Does Walking to a Beat Improve Prosthetic Gait? Investigating Immediate Effects of Rhythmic Auditory Stimulation-Based Biofeedback
Bibliographic record
Abstract
Gait training is important for lower-limb prosthesis users (LLPUs) to regain mobility, however, traditional approaches often present challenges relating to time, cost, and accessibility. To overcome this, our team has been exploring wearable technology for out-of-clinic gait training. One promising biofeedback gait training approach, providing real-time feedback based on specific gait parameters, could be based on rhythmic auditory stimulation (RAS). Studies on the interaction between RAS and gait training have primarily used open-loop RAS without considering the user’s performance. Yet optimal gait training involves progressive, tailored practice and adaptable for goal-directed activities, suggesting the potential benefits of closed-loop RAS-based systems for modulating temporal symmetry and enhancing functional mobility outcomes. The aims of this study were threefold: to (1) examine whether, for LLPUs, RAS can improve the temporal symmetry and other spatiotemporal parameters (speed, cadence, etc.) (2) compare open- and closed-loop RAS strategies, and (3) evaluate symmetry usability and workload. RAS led to significant improvements in temporal symmetry outperforming open loop (5.5 ± 2.4% vs 3.3 ± 2.2%, respectively), thus suggesting the potential benefits of adaptive feedback training. Across both strategies, participants were able to maintain the set cadence and speed during biofeedback trials, and both strategies showed similar results in terms of workload, and overall usability was positively-rated. Future research should focus on adaptable systems tailored to individual needs and long-term assessments across more diverse gait symmetries. Finally, exploring progressive and adaptive gait training, and incorporating variations in step or stride length could optimize gait training protocols for enhanced outcomes.
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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 | 0.002 |
| 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) | 0.002 | 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".