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Record W4398219911 · doi:10.11648/j.ae.20240801.15

Development of a New and Mechanically Intelligent Anti-Tremor Utensil

2024· article· en· W4398219911 on OpenAlexafffund
Michaël Dubé, Thierry Laliberté, Véronique H. Flamand, Alexandre Campeau‐Lecours

Bibliographic record

VenueApplied Engineering · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsCentre for Interdisciplinary Research in RehabilitationUniversité Laval
FundersUniversité Laval
KeywordsMedicineComputer sciencePhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

People living with Parkinson’s disease or with essential tremors face many obstacles in their everyday lives. Being able to eat independently is one of them. Many technologies already exist to help people who have difficulty eating independently. However, following a review of existing devices with a team of occupational therapists, it was found that many commercially available solutions were either unhelpful or too expensive. The need for better adapted solutions was obvious so an iterative design methodology based on the user's needs was followed to create a new anti-tremor utensil. The starting point of the design was to analyze the existing utensils to understand better the pros and cons of the available solutions. During the iterative design methodology, several prototypes emerged and led to the creation of the final spoon prototype presented in this paper. A total of 5 different adaptative spoons were designed and are presented in this paper. A sensor-based frequential analysis combined with an occupational therapist review indicates that the proposed prototype is effective against certain types of tremors and that it could potentially help people living with tremor while they eat. The next step of the development will be to test the new prototype with potential users.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.559

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 head0.034
GPT teacher head0.241
Teacher spread0.206 · 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.

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

Citations0
Published2024
Admission routes2
Has abstractyes

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