Simultaneous Measurement of Breathing Kinematics and Surface Electromyography of Chest Wall Muscles during Maximum Performance and Speech Tasks in Children: Methodological Considerations
Bibliographic record
Abstract
OBJECTIVE: To develop a standardized paediatric protocol for acquiring simultaneous chest wall kinematics and surface electromyography (EMG) of chest wall muscles during maximum performance and speech tasks. PATIENTS AND METHODS: Eighteen healthy participants included: (a) a younger age group (n = 6; ages 4.0-6.5 years), (b) an older age group (n = 6; ages 7.0-10.5 years), and (c) an adult group (n = 8; ages 21-33 years). A child (age 10 years) with spastic-type cerebral palsy (CP) served as a 'proof of protocol feasibility'. Chest wall kinematics and surface EMGs (intercostals, rectus abdominus, external oblique, latissimus dorsi, and erector spinae) were acquired during maximum performance and speech tasks. RESULTS: Successful calibration of the EMG signal and reliable detection of muscle activation onset, offset, and amplitude relative to vital capacity and percent maximum voluntary contraction in children were demonstrated. Kinematic and surface EMG measurements were sensitive to non-speech and speech tasks, age, and neurological status (i.e. CP). CONCLUSION: The simultaneous measurement of kinematics and EMG of the chest wall muscle groups provides a more comprehensive description of speech breathing in children. This protocol can be used for the observation and interpretation of clinical outcomes seen in children with motor speech disorders following treatments that focus on increasing overall respiratory and vocal effort.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".