. This study provides an example of the advantages of using EID at the early phase of design, and how to apply EID to a system of increasing automaticity.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".