Multi-criteria upper-body human motion adaptation

Abstract

Computer-aided simulation of human motion has enabled the integration of human factors in the evaluation of product, process and workplace designs. Realistic human motion simulation can be generated on the basis of existing captured motion. The objective of this work is the development of a new approach for adapting a given motion of a digital human model to new anthropometrics and environment constraints. In order to achieve this objective an algorithm, based on multi-criteria decision making, has been conceptualized and implemented. The proposed algorithm is based on the evaluation of randomly generated human postures, with a number of criteria suitable for preserving desirable qualities of the initial motion while the new constraints are being satisfied. The multi-criteria human motion adaptation (MCMA) algorithm is tested in a pilot case that investigates the opening of a car door from the driver's seated position. The evaluation demonstrates the prediction capabilities of the algorithm, when anthropometrics and environment parameters are modified. The algorithm generates accurate and realistic human motions that can be used in computer-aided design for ergonomics.