Digital Human Forward Kinematic and Dynamic Reliabilities

Abstract

Probabilistic methods have been applied to many problems in various fields of study. There are many distinct applications of probabilistic design in the biomechanics field, in particular. Traditionally, deterministic methods have been applied in digital human modeling (DHM). Transforming the deterministic approach of digital human modeling into a probabilistic approach is natural since there is inherent uncertainty and variability associated with DHM problems. Typically, deterministic studies in this field ignore this uncertainty or try to limit the uncertainty by employing optimization procedures. Often, inverse kinematics or dynamics techniques are introduced to point the system to the desired solution, or “best solution.” Due to the variability in the inputs, a deterministic study may not be enough to account for the uncertainty in the system. Probabilistic design techniques allow the designer to predict the likelihood of an outcome while also accounting for uncertainty, in contrast to deterministic studies. The purpose of this study is to incorporate probabilistic approaches to a deterministic DHM problem that has already been studied, analyzing human forward kinematics and dynamics. The problem is transformed into a probabilistic approach where the human forward kinematic and dynamic reliabilities are determined. The forward kinematic reliability refers to the probability that the human end-effector position (and/or orientation) falls within a specified distance from the desired position (and/or orientation) in an inverse kinematics problem. The forward dynamic reliability refers to the probability that the human end-effector position (and/or velocity) falls within a specified distance from the desired position (and/or velocity) along a specified trajectory in the workspace. The dynamic equations of motion are derived by the Lagrangian backward recursive dynamics formulation.