The NuroSleeve, a user-centered 3D printed hybrid orthosis for individuals with upper extremity impairment
Bibliographic record
Abstract
BACKGROUND: Active upper extremity (UE) assistive devices have the potential to restore independent functional movement in individuals with UE impairment due to neuromuscular diseases or injury-induced chronic weakness. Academically fabricated UE assistive devices are not usually optimized for activities of daily living (ADLs), whereas commercially available alternatives tend to lack flexibility in control and activation methods. Both options are typically difficult to don and doff and may be uncomfortable for extensive daily use due to their lack of personalization. To overcome these limitations, we have designed, developed, and clinically evaluated the NuroSleeve, an innovative user-centered UE hybrid orthosis. METHODS: This study introduces the design, implementation, and clinical evaluation of the NuroSleeve, a user-centered hybrid device that incorporates a lightweight, easy to don and doff 3D-printed motorized UE orthosis and a functional electrical stimulation (FES) component. Our primary goals are to develop a customized hybrid device that individuals with UE neuromuscular impairment can use to perform ADLs and to evaluate the benefits of incorporating the device into occupational therapy sessions. The trial is designed as a prospective, open-label, single-cohort feasibility study of eight-week sessions combined with at-home use of the device and implements an iterative device design process where feedback from participants and therapists informs design improvement cycles. RESULTS: All participants learned how to independently don, doff, and use the NuroSleeve in ADLs, both in clinical therapy and in their home environments. All participants showed improvements in their Canadian Occupational Performance Measure (COPM), which was the primary clinical trial outcome measure. Furthermore, participants and therapists provided valuable feedback to guide further development. CONCLUSIONS: Our results from non-clinical testing and clinical evaluation demonstrate that the NuroSleeve has met feasibility and safety goals and effectively improved independent voluntary function during ADLs. The study's encouraging preliminary findings indicate that the NuroSleeve has met its technical and clinical objectives while improving upon the limitations of the existing UE orthoses owing to its personalized and flexible approach to hardware and firmware design. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04798378, https://clinicaltrials.gov/ct2/show/NCT04798378 , date of registration: March 15, 2021.
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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 | 0.000 | 0.000 |
| 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".