Neuronal coordination of arm and leg movements during human locomotion
Bibliographic record
Abstract
We aimed to study the neuronal coordination of lower and upper limb muscles. We therefore evaluated the effect of small leg displacements during gait on leg and arm muscle electromyographic (EMG) activity in walking humans. During walking on a split-belt treadmill (velocity 3.5 km/h), short accelerations or decelerations were randomly applied to the right belt during the mid or end stance phase. Alternatively, trains of electrical stimuli were delivered to the right distal tibial nerve. The EMG activity of the tibialis anterior (TA), gastrocnemius medialis (GM), deltoideus (Delt), triceps (Tric) and biceps brachii (Bic) of both sides was analysed. For comparison, impulses were also applied during standing and sitting. The displacements were followed by specific patterns of right leg and bilateral arm muscle EMG responses. Most arm muscle responses appeared with a short latency (65-80 ms) and were larger in Delt and Tric than in Bic. They were strongest when deceleration impulses were released during mid stance, associated with a right compensatory TA response. A similar response pattern in arm muscles was obtained following tibial nerve stimulation. The arm muscle responses were small or absent when stimuli were applied during standing or sitting. The arm muscle responses correlated more closely with the compensatory TA than with the compensatory GM responses. The amplitude of the responses in most arm muscles correlated closely with the background EMG activity of the respective arm muscle. The observations suggest the existence of a task-dependent, flexible neuronal coupling between lower and upper limb muscles. The stronger impact of leg flexors in this interlimb coordination indicates that the neuronal control of leg flexor and extensor muscles is differentially interconnected during locomotion. The results are compatible with the assumption that the proximal arm muscle responses are associated with the swinging of the arms during gait, as a residual function of quadrupedal locomotion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".