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.

Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.


Wegner, D. , Chiang, J. , Kemmer, B. , Lamkull, D. , and Roll, R. , 2007, “ Digital Human Modeling Requirements and Standardization,” SAE Technical Paper No. 2007-01-2498.
Bottoms, D. , 1983, “ Design Guidelines for Operator Entry–Exit Systems on Mobile Equipment,” Appl. Ergon., 14(2), pp. 83–90. [CrossRef] [PubMed]
Petzäll, J. , 1995, “ The Design of Entrances of Taxis for Elderly and Disabled Passengers,” Appl. Ergon., 26(5), pp. 343–352. [CrossRef] [PubMed]
Kim, S. H. , and Lee, K. , 2009, “ Development of Discomfort Evaluation Method for Car Ingress Motion,” Int. J. Automot. Technol., 10(5), pp. 619–627. [CrossRef]
Giacomin, J. , and Quattrocolo, S. , 1997, “ An Analysis of Human Comfort When Entering and Exiting the Rear Seat of an Automobile,” Appl. Ergon., 28(5–6), pp. 397–406. [CrossRef] [PubMed]
Causse, J. , Wang, X. , and Denninger, L. , 2012, “ An Experimental Investigation on the Requirement of Roof Height and Sill Width for Car Ingress and Egress,” Ergonomics, 55(12), pp. 1596–1611. [CrossRef] [PubMed]
Dufour, F. , and Wang, X. , 2005, “ Discomfort Assessment of Car Ingress/Egress Motions Using the Concept of Neutral Movement,” SAE International Paper No. 2005-01-2706.
Bellman, R. , 1961, Adaptive Control Processes: A Guided Tour, Princeton University Press, Princeton, NJ.
Donoho, D. L. , 2000, “ High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality,” AIDE-Memoire of the Lecture in AMS Conference Math Challenges of 21st Century.
Fan, J. , and Li, R. , 2006, “ Statistical Challenges With High Dimensionality: Feature Selection in Knowledge Discovery,” International Congress of Mathematicians, Madrid, Spain.
Vapnik, V. N. , 1998, Statistical Learning Theory, Wiley, New York.
Jian, A. K. , Duin, R. P. W. , and Mao, J. , 2000, “ Statistical Pattern Recognition: A Review,” IEEE Trans. Pattern Anal. Mach. Intell., 22(1), pp. 4–37. [CrossRef]
Fan, J. , and Fan, Y. , 2008, “ High-Dimensional Classification Using Features Annealed Independence Rules,” Ann. Stat., 36(6), pp. 2605–2637. [CrossRef] [PubMed]
Ramsay, J. O. , and Li, X. , 1998, “ Curve Registration,” J. R. Stat. Soc.: Ser. B, 60(2), pp. 351–363. [CrossRef]
Chateauroux, E. , 2009, “ Analyse du Mouvement d'accessibilité au Poste de Conduite d'une Automobile en vue de la Simulation—Cas Particulier des Personnes Âgées,” Ph.D. thesis, INSA Lyon, France.
Ramsay, J. O. , and Silverman, B. W. , 2005, Functional Data Analysis, Springer, Berlin, Germany.
Kohavi, R. , and John, G. H. , 1997, “ Wrappers for Feature Subset Selection,” Artif. Intell., 97(1–2), pp. 273–324. [CrossRef]
Davis, P. J. , 1975, Interpolation and Approximation, Dover, New York.
De Boor, C. , 2001, A Practical Guide to Splines, Springer, Berlin, Germany.
Cardot, H. , Crambes, C. , and Sarda, P. , 2004, “ Spline Estimation of Conditional Quantiles for Functional Covariates,” C. R. Math., 339(2), pp. 141–144. [CrossRef]
Sambhav, K. , Tandon, P. , and Dhande, S. G. , 2014, “ Force Modeling for Generic Profile of Drills,” ASME J. Manuf. Sci. Eng., 136(4), p. 041019. [CrossRef]
