The human shoulder is a complex musculoskeletal system. Knowledge about its kinematics and dynamics can help improve associated treatments. However, to date direct measurements of these quantities can be only granted through invasive investigations or expensive imaging techniques. Musculoskeletal shoulder models provide useful predictions of shoulder kinematics and dynamics. Nevertheless, there remain significant gaps between the model predictions and behaviors of the real system. This thesis aims at extending an existing shoulder musculoskeletal model for patient-specific clinical applications. To this end, number of improvements are considered.
The initial model only considered an outstretch arm. Therefore, the elbow and the muscles spanning it are added in the extended model. To this end, the bone morphologies of the ulna and the radius and muscles architectures are obtained from MRI scans. The elbow is modeled using two hinge joints replicating its flexion/extension and pronation/supination motions.
The model is developed based on anthropometric data of a single subject. Given anthropometric variabilities among subjects, it cannot predict inter-individual differences. Therefore, scaling routines are developed to scale the model to a specific subject. The model's bone segment inertial properties, skeletal morphologies, and muscles architectures are scaled according to any specific subject. The effects of anthropometric parameters on glenohumeral (GH) joint reaction force predictions are evaluated.
Humeral head translations (HHT) play a crucial role in the GH joint functions. Given that the model is developed based on inverse dynamics, it falls short of predicting the HHT. Therefore, a framework is developed allowing forward-dynamics simulation of the model with a six DOF GH joint. A deformable articular contact is included in the framework defining the GH joint contact force in terms of the joint rotations and translations.
A videogrammetry systems is used for recording upper extremity motions. It measures trajectories of skin-fixed markers. However, it cannot practically track scapula motions and the GH joint center. Therefore, a method is developed estimating the GH joint center and consequently scapula motions. Multi-segment optimization is used to reconstruct the measured motions in terms of joints angles.
A musculotendon model is a key component for muscle-driven applications of the model. A Hill-type musculotendon model is developed. However, the initial state of the Hill-type model is not provided. Therefore, singular perturbation analysis is used to propose a method providing an initial state for the developed Hill-type model.
Given that the model is over-actuated, an optimal load-sharing is used to predict muscle forces. It overlooks antagonistic muscle co-contractions. However, muscle co-contractions play crucial roles in the GH joint stability. Therefore, the load-sharing is modified such that measured electromyography (EMG) data can be incorporated. It is hypothesized that inclusions of the measured EMG can improve model predictions of muscle co-contractions.
The developed model provides predictions of joints angles, muscles forces, and GH joint force and translations that are in good agreements with in vivo studies. It could be populated with pre/post operative patients of total shoulder arthroplasty to answer clinical questions regarding treatments of GH joint osteoarthritis.
EPFL2018
