Real-Time Optimal Reach-Posture Prediction in a New Interactive Virtual Environment

Abstract

Human posture prediction is a key factor for the design and evaluation of workspaces, in a virtual environment using virtual humans. This work presents a new interface and virtual environment for the direct human optimized posture prediction (D-HOPP) approach to predicting realistic reach postures of digital humans, where reach postures entail the use of the torso, arms, and neck. D-HOPP is based on the contention where depending on what type of task is being completed, and human posture is governed by different human performance measures. A human performance measure is a physics-based metric, such as energy or discomfort, and serves as an objective function in an optimization formulation. The problem is formulated as a single-objective optimization (SOO) problem with a single performance measure and as multi-objective-optimization (MOO) problem with multiple combined performance measures. We use joint displacement, change in potential energy, and musculoskeletal discomfort as performance measures. D-HOPP is equipped with an extensive yet intuitive user-interface, and the results are presented in an interactive virtual environment.