Anterior cruciate ligament injuries are among the most common injuries worldwide, especially in sports and athletics. Current studies on anterior cruciate ligament injuries demonstrate that, in countries where it has been possible to extract data at a national level, such injuries have an annual incidence in the range of 29 to 38 per 100,000 people. The aim of this thesis is to obtain an estimate of the internal forces of muscles and ligaments that cannot be measured in vivo and in particular the stresses applied to the anterior cruciate ligament, to evaluate its overload in order to prevent possible injuries. Motion analysis is based on musculoskeletal modeling, which allows the collection and analysis of information on muscle activations and forces that are difficult or impossible to measure experimentally. In this project, are of particular interest data concerning both kinematics, which defines the position, speed and acceleration of the body, and the dynamics, which defines the forces and the moments that generate the movement. In order to obtain this data, a 6-degree-of-freedom musculoskeletal model of the knee is used, which implements in detail the muscles of the lower limb, as well as the cruciate ligaments and collateral ligaments. In this study previously collected data from 5 healthy subjects, acquired as controls, and 5 pathological subjects 6-8 months after anterior cruciate ligament reconstruction recorded while performing tasks such as walking and dropping are processed. Each subject was acquired via stereophotogrammetry, a surface electromyography system, and two force plates. The data of interest such as muscle forces, muscle activations, and anterior cruciate ligament stress forces were estimated through a musculoskeletal modeling software, i.e. Opensim by adopting two different methods whose validity was assessed by comparing simulated activations with experimentally measured ones. In the first method, the results are obtained by means of static optimization via a predefined conventional pipeline described below. The second method uses the Moco software toolkit combined with surface electromyography, and includes an innovative optimization method, based on direct collocation, for estimating the parameters of interest. Through these tools, it was possible to perform a quantitative evaluation of tensions and deformations related to the knee ligaments in order to highlight any differences between healthy and pathological subjects.
Le lesioni del legamento crociato anteriore sono tra gli infortuni più comuni a livello mondiale, in particolar modo in campo sportivo e atletico. Gli studi attuali sulle lesioni del legamento crociato anteriore dimostrano che, nei paesi dove è stato possibile estrarre dati a livello nazionale, tali lesioni hanno un’incidenza annuale in un range compreso tra 20 e 50 ogni 100.000 persone. L’obiettivo di questa tesi è di ottenere una stima delle forze interne di muscoli e legamenti che non possono essere misurate in vivo e in particolare le sollecitazioni applicate al legamento crociato anteriore e posteriore per valutare le eventuali condizioni di sovraccarico, in modo da prevenire possibili infortuni. L’analisi del movimento presenta tra le sue possibili applicazioni anche la modellazione muscoloscheletrica, la quale consente di raccogliere e analizzare informazioni su attivazioni di muscoli sia superficiali che profondi e forze muscolari difficili o impossibili da misurare sperimentalmente. In questo lavoro, sono di particolare interesse dati riguardanti sia cinematica, che definisce posizione, velocità e accelerazione del corpo, sia la dinamica del gesto motorio, cioè le forze e i momenti che generano il movimento. Al fine di ottenere tali dati, viene utilizzato un modello muscoloscheletrico del ginocchio a 6 gradi di libertà, il quale implementa in modo dettagliato i muscoli dell’arto inferiore, oltre che i legamenti del crociato e i legamenti collaterali. Sono stati elaborati i dati di 5 soggetti sani, precedentemente rilevati e utilizzati come soggetti di controllo, e 5 soggetti patologici tra 3-8 mesi dall’intervento di ricostruzione del legamento crociato anteriore acquisiti durante l’esecuzione di task motori quali cammino e drop landing. Per ogni soggetto si sono rilevati i dati tramite un sistema di stereofotogrammetria, un sistema di elettromiografia di superficie e due pedane di forza. Le forze muscolari, le attivazioni muscolari, e le forze di sollecitazione del legamento crociato anteriore sono state stimate tramite il software per la modellazione muscoloscheletrica Opensim, adottando due metodi. L’affidabilità di questi ultimi è stata stimata confrontando le attivazioni muscolari simulate rispetto a quelle registrate sperimentalmente. Nel primo metodo, i risultati sono ottenuti per mezzo dell’ottimizzazione statica nel software Opensim e tramite una pipeline convenzionale predefinita descritta in seguito. Nel secondo metodo si è utilizzato il toolkit software Moco combinato con l’elettromiografia di superficie, e comprende un metodo di ottimizzazione innovativo, basato sulla collocazione diretta, per la stima dei parametri di interesse. Successivamente, tramite tali strumenti è stato possibile eseguire una valutazione quantitativa di tensioni e deformazioni relative ai legamenti del ginocchio al fine di evidenziare eventuali differenze tra soggetti sani e soggetti patologici.
Modello muscoloscheletrico del ginocchio a 6-GDL EMG-driven per la valutazione del sovraccarico dei legamenti nelle applicazioni di prevenzione degli infortuni
PAGANO, FRANCESCO
2023/2024
Abstract
Anterior cruciate ligament injuries are among the most common injuries worldwide, especially in sports and athletics. Current studies on anterior cruciate ligament injuries demonstrate that, in countries where it has been possible to extract data at a national level, such injuries have an annual incidence in the range of 29 to 38 per 100,000 people. The aim of this thesis is to obtain an estimate of the internal forces of muscles and ligaments that cannot be measured in vivo and in particular the stresses applied to the anterior cruciate ligament, to evaluate its overload in order to prevent possible injuries. Motion analysis is based on musculoskeletal modeling, which allows the collection and analysis of information on muscle activations and forces that are difficult or impossible to measure experimentally. In this project, are of particular interest data concerning both kinematics, which defines the position, speed and acceleration of the body, and the dynamics, which defines the forces and the moments that generate the movement. In order to obtain this data, a 6-degree-of-freedom musculoskeletal model of the knee is used, which implements in detail the muscles of the lower limb, as well as the cruciate ligaments and collateral ligaments. In this study previously collected data from 5 healthy subjects, acquired as controls, and 5 pathological subjects 6-8 months after anterior cruciate ligament reconstruction recorded while performing tasks such as walking and dropping are processed. Each subject was acquired via stereophotogrammetry, a surface electromyography system, and two force plates. The data of interest such as muscle forces, muscle activations, and anterior cruciate ligament stress forces were estimated through a musculoskeletal modeling software, i.e. Opensim by adopting two different methods whose validity was assessed by comparing simulated activations with experimentally measured ones. In the first method, the results are obtained by means of static optimization via a predefined conventional pipeline described below. The second method uses the Moco software toolkit combined with surface electromyography, and includes an innovative optimization method, based on direct collocation, for estimating the parameters of interest. Through these tools, it was possible to perform a quantitative evaluation of tensions and deformations related to the knee ligaments in order to highlight any differences between healthy and pathological subjects.File | Dimensione | Formato | |
---|---|---|---|
Pagano_Francesco.pdf
embargo fino al 10/10/2027
Dimensione
9.24 MB
Formato
Adobe PDF
|
9.24 MB | Adobe PDF |
The text of this website © Università degli studi di Padova. Full Text are published under a non-exclusive license. Metadata are under a CC0 License
https://hdl.handle.net/20.500.12608/73130