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Record W4407586307 · doi:10.3390/act14020092

A Dual-Motor Actuator for Ceiling Lift with High-Force and High-Speed Capabilities

2025· article· en· W4407586307 on OpenAlexafffund
Ian Lalonde, Jeff Denis, Mathieu Lamy, Alexandre Girard

Bibliographic record

VenueActuators · 2025
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversité de Sherbrooke
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsLift (data mining)ActuatorCeiling (cloud)Dual (grammatical number)Mechanical engineeringEngineeringAutomotive engineeringComputer scienceStructural engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Patient transfer devices allow for passive movement of patients in hospitals and care centers. Instead of hoisting the patient, it would be beneficial in some cases to assist their movement, enabling them to move by themselves and reducing hospitalization time. However, patient assistance requires devices capable of precisely controlling output forces at significantly higher speeds than those used for patient transfers alone, and a single-motor solution would be over-sized and would show poor efficiency for accomplishing both functions. This paper presents a ceiling robot, using a dual-motor actuator and adapted control schemes, that can be used to transfer patients, assist patients in their movement, and help prevent falls. The prototype is shown to be able to lift patients weighing up to 318 kg and to assist a patient with a desired force of up to 100 kg with a precision of 7.8%. Also, a smart control scheme to manage falls is shown to be able to stop a patient who is falling by applying a desired deceleration.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

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 communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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