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Record W2114946454 · doi:10.3109/17483107.2010.528144

Usability assessment of ASIBOT: a portable robot to aid patients with spinal cord injury

2010· article· en· W2114946454 on OpenAlexaboutno aff
Alberto Jardón, Ángel Gil, Ana I. Peña, Concepción A. Monje, Carlos Balaguer

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2010
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityUSableHuman–computer interactionTask (project management)PopulationEngineeringComputer scienceMedicineMultimedia

Abstract

fetched live from OpenAlex

The usability concept refers to aspects related to the use of products that are closely linked to the user's degree of satisfaction. Our goal is to present a functional evaluation methodology for assessing the usability of sophisticated technical aids, such as a portable robot for helping disabled patients with severe spinal cord injuries. The specific manipulator used for this task is ASIBOT, a personal assistance robot totally developed by RoboticsLab at the University Carlos III of Madrid. Our purpose is also to improve some aspects of the manipulator according to the user's perception. For our case study, a population of six patients with spinal cord injury is considered. These patients have been suffering spinal cord injuries for a period of time longer than 1 year before the tests are carried out. The methodology followed for the information gathering is based on the Quebec User Evaluation of Satisfaction with assistive Technology (QUEST). Different daily functions, such as drinking, brushing one's teeth and washing one's face, are considered to assess the user's perception when using ASIBOT as a technical aid. The human factor in this procedure is the main base to establish the specific needs and tools to make the end product more suitable and usable.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.002
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 head0.023
GPT teacher head0.425
Teacher spread0.402 · 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

Citations28
Published2010
Admission routes1
Has abstractyes

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