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Record W4407266936 · doi:10.1080/17483107.2025.2460752

Design priorities for an at-home upper limb stroke rehabilitation robot

2025· article· en· W4407266936 on OpenAlexafffund
Shane Forbrigger, T. Claire Davies, Vincent DePaul, Evelyn Morin, Keyvan Hashtrudi-Zaad

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2025
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRehabilitationPhysical medicine and rehabilitationStroke (engine)RobotMedicinePhysical therapyEngineeringComputer scienceArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

OBJECTIVE: The design of at-home stroke rehabilitation robots must be closely linked to the needs of users, especially stroke survivors and therapists, to ensure that such designs are effective in the home environment, which is less controlled than clinical environments. Translating user needs into the technical descriptors of a design is essential to this design process. This paper analyses user needs identified from interviews with stroke survivors and therapists in previous work. METHODS: The relationship between user needs and the broad technical properties of rehabilitation robot design are related using the House of Quality, an approach from Quality Function Deployment. Technical benchmarks are identified from previous rehabilitation robot designs and technical priorities are determined from the House of Quality. An at-home upper limb stroke rehabilitation robot concept for supporting therapy activities in a vertical planar workspace is described and evaluated using the identified technical priorities. IMPACT: The proposed design, a constrained cable robot, is determined to be appropriate for the desired application based on the technical priorities from the House of Quality.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.017
GPT teacher head0.311
Teacher spread0.295 · 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 designObservational
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

Citations0
Published2025
Admission routes2
Has abstractyes

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