The use of augmented auditory feedback to improve arm reaching in stroke: a case series
Bibliographic record
Abstract
PURPOSE: After practice, augmented feedback is the most important factor that facilitates motor learning. We assess the potential effectiveness of two types of augmented auditory feedback on the re-learning of arm reaching in individuals with stroke: (a) real-time knowledge of performance (KP) feedback and (b) rhythmic cueing in the form of knowledge of results (KR) feedback. METHOD: Five participants with stroke underwent short-term practice, reaching with their affected arm with KP, KR and no feedback, on separate days. We assessed range of motion of the upper extremity (shoulder, elbow) and trunk, mean error and variability of the performed trajectory, and movement time, before and after training. RESULTS: All participants benefitted from practice with feedback, though the effects varied across participants and feedback type. In three participants, KP feedback increased elbow extension and reduced compensatory trunk flexion. In four participants, KR feedback reduced movement time taken to perform the reach. Of note, one participant benefitted mostly from KP feedback, which increased shoulder flexion and elbow extension, and decreased compensatory trunk flexion and mean error. CONCLUSIONS: Within day practice with augmented auditory feedback improves reaching in individuals with stroke. This warrants further investigation with longer practice periods in a larger sample size. IMPLICATIONS FOR REHABILITATION: After practice, augmented feedback is the second most important factor that facilitates motor learning. Music-based augmented auditory feedback has potential to enhance reaching abilities in individuals with stroke. Future studies are warranted to evaluate the long-term effectiveness of this feedback over a longer training period in a larger sample size.
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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 | 0.001 | 0.008 |
| 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 | 0.000 |
| 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".