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Video game-based neuromuscular electrical stimulation system for calf muscle training: A case study

2010· article· en· W1986666832 on OpenAlexaff
Dimitry G. Sayenko, Kei Masani, Matija Milosevic, Mark F. Robinson, Albert H. Vette, Kristiina M. Valter McConville, Miloš R. Popović

Bibliographic record

VenueMedical Engineering & Physics · 2010
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsToronto Metropolitan UniversityUniversity of TorontoToronto Rehabilitation Institute
Fundersnot available
KeywordsAnklePhysical medicine and rehabilitationIsometric exerciseTraining systemVideo gameElectrical muscle stimulationRehabilitationStrength trainingRange of motionIsotonicPhysical therapyConcentricMedicineTraining (meteorology)Resistance trainingComputer scienceStimulationMultimediaAnatomyMathematics

Abstract

fetched live from OpenAlex

A video game-based training system was designed to integrate neuromuscular electrical stimulation (NMES) and visual feedback as a means to improve strength and endurance of the lower leg muscles, and to increase the range of motion (ROM) of the ankle joints. The system allowed the participants to perform isotonic concentric and isometric contractions in both the plantarflexors and dorsiflexors using NMES. In the proposed system, the contractions were performed against exterior resistance, and the angle of the ankle joints was used as the control input to the video game. To test the practicality of the proposed system, an individual with chronic complete spinal cord injury (SCI) participated in the study. The system provided a progressive overload for the trained muscles, which is a prerequisite for successful muscle training. The participant indicated that he enjoyed the video game-based training and that he would like to continue the treatment. The results show that the training resulted in a significant improvement of the strength and endurance of the paralyzed lower leg muscles, and in an increased ROM of the ankle joints. Video game-based training programs might be effective in motivating participants to train more frequently and adhere to otherwise tedious training protocols. It is expected that such training will not only improve the properties of their muscles but also decrease the severity and frequency of secondary complications that result from SCI.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.342
Teacher spread0.302 · 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 designSimulation or modeling
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

Citations15
Published2010
Admission routes1
Has abstractyes

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