Christian Simonidis

A Kinematic Study of Human Torso Motion

This paper illustrates a kinematic study of human torso motion in order to design and transfer human-like motion on humanoid robots. The realization is done using motion capture data and an optimization based inverse kinematic approach for mapping motion data to skeleton models with the main focus on reproducing realistic torso motion. The kinematic model is based on a multiybody approach using relative coordinates. According to the difficulty of marker based motion reconstruction of human torso movements a detailed multibody model of the spine with a coupling structure between vertebrae based on medical data is introduced. Then, a new formulation describing the kinematic constraints between pelvis and shoulder girdle is presented in order to simplify modeling effort while maintaining natural motion of the torso. Results are compared for key movements with common models. The developed models will be used for design application in the Collaborative Research Center 588 “Humanoid Robots - Learning and Cooperating Multimodal Robots”.Copyright

CORRECTION OF MOTION CAPTURE DATA WITH RESPECT TO KINEMATIC DATA CONSISTENCY FOR INVERSE DYNAMIC ANALYSIS

Inverse dynamics analysis of human motion requires that the trajectories of the selected anatomical points are known. Therefore, standard motion capture technique by tracking marker points is generally used to obtain the trajectories. The tracking process, however, introduces high-frequency noise into the trajectories and the measured data can not be used directly to proceed in the inverse dynamic analysis. A mechanical system is consistent with kinematic data if the constraint equations of position and their time derivatives are satisfied by any parameters contained in the data set. Spurious reaction forces result from violations of the constraint equations using non consistent data. Therefore, a method is applied in this paper, whereby a new set of trajectories is generated by performing a projection of the observed positions, velocities and accelerations onto the corresponding constraint manifold to ensure the consistency of the data mentioned above. Finally, the kinematics of the system is described with the corrected data set.Copyright

A computational model of human movement coordination

Due to the numerous degrees of freedom in the human motor system, there exists an infinite number of possible movements for any given task. Unfortunately, it is currently unknown how the human central nervous system (CNS) chooses one movement out of the plethora of possible movements to solve the task at hand. The purpose of this study is the construction of a computational model of human movement coordination to unravel the principles the CNS might use to select one movement from plethora of possible movements in a given situation. Thereby, different optimization criteria were examined. The comparison of predicted and measured movement patterns exhibited that a minimum jerk strategy on joint level yielded the closest fit to the human data.

Determining the principles of human motion by combining motion analysis and motion synthesis

Synthesizing of human motion is one of the challenges in humanoid robotics research. Interested in the construction of humanoid service robots exhibiting human-like movements research is following different ways. This paper is going along with the idea of determining the principles of human motor control in order to understand the generation of human motion. A computational framework based on an efficient technique combining motion capture with multibody systems and optimal control theory for large-scale dynamic analysis and synthesis of motion is presented. Experiments were performed for human pointing gestures and the framework was validated computing the optimal trajectories of minimum hand jerk, modified minimum hand jerk, minimum angle jerk and minimum torque change.

Improving marker based inverse kinematics solutions for under-determined spinal models

A well known problem in biomechanics is the inverse kinematics based motion reconstruction from segments in a mechanical model with motion capture data. This contribution presents an optimization based inverse kinematics approach to determine solutions for under-determined systems. This paper presents the application of a constrained nonlinear least-squares optimization approach for underdetermined systems. It is suitable for large multibody chains with only few marker information available, e.g. for a detailed model of the human spine. Results of a successful motion reconstruction will be shown for a spinal model with 75dof.