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Record W1752035993 · doi:10.3233/nre-2011-0655

The future of intelligent assistive technologies for cognition: Devices under development to support independent living and aging-with-choice

2011· article· en· W1752035993 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeurorehabilitation · 2011
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsCognitionContext (archaeology)Independent livingProcess (computing)Assistive technologyHuman–computer interactionComputer sciencePsychologyGerontologyMedicineNeuroscience

Abstract

fetched live from OpenAlex

A person's ability to be independent is dependent on his or her overall health, mobility, and ability to complete activities of daily living. Intelligent assistive technologies (IATs) are devices that incorporate context into their decision-making process, which enables them to provide customised and dynamic assistance in an appropriate manner. IATs have tremendous potential to support people with cognitive impairments as they can be used to support many facets of well-being; from augmenting memory and decision making tasks to providing autonomous and early detection of possible changes in health. This paper presents IATs that are currently in development in the research community to support tasks that can be impacted by compromised cognition. While they are not yet ready for the general public, these devices showcase the capabilities of technologies one can expect to see in the consumer marketplace in the near future.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.040
GPT teacher head0.276
Teacher spread0.236 · 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