Haptic rehabilitation exercises performance evaluation using automated inference systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Haptics and virtual environments offer the opportunity to improve the traditional methods of stroke rehabilitation. Traditionally, a therapist has to subjectively evaluate the patient's performance. This paper aims to introduce an automated inference system that utilises haptic data to quantise the patient's performance. Two systems were implemented: a Fuzzy Inference System (FIS) and an Adaptive Neuro-Fuzzy Inference System (ANFIS). The two systems were validated with sample input/output datasets. Testing with real subjects' data has led to the conclusion that the CyberForce system is incapable of providing normative data for evaluating the patient performance due to calibration and consistency issues. This is an expanded version of a paper presented at the 3rd IEEE International Workshop on Medical Measurements and Applications, 9?10 May 2008, Ottawa, ON, Canada.
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.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 it