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Record W3152433297

Developing and Validating a Functional Electrical Stimulation Controller for Use with Visual Feedback Training for Standing Balance Therapy in Individuals with Incomplete Spinal Cord Injury

2019· dissertation· en· W3152433297 on OpenAlexfundno aff
Emerson Paul Grabke

Bibliographic record

VenueTSpace · 2019
Typedissertation
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsFunctional electrical stimulationPhysical medicine and rehabilitationBalance (ability)Spinal cord injuryVisual feedbackPhysical therapyRehabilitationMedicineStimulationPsychologySpinal cordComputer scienceNeuroscienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Incomplete spinal cord injury (iSCI) can impair standing balance, increasing fall likelihood. Visual feedback training (VFT) and functional electrical stimulation (FES) have shown promise in iSCI standing balance rehabilitation. Combining VFT and lower-limb FES was hypothesized to yield more effective standing balance therapy. This thesis developed a VFT-complementary FES system for regulating ankle muscles. In my previous work, I have developed a VFT system with another student. For my thesis project, I (1) designed an FES controller, (2) optimized the controller to mimic able-bodied ankle muscle activation data during VFT sessions, and (3) tested the resultant FES controller on able-bodied individuals as they underwent VFT to determine the effects of the FES controller on able-bodied postural control. As expected, the resultant FES-VFT system did not deteriorate able-bodied postural control. The results of this research contribute toward the development of a more effective standing balance rehabilitation system for individuals with iSCI.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.162
GPT teacher head0.462
Teacher spread0.300 · 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.

Study designObservational
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
Published2019
Admission routes1
Has abstractyes

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