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Record W2540047426 · doi:10.1109/ie.2016.52

Validation of a Smart Stove for Traumatic Brain Injury Patients in a Cooking Task

2016· article· en· W2540047426 on OpenAlexaff
Ame Lie Boudreault, Julie Bouchard, Bruno Bouchard, Shawn Whittom-Ross

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsStoveTask (project management)Traumatic brain injuryReliability (semiconductor)PsychologyActivities of daily livingPhysical medicine and rehabilitationMedicineApplied psychologyPsychiatryEngineering

Abstract

fetched live from OpenAlex

Each year, thousands of individuals sustain a traumatic brain injury leaving them diminished in various spheres. The impacts of such a condition are multifaceted and may change the lifestyle of victims and their families, who often take on the role of natural caregivers. Victims are often young adults who wish to maintain a lifestyle similar to the one they had prior to their accident. Though certain cases imply that resuming their activities of daily living is not a possibility, others will be able to live a fairly normal life with the help of natural caregivers or assistive technologies. This paper presents a study that was performed in our laboratory with TBI victims to assess the reliability of a smart stove designed for cognitively-impaired individuals in order to determine if it can replace the assistance provided by a caregiver.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.038
GPT teacher head0.282
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations5
Published2016
Admission routes1
Has abstractyes

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