Smart Generation of Human Motions for Designing Working Spaces

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This project developed an accurate simulation and animation mechanisms of human motions and behaviors for estimating and inspecting the efficiency and comfortableness of facilities environment from the viewpoint of human engineering.Simulations of human motions often require the predictive generation of motions for the different conditions of motion capture session. We have developed the motion synthesis mechanism through the interpolations of motion samples by using an accurate prediction method called kriging, which was propose in geostatistics, and have demonstrated its superiority in prediction accuracy over the method based on the radial basis functions with an objective analysis. We also demonstrated the effectiveness of this method for predicting human foot prints avoiding obstacles.Smart generation of motion data requires to adequately select motion clips from huge amount of motion data sets, and we therefore have developed the graphical user interface for motion data retrieval by distributing representative posture icons included in motion data sets onto a two dimensional display.As a part of prediction mechanism of human behaviors, we have developed the simulation of walking behaviors on the basis of the knowledge or the information discovered in urban engineering or cognitive psychology. This simulation can well-approximate the flow of human walking for various spatial densities of walkers in a macro level, and can generate plausible collision avoiding behaviors reflecting psychological features in a micro level.Based on the above mentioned technologies, this project has developed the middleware for simulating a sequence of motions with visualization tools of Web3D, from the procedural manual for estimating working time and facilities data given with X3D.