Yuan, M. , and Lin, Y. , 2006, “ Model Selection and Estimation in Regression With Grouped Variable,” J. R. Stat. Soc.: Ser. B, 68(1), pp. 49–67. [CrossRef]
Paynabar, K. , Jin, J. , and Reed, M. , 2015, “ Informative Sensor and Feature Selection Via Hierarchical Non-Negative Garrote,” Technometrics, 57(4), pp. 514–523. [CrossRef]
Friedman, J. , Hastie, T. , and Tibshirani, R. , 2008, The Elements of Statistical Learning, Springer, Berlin, Germany.
Hocking, R. R. , 1976, “ The Analysis and Selection of Variables in Linear Regression,” Biometrics, 32(1), pp. 1–49. [CrossRef]
Draper, N. , and Smith, H. , 1998, Applied Regression Analysis, Wiley, New York.
Cortes, C. , and Vapnik, V. , 1995, “ Support-Vector Networks,” Mach. Learn., 20(3), pp. 273–297.
Cherkassky, V. , and Ma, Y. , 2004, “ Practical Selection of SVM Parameters and Noise Estimation for SVM Regression,” Neural Networks, 17(1), pp. 113–126. [CrossRef] [PubMed]
Pal, M. , and Foody, G. M. , 2010, “ Feature Selection for Classification of Hyperspectral Data by SVM,” IEEE Trans. Geosci. Remote Sens., 48(5), pp. 2297–2307. [CrossRef]
Du, S. , Liu, C. , and Xi, L. , 2015, “ A Selective Multiclass Support Vector Machine Ensemble Classifier for Engineering Surface Classification Using High Definition Metrology,” ASME J. Manuf. Sci. Eng., 137(1), p. 011003. [CrossRef]
Boser, B. E. , Guyon, I. M. , and Vapnik, V. , 1992, “ A Training Algorithm for Optimal Margin Classifiers,” Annual ACM Workshop on COLT, pp. 144–152.
Matheny, M. E. , Resnic, F. S. , Arora, N. , and Ohno-Machado, L. , 2007, “ Effects of SVM Parameter Optimization on Discrimination and Calibration for Post-Procedural PCI Mortality,” J. Biomed. Inf., 40(6), pp. 688–697. [CrossRef]
Freund, Y. , and Schapire, R. E. , 1997, “ A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,” J. Comput. Syst. Sci., 55(1), pp. 119–139. [CrossRef]
Breiman, L. , 2001, “ Random Forests,” Mach. Learn., 45(1), pp. 5–32 [CrossRef]
Cheung, K. M. , Baker, S. , and Kanade, T. , 2005, “ T. Shape-From-Silhouette Across Time. Part II: Applications to Human Modeling and Markerless Motion Tracking,” Int. J. Comput. Vision, 63(3), pp. 225–245. [CrossRef]
Corazza, S. , Mündermann, L. , Chaudhari, A. M. , Demattio, T. , Cobelli, C. , and Andriacchi, T. P. , 2006, “ A Markerless Motion Capture System to Study Musculoskeletal Biomechanics: Visual Hull and Simulated Annealing Approach,” Ann. Biomed. Eng., 34(6), pp. 1019–1029. [CrossRef] [PubMed]


Grahic Jump Location
Fig. 3

Framework for developing a classifier based on human motion

Grahic Jump Location
Fig. 2

Motion trajectories calculated from one ingress trial

Grahic Jump Location
Fig. 1

Car seat design variables as per SAE J1100

Grahic Jump Location
Fig. 4

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

Grahic Jump Location
Fig. 5

Example of right ankle curves (z direction) before registration

Grahic Jump Location
Fig. 6

Example of right ankle curves (z direction) after registration

Grahic Jump Location
Fig. 8

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

Grahic Jump Location
Fig. 9

Importance of joints using GNNG

Grahic Jump Location
Fig. 10

SGS results at each step



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In