Biomechanical Methods to Quantify Muscle Effort During Resistance Exercise
Bibliographic record
Abstract
Chiu, LZF. Biomechanical methods to quantify muscle effort during resistance exercise. J Strength Cond Res 32(2): 502-513, 2018-Muscle hypertrophy and strength adaptations elicited by resistance training are dependent on the force exerted by active muscles. As an exercise may use many muscles, determining force for individual muscles or muscle groupings is important to understand the relation between an exercise and these adaptations. Muscle effort-the amount of force or a surrogate measure related to the amount of force exerted during a task-can be quantified using biomechanical methods. The purpose of this review was to summarize the biomechanical methods used to estimate muscle effort in movements, particularly resistance training exercises. These approaches include the following: (a) inverse dynamics with rigid body models, (b) forward dynamics and EMG-driven models, (c) normalized EMG, and (d) inverse dynamics with point-mass models. Rigid body models quantify muscle effort as net joint moments. Forward dynamics and EMG-driven models estimate muscle force as well as determine the effect of a muscle's action throughout the body. Nonlinear relations between EMG and muscle force and normalization reference action selection affect the usefulness of EMG as a measure of muscle effort. Point-mass models include kinetics calculated from barbell (or other implement) kinematics recorded using electromechanical transducers or measured using force platforms. Point-mass models only allow the net force exerted on the barbell or lifter-barbell system to be determined, so they cannot be used to estimate muscle effort. Data from studies using rigid body models, normalized EMG, and musculoskeletal modeling should be combined to develop hypotheses regarding muscle effort; these hypotheses should be verified by training interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".