Usability study of a device for compensation of foot drop based on FES and surface multi-field electrodes in a clinical environment
Bibliographic record
Abstract
Functional Electrical Stimulation (FES) has shown successful assistive and rehabilitation effects in people affected by foot drop dysfunction caused by neurological disorders [1]. Still, FES-based foot drop devices are not present in regular therapy programs of many countries due to barriers caused by technological, usability or reliability issues [2]. FES devices based on surface multi-field electrodes bring new broader stimulation possibilities and features like compensation of eversion/inversion and thus, potentially increase the configuration options. In this study, the satisfaction and usability aspects of a multi-field based FES device were analyzed in a clinical environment. Method The FES device used in this study was the Fesia Walk device for compensation of foot drop, which is based on a multi-field surface electrode and an inertial sensor for gait phase detection. 10 acquired brain injury subjects in chronic stage and 4 therapists participated in the study. The therapists received a two-hour training session prior to the therapy sessions. Every subject was assigned to one therapist and received 3 sessions of habituation and 6 sessions of over ground walking with the Fesia Walk during three weeks. Both therapists and users were evaluated with the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST). Additionally, the therapists were evaluated with the System Usability Scale (SUS). An individual interview was carried out with each of the participants. Results The device received good scores in both the QUEST and SUS scales, with mean scores of 4.14 out of 5 and 85.6 out of 100 respectively. Furthermore, most users and all therapists showed interest to continue using the device after the study. Discussion and conclusions This usability study indicated that it is possible to include surface multi-field based FES devices for the compensation of foot drop in practical therapeutic programs and that they can be used as regular tools by therapists in clinical environments.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".