Predictive Dynamic Simulation of Cycling Using Olympic Cyclist and Bicycle Models
Bibliographic record
Abstract
Predictive dynamic simulation is a useful tool for analyzing human movement and \noptimizing performance. Such simulations do not require experimental data collection \nand provide the opportunity to analyze a variety of potential scenarios. This presents \ninteresting possibilities for investigating the optimal technique in sports applications, such \nas cycling. Much of the previous research on modeling and simulation of cycling has focused \non seated pedaling and models the bicycle or ergometer with an e ective resistive torque \nand inertia. This study was focused on modeling standing starts, a component of certain \ntrack cycling events in which the cyclist starts from rest and attempts to accelerate to top \nspeed as quickly as possible. A useful model would need to incorporate bicycle dynamics, \nincluding tire models, and complete cyclist dynamics, including the upper body. \n \nA ten degree-of-freedom, two-legged cyclist and bicycle model was developed using \nMapleSim and utilized for predictive simulations of standing starts. A joint torque model \nwas incorporated to represent musculoskeletal dynamics, including scaling based on joint \nangle and angular velocity to represent the muscle force-length and force-velocity relationships. \nTire slip for the bicycle model was represented by the Pacejka tire model for \nwheel-ground contact. GPOPS-II, a direct collocation optimal control software, was used \nto solve the optimal control problem for the predictive simulation. \n \nFirst, a modi ed version of this model was used to simulate ergometer pedaling. The \nmodel was validated by comparing simulated ergometer pedaling against ergometer pedaling \nperformed by seven Olympic-level track cyclists from the Canadian team. A kinematic \ndata tracking approach was used to assess the abilities of the model to match experimental \ndata. Following the successful matching of experimental data, a purely predictive \nsimulation was performed for seated maximal start-up ergometer pedaling with an objective \nfunction of maximizing the crank progress. These simulations produce joint angles, \ncrank torque, and power similar to experimental results, indicating that the model was a \nreasonable representation of an Olympic cyclist. \n \nSubsequently, experimental data were collected for a single member of the Canadian \nteam performing standing starts on the track. Data collected included crank torque, cadence, \nand joint kinematics. Predictive simulations of standing starts were performed using \nthe combined cyclist and bicycle model. Key aspects of the standing start technique, including \nthe drive and reset, were captured in the predictive simulations. The results show \nthat optimal control can be used for predictive simulation with a combined cyclist and \nbicycle model. Future work to improve upon the current model is discussed.
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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 | 0.000 | 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) | 0.000 | 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".