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Record W2903679110 · doi:10.1101/492124

Defining the design requirements for an assistive powered hand exoskeleton

2018· preprint· en· W2903679110 on OpenAlexafffund
Quinn A. Boser, Michael R. Dawson, Jonathon S. Schofield, Gwen Dziwenko, Jacqueline S. Hebert

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of Alberta
FundersGlenrose Rehabilitation HospitalTD Bank
KeywordsExoskeletonGRASPPhysical medicine and rehabilitationSession (web analytics)Control (management)Computer scienceFunction (biology)Human–computer interactionPhysical therapyMedicinePsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.712
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.247
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2018
Admission routes2
Has abstractyes

Explore more

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