Defining the design requirements for an assistive powered hand exoskeleton
Bibliographic record
Abstract
The goal of this study was to identify design criteria for the development of an assistive powered hand exoskeleton by consulting with potential end users. Structured interviews with clinicians and patients with hand impairment were carried out and the results were tabulated. Three participants with impaired hand function also underwent a quantitative measurement session regarding hand function. The objective of the measurement sessions was to understand the characteristics, abilities and limitations of the upper limb of individuals who could benefit from a hand exoskeleton device, in order to better define design criteria and control options for such a device. For the most part, clinicians and participants with hand impairment agreed on expectations for a hand exoskeleton device on topics including important grasp patterns, wear time, and grip strength. However, their expectation seemed to diverge on the topic of control, where clinicians felt simple reliable control strategies would be preferred, but patients desired intuitive control. This research has identified key features of hand exoskeleton design requirements that will need to be met in order to have acceptable clinical translation to patient populations. Including end-users in the design of such a device is essential for successful patient-oriented technology development.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".