Bibliographic record
Abstract
Increased awareness of the signs of stroke and better stroke management has increased survival rate close to 90% in Canada. However, this means each year close to 4000 Albertans require rehabilitation to re-learn skills for performing daily activities. A major challenge for therapists carrying out rehabilitation is the assessment of motor and sensory functions, especially of the upper extremities. Assessment tools range from therapists using simplistic observer based ordinal scales to more quantitative research-based tools such as the NEOFECT data gloves, using which patients can perform virtual reality-based exercises displayed on a computer screen or using virtual reality headsets. However, existing smart-gloves are marketed towards a clinic, not home, environment. Apart from their high initial $15,000USD cost, they require additional interfacing including costly and precise installation of cameras/base-stations for position tracking in addition to headsets to interact with the virtual reality environments. We propose a prototype of a data glove and arm tracking system with a variety of low-cost, position, orientation, and feedback sensors to supplement clinic assessments, and enable their continued use at home. Preliminary results for gamified rehabilitation exercises to encourage participation in a wider variety of motor and sensory tasks using smart-glove monitoring will be presented. Covid-19 restrictions have limited the ability to evaluate the accuracy and effectiveness of the glove monitoring by comparison to existing assessment approaches for both simplistic and complex therapeutic activities in the clinic. An initial optimization of the real-time capture of smart-glove measurements will initiate our longer term research goal of implementing machine learning models for user’s hand performance evaluation. We believe our prototype has the potential to lead to a new device that could assist therapists, patients, and families by enabling an adjunctive route for monitoring and evaluating stroke rehabilitation and recovery of the post-stroke hand when used in home-based environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".