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Record W2103258837 · doi:10.1109/iembs.1998.744742

Devices for improved mobility after spinal cord injury and stroke

2002· article· en· W2103258837 on OpenAlexaff
R. B. Stein, Su Ling Chong, Kelvin B. James, Jianguo Cheng, Marguerite Wieler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWheelchairFunctional electrical stimulationPhysical medicine and rehabilitationFoot dropComputer scienceLift (data mining)Spinal cord injurySimulationEngineeringMedicineSpinal cordStimulation

Abstract

fetched live from OpenAlex

Technology can improve the ability of people with disability to move in a variety of ways. Two devices are described here. The first is a novel foot drop stimulator that enables people, who drop or drag their foot during the swing phase of the walking cycle, to lift the foot through the use of functional electrical stimulation (FES). This stimulator uses a built-in tilt sensor to decide when stimulation through electrodes over the common peroneal nerve should be activated. The second device is for people who are more severely disabled and use a wheelchair for transportation. FES can be used to flex and extend the leg about the knee joint of the subject. This motion is then coupled to the wheel of the chair and permits propulsion of the wheelchair with the legs as well as with the arms. This wheelchair should have a number of benefits that are currently being studied with prototypes and with computer modeling.

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)0.001
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 score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0160.003

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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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

Citations3
Published2002
Admission routes1
Has abstractyes

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