End-user and clinician perspectives on the viability of wearable functional electrical stimulation garments after stroke and spinal cord injury
Bibliographic record
Abstract
PURPOSE: Functional electrical stimulation (FES), through repetitive training (FES-therapy) or continuous assistance (neuro-prosthesis), can restore motor function after paralysis due to spinal cord injury or stroke. With current technology, patients are often incapable of independently applying FES, thereby limiting its use. Novel FES-garments with embedded stimulation electrodes were developed in collaboration with Myant, Canada, to address this problem. The purpose of this study was to collect the views of future end-users to inform the refinement of the device design and to obtain insights on subsequent commercialization of this rehabilitation and assistive technology. METHODS: = 19). Participant took part in interviews or focus groups after a presentation of the garments. An inductive content analysis was used to generate the themes from the data and identify data saturation. RESULTS: The identified themes and sub-themes were: (1) User Perspectives: users' characteristics (needs, limitations), expected benefits (beliefs), and anticipated problems (fears); (2) Device Design: technical features, usability, and disadvantages of the garment, cables, stimulator, software, and interface; (3) Acquisition Process: organizational procedures (acquisition and adoption steps); and (4) Business Model: financial and strategic aspects to facilitate commercialization and support users. CONCLUSIONS: The insights obtained from end-users and clinicians provide guidelines to optimize the development of novel FES-garments, and strategies for bringing the device to the market. The themes identified can serve to inform other rehabilitation and assistive technology developers with processes and ideas on how to meet these groups' needs.IMPLICATIONS FOR REHABILITATIONParticipants with neurological paralysis have interest and critical views on new rehabilitation and assistive technology, and the repercussions of using new technologies to address their function, health and wellbeing.The FES-garment design presented appeared acceptable to the end-users, pending resolution of certain shortcomings (wiring, operating duration, robustness, easiness to don and doff).End-users and clinicians had specific views regarding the acquisition process of new technologies (training, customization, and follow-up/support), which are important to take into consideration to ensure broad stakeholders uptake.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".