Motor Imagery Training for Gait Rehabilitation of People With Post-Stroke Hemiparesis: Practical Applications and Protocols
Bibliographic record
Abstract
Over the last two decades, the use of motor imagery (MI) for post-stroke rehabilitation has significantly increased. Previous findings support the feasibility of the incorporation of specific MI exercises to improve walking skills in individuals with post-stroke hemiparesis. However, detailed practical applications and specific protocols for the implementation of MI are scarce. The objective of this manuscript is to propose practical applications for a structured MI regimen, including detailed protocols of a six-week intervention targeting gait improvement following stroke. The proposed regimen is based on previous experience with MI rehabilitation programs for gait improvement following stroke, motor learning principles with applications for stroke rehabilitation, and the PETTLEP model. The proposed detailed protocols were found to be adjusted for gait improvement of post-stroke survivors as described in several studies, and may address the targets of different rehabilitation programs. Based on motor learning principles and guidelines, an example of verbal instructions for each treatment session during six weeks of intervention is proposed. The potential of this training program to augment and extend the rehabilitation process was proven in several studies. The variety of possibilities of scenes to image allows the clinician to target specific impaired performance and disabilities. By using the proposed structure and protocols, a large number of therapists may be able to address these targets.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Proposed clinical protocol for motor imagery training after stroke; a rehabilitation practice guide.
The article proposes clinical motor-imagery rehabilitation protocols for post-stroke gait.
Motor-imagery protocols for post-stroke gait rehab is clinical rehabilitation practice, not research methods study.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".