Animating Human Locomotion with Inverse Dynamics

Locomotion is a major component of human activity, and there have been many attempts to reveal its principles through the application of physics and dynamics. Both computer graphics and robotics continue such efforts, but many problems remain unsolved, even in characterizing the simplest case: linear, forward, rhythmic walking

Curved Path Walking

Research on biped locomotion has focused on sagittal plane walking in which the stepping path is a straight line. Because a walking path is often curved in a three dimensional environment, a 3D locomotion subsystem is required to provide general walking animation. In building a 3D locomotion subsystem, we tried to utilize pre-existing straight path (2D) systems. The movement of the center of the body is important in determining the amount of banking and turning. The center site is defined to be the midpoint between the two hip joints. An algorithm to obtain the center site trajectory that realizes the given curved walking path is presented. From the position and orientation of the center site, we compute stance and swing leg configuration as well as the upper body configuration, based on the underlying 2D system

NaturalWalk: An Anatomy-based Synthesizer for Human Walking Motions

We present a novel data-driven approach for synthesizing human gait motions with individual style characteristics and natural appearance. Our approach is based on the concept of a motion signature that captures the essential characteristic of an individual walking motion. For each joint angle our motion model consists of a shape template and feature functions that describe the variation of that shape with the stride length. For the synthesis of a walking motion, the feature functions are evaluated for a desired stride length. Then the templates are adapted to match the computed features and used as progressions for the joint angles of the skeleton. We demonstrate our data driven approach using motion data captured from 12 individuals. We report on an experiment showing that the synthesized motions have a natural appearance and maintain the individual style.:1. Introduction 2. Related Work 3. Preliminaries 3.1 Mathematics of motion 3.2 Walking motions 4. Data acquisition and analysis 5. Shape templates and feature functions 5.1 Definition of template functions 5.2 Continuous representation of template functions 5.3 Building the feature functions 6. Motion Generation 6.1 Adaption of template functions 6.2 Computing the poses 7 Experimental Results 7.1 Numerical Evaluation 7.2 User Study Acknowledgment Reference

A model for the micro-doppler effect of the quadrupedal body motion

In radar returns from quadrupedal body motion the micro-Doppler effect is present. This effect can be used to identify and distinguish this motion from others. In this work the model for the micro-Doppler effect due to quadrupedal motion is developed from Maxwell’s equations. The common gait for all quadrupedal mammal motion is described and analyzed. The radar crossection of quadrupedal motion backscattering is discussed. Quadrupedal motion is simulated and the radar returns are analyzed. The micro-Doppler signatures are decomposed and kinematic parameters are extracte

Intermittent Non-Rhythmic Human Stepping and Locomotion

When humans need to get from one location to another, there are many occasions where non-rhythmic stepping (NRS) is more desirable than normal walking. This can be observed in performing tasks in a constricted work space. For this purpose NRS is considered as a variation of curved path walking. Four types of local adjustment are dealt with: forward, backward, lateral stepping, and turnaround. Combined with curved path walking, NRS provides a very useful tool for animating human locomotion behaviors. In the lower body motion, the trajectory of the hip, angular trajectory of the feet, and the trajectory of the swing ankle during the swing phase determine the basic outline of an NRS. These trajectories are precomputed at the start of each step. The stepping process is called with a normalized time to generate the actual pose of the NRS at that moment. the normalized time is a logical time, covering zero to one during a complete step