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Record W2347174070 · doi:10.18192/riss-ijhs.v2i1.1527

SmartHand: A Sense of Assistive Devices

2011· article· en· W2347174070 on OpenAlexaffvenue
Philip Lee

Bibliographic record

VenueRevue interdisciplinaire des sciences de la santé - Interdisciplinary Journal of Health Sciences · 2011
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAssistive technologyComputer scienceAssistive deviceCompetence (human resources)ReinnervationInternet privacyArtificial limbsHuman–computer interactionRisk analysis (engineering)PsychologyMedicinePhysical medicine and rehabilitationArtificial intelligenceNeuroscienceProsthesisSocial psychology

Abstract

fetched live from OpenAlex

Amputees have often faced social stigma attributable to their visible limb loss or use of artificial substitutions. In recent years, the use of prosthetics has become much more advanced, particularly in the field of sensory prostheses. One such assistive device, a new prototype technology known as the SmartHand, has recently been featured on several news networks. It is through this discussion that the SmartHand will be reviewed and compared with existing platforms that include myoelectric prostheses and targeted muscle reinnervation. Use of the SmartHand has been noted as having increased levels of competence in and improved the quality of life of its users. These conclusions also bring to light the scientific barriers that are faced, primarily with respect to tissue rejection. The cost benefit analysis of such a device may also produce conflicting data, thereby making it difficult to implement this device on a larger scale. Despite these problems, the SmartHand represents one of the most scientifically advanced assistive devices available in today’s market, whose usefulness for amputees is undeniable.

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)0.002
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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.055
GPT teacher head0.369
Teacher spread0.315 · 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

Citations0
Published2011
Admission routes2
Has abstractyes

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Same venueRevue interdisciplinaire des sciences de la santé - Interdisciplinary Journal of Health SciencesSame topicMuscle activation and electromyography studiesFrench-language works237,207