Strength Guided Motion

A methodology and algorithm is presented that generates motions imitating the way humans complete a lifting task under various loading conditions. The path taken depends on natural parameters: the figure geometry, the given load, the final destination, and especially, the strength model of the agent. Additional user controllable parameters of the motion are the comfort of the action and the perceived exertion of the agent. The algorithm uses this information to incrementally compute a motion path of the end effector moving the load. It is therefore instantaneously adaptable to changing force, loading, and strength conditions. Various strategies are used to model human behavior (such as pull back, add additional joints, and jerk) that compute the driving torques as the situation changes. The strength model dictates acceptable kinematic postures. The resulting algorithm offers torque control without the tedious user expression of driving forces under a dynamics model. The algorithm runs in near-realtime and offers an agent-dependent toolkit for fast path prediction. Examples are presented for various lifting tasks, including one- and two-handed lifts, and raising the body from a seated posture

INVERSE DYNAMICS IN SPORTS BIOMECHANICS

The aim of this paper is to illustrate developments in inverse dynamics using selected examples. It will give a description of the method with emphasis on the critical parts. Results are discussed for several examples and the methodological difficulties are specified. It is shown how hidden parameters can be uncovered with the help of inverse dynamics. The quantification of sports performance is demonstrated, and the applicability of inverse dynamics in the training process is illustrated

Velocity based controllers for dynamic character animation

Dynamic character animation is a technique used to create character movements based on physics laws. Proportional derivative (PD) controllers are one of the preferred techniques in real time character simulations for driving the state of the character from its current state to a new target-state. In this paper is presented an alternative approach named velocity based controllers that are able to introduce into the dynamical system desired limbs relative velocities as constraints. As a result, the presented technique takes into account all the dynamical system to calculate the forces that transform our character from its current state to the target-state. This technique allows realtime simulation, uses a straightforward parameterization for the character muscle force capabilities and it is robust to disturbances. The paper shows the controllers capabilities for the case of human gait animation.Postprint (published version

Evaluation of a geometry-based knee joint compared toaplanarknee joint

Today neuromuscular simulations are used in several fields, such as diagnostics and planing of surgery, to get a deeper understanding of the musculoskeletal system. During the last year, new models and datasets have been presented which can provide us with more in-depth simulations and results. The same kind of development has occurred in the field of studying the human knee joint using complex three dimensional finite element models and simulations. In the field of musculoskeletal simulations, no such knee joints can be used. Instead the most common knee joint description is an idealized knee joint with limited accuracy or a planar knee joint which only describes the knee motion in a plane. In this paper, a new knee joint based on both equations and geometry is introduced and compared to a common clinical planar knee joint. The two kinematical models are analyzed using a gait motion, and are evaluated using the muscle activation and joint reaction forces which are compared to in-vivo measured forces. We show that we are able to predict the lateral, anterior and longitudinal moments, and that we are able to predict better knee and hip joint reaction force

Human Factors Simulation Research at the University of Pennsylvania

Jack is a Silicon Graphics Iris 4D workstation-based system for the definition, manipulation, animation, and human factors performance analysis of simulated human figures. Built on a powerful representation for articulated figures, Jack offers the interactive user a simple, intuitive, and yet extremely capable interface into any 3-D articulated world. Jack incorporates sophisticated systems for anthropometric human figure generation, multiple limb positioning under constraints, view assessment, and strength model-based performance simulation of human figures. Geometric workplace models may be easily imported into Jack. Various body geometries may be used, from simple polyhedral volumes to contour-scanned real figures. High quality graphics of environments and clothed figures are easily obtained. Descriptions of some work in progress are also included

Muscle activation mapping of skeletal hand motion: an evolutionary approach.

Creating controlled dynamic character animation consists of mathe- matical modelling of muscles and solving the activation dynamics that form the key to coordination. But biomechanical simulation and control is com- putationally expensive involving complex di erential equations and is not suitable for real-time platforms like games. Performing such computations at every time-step reduces frame rate. Modern games use generic soft- ware packages called physics engines to perform a wide variety of in-game physical e ects. The physics engines are optimized for gaming platforms. Therefore, a physics engine compatible model of anatomical muscles and an alternative control architecture is essential to create biomechanical charac- ters in games. This thesis presents a system that generates muscle activations from captured motion by borrowing principles from biomechanics and neural con- trol. A generic physics engine compliant muscle model primitive is also de- veloped. The muscle model primitive forms the motion actuator and is an integral part of the physical model used in the simulation. This thesis investigates a stochastic solution to create a controller that mimics the neural control system employed in the human body. The control system uses evolutionary neural networks that evolve its weights using genetic algorithms. Examples and guidance often act as templates in muscle training during all stages of human life. Similarly, the neural con- troller attempts to learn muscle coordination through input motion samples. The thesis also explores the objective functions developed that aids in the genetic evolution of the neural network. Character interaction with the game world is still a pre-animated behaviour in most current games. Physically-based procedural hand ani- mation is a step towards autonomous interaction of game characters with the game world. The neural controller and the muscle primitive developed are used to animate a dynamic model of a human hand within a real-time physics engine environment