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Record W2982277059 · doi:10.29173/aar89

Upregulation of spinal stretch reflexes during upper-limb posture control task

2019· article· en· W2982277059 on OpenAlexaffvenue
Ryan Raymond Miller, Tyler Cluff

Bibliographic record

VenueAlberta Academic Review · 2019
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSensory systemPhysical medicine and rehabilitationProprioceptionBalance (ability)ReflexVestibular systemUpper limbTask (project management)ElbowMotor controlElectromyographyPsychologyMedicineNeuroscienceAnatomyEngineering

Abstract

fetched live from OpenAlex

Background: Sensory feedback from receptors in the eyes, skin, vestibular organs and muscles allows us to build accurate representations of the position and motion of our body within the environment. In unpredictable situations, such as when holding an umbrella in gusting winds, studies have suggested the nervous system upregulates the sensitivity of sensory organs to counter disturbances and increase the probability of success. To date, studies have focused exclusively on the upregulation of feedback mechanisms in the lower-limbs during standing balance. We know comparatively little about whether and how sensory upregulation contributes to the control of upper limb motor actions. Objectives: Examine the upregulation and adaptation of upper limb muscle activity and spinal stretch reflexes when interacting with unpredictable mechanical environments. Methods: Ten healthy, right-handed adults (age range: 20 – 27 years) performed a postural control task where the goal was to maintain their hand within a fixed target. Participants performed the task while seated with their arm supported in an exoskeleton robot that can sense and disturb arm motion. They received real-time feedback of their movements on a virtual reality system. The protocol was delivered in three phases. The baseline phase consisted of 50 trials where subjects maintained their hand in the target in the absence of mechanical disturbances. Subjects then performed a peri-exposure phase that consisted of 100 null trials (no forces applied), 100 step-torque perturbations that produced rapid elbow flexion (+2Nm), and 100 perturbations that caused rapid elbow extension (-2Nm). We then unexpectedly removed the perturbations and subjects performed 75 trials to determine whether muscle activity returned to baseline levels. Kinematics and muscle activity were recorded throughout the experiment. Results: Preliminary results show that background muscle activity and spinal stretch reflexes were the largest when first exposed to unpredictable mechanical perturbations and adapted systematically with repeated exposure. Conclusions: Similar to results observed in the lower-limbs during standing balance experiments, we observed upregulation of background muscle activity and spinal stretch reflexes when interacting with unpredictable mechanical environments with the upper-limb. The amplitude of spinal stretch responses and background muscle activity decayed systematically with repeated exposure to unpredictable mechanical perturbations.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.008
GPT teacher head0.306
Teacher spread0.299 · 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 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

Citations1
Published2019
Admission routes2
Has abstractyes

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