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Predicting Subjective Responses From Human Motion: Application to Vehicle Ingress Assessment

[+] Author Affiliations
Hadi I. Masoud

University of Michigan–Ann Arbor, Ann Arbor, MIKing Abdul-Aziz University, Jeddah, Saudi Arabia

Matthew P. Reed

University of Michigan Transportation Research Institute, Ann Arbor, MIUniversity of Michigan–Ann Arbor, Ann Arbor, MI

Kamran Paynabar

Georgia Institute of Technology, Atlanta, GA

Jionghua (Judy) Jin

University of Michigan–Ann Arbor, Ann Arbor, MI

Ksenia K. Kozak, Nanxin Wang, Jian Wan, Gianna Gomez-Levi

Ford Motor Company, Dearborn, MI

Paper No. MSEC2014-4039, pp. V001T04A009; 9 pages
  • ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference
  • Volume 1: Materials; Micro and Nano Technologies; Properties, Applications and Systems; Sustainable Manufacturing
  • Detroit, Michigan, USA, June 9–13, 2014
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-4580-6
  • Copyright © 2014 by ASME


The ease of entering a car is one of the important ergonomic factors that car manufacturers consider during the process of car design. This has motivated many researchers to investigate factors that affect discomfort during ingress. The patterns of motion during ingress may be related to discomfort, but the analysis of motion is challenging. In this paper, a modeling framework is proposed to use the motions of body landmarks to predict subjectively reported discomfort during ingress. Foot trajectories are used to identify a set of trials with a consistent right-leg-first strategy. The trajectories from 20 landmarks on the limbs and torso are parameterized using B-spline basis functions. Two group selection methods, group nonnegative garrote (GNNG) and stepwise group selection (SGS), are used to filter and identify the trajectories that are important for prediction. Finally, a classification and prediction model is built using support vector machine (SVM). The performance of the proposed framework is then evaluated against simpler, more common prediction models.

Copyright © 2014 by ASME



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