Publication
Joint Angle Data Representation for Data Driven Human Motion Synthesis
Han Du; Martin Manns; Erik Herrmann; Klaus Fischer
In: Roberto Teti (Hrsg.). Procedia CIRP - Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future - Proceedings of the 48th CIRP Conference on Manufacturing Systems. CIRP Conference on Manufactoring Systems (CIRP CMS-2015), June 24-26, Ischia (Naples), Italy, Pages 746-751, Vol. 41, Elsevier, 2016.
Abstract
For ergonomic assessment of manual assembly tasks, digital simulation has received increasing attention due to its efficiency
compared to physical prototypes. One of the crucial parts of digital simulation is an accurate animation of the digital human
model (DHM). Current digital simulation tools such as Delmia V5 require interactive manual editing to produce animations,
which is time consuming and can look unnatural. On the other hand, data-driven motion synthesis that is based on motion capture
data can produce natural motions with little user involvement. The practical difficulty lies in processing motion data into a
parameterized motion model. A common approach is decomposing motions and categorizing them into finite short motion
primitives. For each motion primitive, motion data is represented as a numerical vector, on which functional principal component
analysis (FPCA) is applied to reduce dimensionality. In this work, different ways of representing joint angles from motion
capture data are explored: Euler angle, quaternion and exponential map. The data representations are evaluated for their
reconstruction error with FPCA. In the tests, quaternion representation shows best performance for motion data representation,
which contradicts a preference in literature for exponential map representation. Therefore, quaternion representation is
considered appealing for statistically modelling motion data.