Bibliographic record
Abstract
The goal of this study was to apply recurrence quantification analysis (RQA) to surface electromyographic (sEMG) signals during motor learning and training activities. It has been previously demonstrated that the RQA variable, percentage of determinism (�T), is related to the synchronization of motor units. It is suggested that �T will change throughout the motor learning and training process. As a result, the experiment consisted of two separate parts. The motor learning part required a male subject to train using the Nintendo Wii Fit® software, Wii Fit® balance board and the Nintendo Wii® gaming console. The myoelectric signals were acquired from the peroneus longus (PL) and soleus (S) muscles. During the course of this experiment a soccer simulator and three in-game balance tests were used to evaluate motor learning. The second part of the experiment consisted of a chronic incomplete spinal cord injured patient from the Toronto Rehabilitation Institute. The subject trained three times a week for fourteen days. Each training session consisted of the subject performing weighted dorsaf and plantar flexion. Both parts of the experiments suggests that there is a decrease in synchronization of motor units after motor learning and training (decrease in �T). Additionally, the time course of �T displayed a con-vergence of levels between the right PL and right S during the virtual environment training. It is concluded that RQA demonstrates the ability to detect motor learning and training. Possible applications for the use of RQA on sEMG signal could be the evaluation of rehabilitation programs. By monitoring the �T, it may be possible to determine if a particular rehabilitation program is effective for a patient. This could lead to customizable programs, suited for a specific person, in order to increase the rate of recovery.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".