Camera-Based Mirror Visual Feedback: Potential to Improve Motor Preparation in Stroke Patients
Bibliographic record
Abstract
Mirror visual feedback (MVF) is used widely for motor recovery after stroke, but an optimal training setup and systematic procedure are lacking. New optimization strategies have been proposed, one of which is a camera technique. We investigated the effects of a camera-based MVF setup on motor function and motor processes upstream for upper-limb rehabilitation. Seventy-nine stroke patients were assigned randomly to the MVF group (MG; ${N} = {38}$ ) or conventional group (CG; ${N} = {41}$ ), which, respectively, received camera-based MVF and dosage-equivalent physiotherapy or/and occupational therapy for 1 h/day and five days/week for four weeks. Two clinical scales were used to quantify the effect of the intervention methods: the Fugl-Meyer assessment-upper limb (FMA-UL) subscale and Barthel index. The hand laterality task was used to evaluate the ability of mental rotation, including the reaction time and accuracy. All measurements were improved significantly for both groups following intervention. FMA-UL was improved significantly in the MG compared with that in the CG. In lateralization tasks, the RT of the MG was significantly shorter than that of the CG at the endpoint. For all patients, judgments for the affected side were significantly slower and less accurate than for the less-affected side. Subgroup analyses suggested greater benefits of motor function, the activities of daily life, and mental rotation were achieved in subacute patients after MVF. A trend toward greater improvements in motor function for patients with severe-moderate motor impairment and patients with right-hemisphere damage were also revealed. Camera-based MVF has improved the motor function and ability of mental rotation for stroke patients, especially for patients in the subacute stage, which indicates the potential to improve motor preparation. Further studies might combine mental rotation with electroencephalography to investigate the neuro-mechanism of MVF.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".