Title: Comprehensive Biomechanical Modeling and Control of the Upper Body

Abstract:
Physics-based modeling and control has made significant strides in the area of biomechanical computer animation. This talk will review our recent work on the comprehensive biomechanical modeling of the human upper body. I will present a comprehensive biomechanical model of the human upper body, confronting the combined challenge of modeling and controlling more or less all of the relevant articular bonesand muscles, as well as simulating the physics based deformations and bulging of the soft tissues. In particular, our dynamic skeletal model comprises 68 bones and 147 articular degrees of freedom, including those of each vertebra and most of the ribs. To be properly actuated and controlled, the skeletal model requires comparable attention to detail with respect to muscle modeling. We incorporate a full complement of actuators, a total of 814 muscles, each of which is modeled as a Hill-type force actuator. To simulate the biomechanics of the active muscular tissues and passive soft tissues, we also develop a coupled finite element model with the appropriate constitutive behavior, in which are embedded the detailed 3D anatomical geometries of upper-body skin, muscle, and bone. An important component of our model is the neck-head-face complex. Unlike the human face, the neck has been largely overlooked in the computer graphics literature, this despite its complex anatomical structure and the important role that it plays in supporting the head in balance while generating the controlled head movements that are essential to so many aspects of human behavior. I will present a biomechanical model of the human head-neck system. Emulating the relevant anatomy, our model is characterized by appropriate kinematic redundancy (7 cervical vertebrae coupled by 3-DOF joints) and muscle actuator redundancy (72 neck muscles arranged in 3 muscle layers). Our anatomically consistent biomechanical model confronts us with a challenging motor control problem, even for the relatively simple task of balancing the mass of the head in gravity atop the cervical spine. In our initial work on confronting the motor control challenge, we have developed a novel neuromuscular control model for human head animation that emulates the relevant biological motor control mechanisms. Incorporating low-level reflex and high-level voluntary sub-controllers, our hierarchical controller provides input motor signals to the numerous muscle actuators. In addition to head pose and movement, it controls the tone of mutually opposed neck muscles to regulate the stiffness of the head-neck multibody system. Employing machine learning techniques, the neural networks within our neuromuscular controller are trained offline to efficiently generate the online pose and tone control signals necessary to synthesize a variety of autonomous movements for the behavioral animation of the human head and face.