Predictive Dynamic Simulation of Cycling Using Olympic Cyclist and Bicycle Models
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».