This paper describes algorithms for the animation of men and women performing three dynamic athletic behaviors: running, bicycling, and vaulting. We animate these behaviors using control algorithms that cause a physically realistic model to perform the desired maneuver. For example, control algorithms allow the simulated humans to maintain balance while moving their arms, to run or bicycle at a variety of speeds, and to perform a handspring vault. Algorithms for group behaviors allow a number of simulated bicyclists to ride as a group while avoiding simple patterns of obstacles. We add secondarymotion to the animations with springmass simulations of clothing driven by the rigid-body motion of the simulated human. For each simulation, we compare the computed motion to that of humans performing similar maneuvers both qualitatively through the comparison of real and simulated video images and quantitatively through the comparison of simulated and biomechanical data.

Birds, fish, and many other animals travel as a flock, school, or herd. Animals in these groups must remain in close proximity while avoiding collisions with neighbors and with obstacles. We would like to reproduce this behavior for groups of artificial creatures with significant dynamics. In this paper we describe an algorithm for creatures that move as a group and evaluate the performance of the algorithm with three simulated systems: legged robots, human-like bicycle riders, and point-mass systems. Both the legged robots and the bicyclists are dynamic simulations that must control balance, facing direction, and forward speed as well as movement with the group. The point-mass systems have minimal dynamics and are included to facilitate our understanding of the effects of the dynamics on the performance of the algorithms. Introduction To run as a group, animals must remain in close proximity while changing direction and velocity and avoiding collisions with other group members and o...

A musculoskeletal model was developed to determine the theoretically optimal pedaling technique (quantified as pedal forces and kinematics) for sprint cycling. Input was a set of stimulation patterns for the eight major muscle groups in the lower extremity, output are the kinematics and forces predicted by a direct dynamics simulation. Stimulation patterns were optimized until maximal average power output was reached. The optimal technique produced 529.1 W in a single limb and was sufficiently similar to typical EMG, pedal force, and kinematic data to conclude that the model is a suitably realistic representation of a cyclist. The optimal pedaling technique was characterized by a raised heel at top dead centre and a large force along the crank at bottom dead centre. In addition, negative torque was produced during the upstroke. These features, traditionally regarded as undesirable, can be explained using properties of the system and the requirement of maximal power output. In a second ...

Birds, fish, and many other animals travel as a flock, school, or herd. Animals in these groups must remain in close proximity while avoiding collisions with neighbors and with obstacles. We would like to reproduce this behavior for groups of simulated creatures traveling fast enough that dynamics plays a significant role in determining their movement. In this paper, we describe an algorithm for controlling the movements of creatures that travel as a group and evaluate the performance of the algorithm with three simulated systems: legged robots, humanlike bicycle riders, and point-mass systems. Both the legged robots and the bicyclists are dynamic simulations that must control balance, facing direction, and forward speed as well as position within the group. The simpler point-mass systems are included because they help us to understand the effects of the dynamics on the performance of the algorithm.

I would like to thank the staff at the Radlabor Freiburg (Radlabor GmbH, Freiburg, Germany) for the use of the laboratory facilities and for providing assistance with the measurement devices. In cycling, force applied to the pedal is conventionally measured using strain gauges or piezoelectric force transducers within the pedal body or at the crank. This thesis takes a different approach to determine pedal force based on motion capturing. Pedal force is calculated as the sum of forces needed to overcome the resistance of the ergometer brake and the moment of inertia of the ergometer’s flywheel. The former is obtained from cadence and power measurements of the ergometer, the latter by means of flywheel inertia and angular acceleration of the crank. The crank angle was determined by tracking the pedal movement using motion capturing. Then the second derivative of the crank angle gave its angular acceleration. As noise inherent in measurement data causes serious problems when computing derivatives, the data was smoothed beforehand. Three smoothing techniques were applied: a Butterworth filter, a Kalman smoother, and singular spectrum analysis. The angular acceleration obtained by the three methods was similar. The analysis of the pedal motion data revealed that systematic errors and strong measurement noise prevent sufficiently accurate estimates of the angular acceleration of the crank. Therefore, the resulting pedal force estimates differ considerably from the force obtained by a pedal force measurement device (Powertec System). ii Kurzfassung