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Record W2408500447

Telepresence Robot for Home Care Assistance.

2007· article· en· W2408500447 on OpenAlexaff
François Michaud, Patrick Boissy, Daniel Labonté, Hélène Corriveau, Andrew Grant, Michel Lauria, Richard Cloutier, M.-A. Roux, Daniel Iannuzzi, M.-P. Royer

Bibliographic record

VenueNational Conference on Artificial Intelligence · 2007
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTeleoperationTeleroboticsTelehealthRobotTelemedicineVariety (cybernetics)Mobile robotComputer scienceHuman–computer interactionAging in placeVideoconferencingMultimediaHealth careMedicineArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Teleoperated from a distant location, a mobile robot with some autonomous capabilities can become a beneficial tool in telehealth applications. Assistive technologies for telementoring in homes constitute a very promising avenue to decrease load on the health care system, reduce hospitalization period and improve quality of life. However, design issues related to such systems are broad and mostly unexplored, but with very few systems currently available commercially. Mobile robots operating in home environments must deal with constrained space and a great variety of obstacles and situations to handle. This paper presents the interdisciplinary design methodology followed to develop Telerobot, a telepresence assistive mobile robot for home care assistance of elderly people. Using field trials with existing platforms, focus groups and interviews, initial requirements for the new mobile robot platform with its augmented video user interfaces are outlined.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0120.002

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 head0.224
GPT teacher head0.440
Teacher spread0.216 · 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 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

Citations73
Published2007
Admission routes1
Has abstractyes

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