EMG Signals Can Reveal Information Sharing between Consecutive Pedal Cycles
Bibliographic record
Abstract
PURPOSE: Producing a steady cadence and power while cycling results in fairly consistent average pedal forces for every revolution, although small fluctuations about an average force do occur. This force can be generated by several combinations of muscles, each with slight fluctuations in excitation for every pedal cycle. Fluctuations such as these are commonly thought of as random variation about average values. However, research into fluctuations of stride length and stride time during walking shows information can be contained in the order of fluctuations. This order, or structure, is thought to reveal underlying motor control strategies. Previously, we found persistent structure in the fluctuations of EMG signals during cycling using entropic half-life analysis. These EMG signals contained fluctuations across multiple timescales, such as those within a burst of excitation, between the burst and quiescent period of a cycle, and across multiple cycles. It was not clear which sources of variation contributed to the persistent structure in the EMG. METHODS: In this study, we manipulated variation at different timescales in EMG intensity signals to identify the sources of structure observed during cycling. Nine participants cycled at a constant power and cadence for 30 min while EMG was collected from six muscles of the leg. RESULTS: We found persistent structure across multiple pedal cycles of average EMG intensities, as well as average pedal forces and durations. In addition, we found the entropic half-life did not quantify fluctuations within a burst of EMG intensity; instead, it detected unstructured variation between the burst and quiescent period within a cycle. CONCLUSIONS: The persistent structure in average EMG intensities suggests that fluctuations in muscle excitation are regulated from cycle to cycle.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".