Research Papers

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

[+] Author and Article Information
Hadi I. Masoud

Integrative Systems and Design
University of Michigan–Ann Arbor,
Ann Arbor, MI 48109;
Industrial Engineering,
King Abdul-Aziz University,
Jeddah 21589, Saudi Arabia
e-mail: hadimas@umich.edu

Matthew P. Reed

University of Michigan Transportation
Research Institute,
Ann Arbor, MI 48109;
Industrial and Operations Engineering,
University of Michigan–Ann Arbor,
Ann Arbor, MI 48109
e-mail: mreed@umich.edu

Kamran Paynabar

Industrial Systems Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: kamran.paynabar@isye.gatech.edu

Nanxin Wang

Vehicle Design Department,
Ford Motor Company,
Dearborn, MI 48120
e-mail: nwang1@ford.com

Jionghua (Judy) Jin

Industrial and Operations Engineering,
University of Michigan–Ann Arbor,
Ann Arbor, MI 48109
e-mail: jhjin@umich.edu

Jian Wan

Vehicle Design Department,
Ford Motor Company,
Dearborn, MI 48109
e-mail: jwan1@ford.com

Ksenia K. Kozak

Vehicle Design Department,
Ford Motor Company,
Dearborn, MI 48120
e-mail: kkozak3@ford.com

Gianna Gomez-Levi

Vehicle Design Department,
Ford Motor Company,
Dearborn, MI 48120
e-mail: ggomezle@ford.com

1Corresponding author.

Manuscript received August 21, 2014; final manuscript received November 8, 2015; published online January 5, 2016. Assoc. Editor: Jianjun Shi.

J. Manuf. Sci. Eng 138(6), 061001 (Jan 05, 2016) (8 pages) Paper No: MANU-14-1442; doi: 10.1115/1.4032191 History: Received August 21, 2014; Revised November 08, 2015

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 non-negative 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.

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Fig. 1

Car seat design variables as per SAE J1100

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Fig. 2

Motion trajectories calculated from one ingress trial

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Fig. 3

Framework for developing a classifier based on human motion

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Fig. 4

Define start/end points using the right-leg-first strategy

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Fig. 5

Example of right ankle curves (z direction) before registration

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Fig. 6

Example of right ankle curves (z direction) after registration

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Fig. 8

SVM illustration—example of a linearly separable case with two variables (adapted from Cortes and Vapnik [27])

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Fig. 9

Importance of joints using GNNG

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Fig. 10

SGS results at each step




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