Design and Evaluation of a Soft Robotic Hand Orthosis with People with Severe Hand Impairment after Stroke
Bibliographic record
Abstract
Fifteen million individuals worldwide experience a stroke each year with 50,000 of these cases occurring in Canada. Approximately one-third never fully recover the hand function required to perform activities of daily living independently. The goal of this thesis was to design a usable and accessible robot that provides the necessary assistive forces to move the affected hand after stroke into functional extension and grasp postures. The robot was iteratively designed with occupational therapists and people after stroke to create and update the design specifications and mechanical, electrical and software design choices. Successive design and evaluation cycles of the Hand Extension Robot Orthosis (HERO) are discussed. The successive design iterations were evaluated by a total of 30 participants with severe hand impairment after stroke. The iterations were increasingly effective in assisting flaccid and clenched finger extension, range of motion and grip force. With the final iteration, My-HERO, established criteria for clinically meaningful important difference thresholds were surpassed by all participants for the Fugl-Meyer Assessment-Hand and the majority of participants for the Chedoke Arm and Hand Activity Inventory-13. The majority of participants were satisfied with My-HERO and desired to use it in the clinic and at home for rehabilitation and assistance during their therapy and daily routines. This work presents novel robotic hand orthoses and novel methods for controlling them. This work shows how well robotic hand orthoses extend flaccid and clenched fingers, increase range of motion and grip strength, and enhance hand function and performance on daily living tasks. Therapists and people after stroke should use this information when planning how to incorporate these devices into therapy and daily routines. This work shows it is feasible to use a user-centred design process to develop usable adaptive and rehabilitation technology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".