Multiple User Defined End-Effectors with Shared Memory Communication for Posture Prediction 2008-01-1922
Inverse Kinematics on a human model combined with optimization provides a powerful tool to predict realistic human postures. A human posture prediction tool brings up the need for greater flexibility for the user, as well as efficient computation performance. This paper demonstrates new methods that were developed for the application of digital human simulation as a software package by allowing for any number of user specified end-effectors and increasing communication efficiency for posture prediction. The posture prediction package for the digital human, Santos™, uses optimization constrained by end-effectors on the body with targets in the environment, along with variable cost functions that are minimized, to solve for all joint angles in a human body. This results in realistic human postures which can be used to create optimal designs for things that humans can physically interact with. Previously the end-effectors could only be specified in relation to the left and right wrist and ankle joints. Since the tool was still in developmental phases, communication between the software used to visualize the digital human and environment was done through file I/O. A new optimization method has been developed and implemented to allow for any number of user specified end-effectors, which can be in relation to any joint in the body. Each end-effector can be constrained to any individual target in the environment, which allows for much more flexible interface for a user to define the boundaries of predicting human posture. Communication speeds were increased on average by almost six times through the use of creating a shared memory block, which can be accessed by posture prediction code and the application to visualize the resulting postures at the same time. The combined results of these additional features for posture prediction allow for dynamically updating and visualizing of posture prediction results as new targets for any part of the human body are created or changed in the environment. This in turn provides a new intuitive method for creating a posture prediction simulation which is more interactive with the user.