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Record W2028261745 · doi:10.1063/1.3147404

Neuromechanical considerations for incorporating rhythmic arm movement in the rehabilitation of walking

2009· article· en· W2028261745 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChaos An Interdisciplinary Journal of Nonlinear Science · 2009
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of Victoria
Fundersnot available
KeywordsKinematicsPhysical medicine and rehabilitationRhythmElectromyographyTrunkSwingTreadmillComputer scienceRehabilitationMovement (music)Task (project management)Functional movementLumbarBiomechanicsMedicinePhysical therapyEngineeringAnatomyPhysics

Abstract

fetched live from OpenAlex

We have extensively used arm cycling to study the neural control of rhythmic movements such as arm swing during walking. Recently rhythmic movement of the arms has also been shown to enhance and shape muscle activity in the legs. However, restricted information is available concerning the conditions necessary to maximally alter lumbar spinal cord excitability. Knowledge on the neuromechanics of a task can assist in the determination of the type, level, and timing of neural signals, yet arm swing during walking and arm cycling have not received a detailed neuromechanical comparison. The purpose of this research was to provide a combined neural and mechanical measurement approach that could be used to assist in the determination of the necessary and sufficient conditions for arm movement to assist in lower limb rehabilitation after stroke and spinal cord injury. Subjects performed three rhythmic arm movement tasks: (1) cycling (cycle); (2) swinging while standing (swing); and (3) swinging while treadmill walking (walk). We hypothesized that any difference in neural control between tasks (i.e., pattern of muscle activity) would reflect changes in the mechanical constraints unique to each task. Three-dimensional kinematics were collected simultaneously with force measurement at the hand and electromyography from the arms and trunk. All data were appropriately segmented to allow a comparison between and across conditions and were normalized and averaged to 100% movement cycle based on shoulder excursion. Separate mathematical principal components analysis of kinematic and neural variables was performed to determine common task features and muscle synergies. The results highlight important neural and mechanical features that distinguish differences between tasks. For example, there are considerable differences in the anatomical positions of the arms during each task, which relate to the moments experienced about the elbow and shoulder. Also, there are differences between tasks in elbow flexion/extension kinematics alongside differential muscle activation profiles. As well, mechanical assistance and constraints during all tasks could affect muscle recruitment and the functional role of muscles. Overall, despite neural and mechanical differences, the results are consistent with conserved common central motor control mechanisms operational for cycle, walk, and swing but appropriately sculpted to demands unique to each task. However, changing the mechanical parameters could affect the role of afferent feedback altering neural control and the coupling to the lower limbs.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.307
Teacher spread0.285 · 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