Evaluation of a Vibrotactile Biofeedback System Targeting Stance Time Symmetry Ratio of Individuals With Lower-Limb Amputation: A Pilot Study
Bibliographic record
Abstract
Individuals with lower-limb amputation (LLA) often exhibit atypical gait patterns and asymmetries. These patterns can be corrected using biofeedback (BFB). Real-time BFB strategies have demonstrated to be effective to various degrees in BFB systems. However, no studies have evaluated the use of corrective vibrotactile BFB strategies to improve temporal gait symmetry of LLA. The aim of this study was to evaluate a wearable vibrotactile BFB system to improve stance time symmetry ratio (STSR) of LLA, and compare two corrective BFB strategies that activate either one or two vibrating motors at two different frequency and amplitude levels, based on a pre-set STSR target. Gait patterns of five unilateral LLA were assessed with and without BFB. Spatiotemporal and kinematic gait parameters were measured and assessed using a wearable motion capture system. Usability and workload were assessed using the System Usability Scale and NASA Task Load Index questionnaires, respectively. Results showed that participants significantly ( [Formula: see text]) improved STSR with BFB; however, this coincided with a reduction in gait speed and cadence compared to walking without feedback. Knee and hip flexion angles improved and changes in other parameters were variable. Immediate post-test retention effects were observed, suggesting that gait changes due to BFB were preserved for at least a short-time after feedback was withdrawn. System usability was found to be acceptable while using BFB. The outcomes of this study provide new insights into the development and implementation of clinically practical and viable BFB system. Future work should focus on assessing the long-term use and retention effects of BFB outside controlled-laboratory conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".