Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-13T07:48:51.005Z Has data issue: false hasContentIssue false
  • English
  • Français

Optimization-based posture prediction for human upper body

Published online by Cambridge University Press:  01 July 2009

Zan Mi ,
Jingzhou (James) Yang  and
Karim Abdel-Malek
Zan Mi
Affiliation:
Center for Computer-Aided Design, The University of Iowa, Iowa City, IA 52242-1000.
Jingzhou (James) Yang*
Affiliation:
Center for Computer-Aided Design, The University of Iowa, Iowa City, IA 52242-1000. Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 97409-1021.
Karim Abdel-Malek
Affiliation:
Center for Computer-Aided Design, The University of Iowa, Iowa City, IA 52242-1000.
*
*Corresponding author. E-mail: james.yang@ttu.edu
Get access Rights & Permissions [Opens in a new window]

Summary

A general methodology and associated computational algorithm for predicting postures of the digital human upper body is presented. The basic plot for this effort is an optimization-based approach, where we believe that different human performance measures govern different tasks. The underlying problem is characterized by the calculation (or prediction) of the human performance measure in such a way as to accomplish a specified task. In this work, we have not limited the number of degrees of freedom associated with the model. Each task has been defined by a number of human performance measures that are mathematically represented by cost functions that evaluate to a real number. Cost functions are then optimized, i.e., minimized or maximized, subject to a number of constraints, including joint limits. The formulation is demonstrated and validated. We present this computational formulation as a broadly applicable algorithm for predicting postures using one or more human performance measures.

Keywords

Posture predictionInverse kinematicsHuman postures
Type
Article
Information
Robotica , Volume 27 , Issue 4 , July 2009 , pp. 607 - 620
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Abdel-Malek, K., Yu, W., Tanbour, E. and Jaber, M., “Posture Prediction Versus Inverse Kinematics,” Proceedings of the ASME Design Automation Conference, Pittsburgh, PA (2001).CrossRefGoogle Scholar
2.Beck, D. J. and Chaffin, D. B., “Evaluation of Inverse Kinematic Models for Posture Prediction,” Proceedings of the International Conference on Computer Aided Ergonomics and Safety – CAES '92, Rome, Italy, May 18–20, 1992 pp. 329.Google Scholar
3.Cao, L. Y., Kim, B. G., Kurths, J. and Kim, S., “Detecting determinism in human posture control data,” Int. J. Bifur. Chaos 8 (1), 179–188 (1998).CrossRefGoogle Scholar
4.Chaffin, D. B., Motion Database (2003) http://www.engin.umich.edu/dept/ioe/HUMOSIM/database.html.Google Scholar
5.Das, B. and Behara, D. N., “Three-dimensional workspace for industrial workstations,” Human Factors 40 (4), 633–646 (1998).CrossRefGoogle Scholar
6.Das, B. and Sengupta, A. K., “Computer-aided human modeling programs for workstation design,” Ergonomics 38, 1958–1972 (1995).CrossRefGoogle ScholarPubMed
7.Denavit, J. and Hartenberg, R. S., “A kinematic notation for lower-pair mechanisms based on matrices,” J. Appl. Mech. 77, 215–221 (1955).CrossRefGoogle Scholar
8.Dysart, M. J. and Woldstad, J. C., “Posture prediction for static sagittal-plane lifting,” J. Biomech. 29 (10), 1393–1397 (1996).CrossRefGoogle ScholarPubMed
9.Eksioglu, M., Fernandez, J. E. and Twomey, J. M., “Predicting peak pinch strength: Artificial neural networks vs. regression,” Int. J. Ind. Ergonomics 18 (5–6), 431–441 (1996).CrossRefGoogle Scholar
10.Faraway, J. J., Zhang, X. and Chaffin, D. B., “Rectifying postures reconstructed from joint angles to meet constraints,” J. Biomech. 32 (7), 733–736 (1999).CrossRefGoogle ScholarPubMed
11.Goldberg, D., Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, New York, 1989).Google Scholar
12.Hestenes, D., “Invariant body kinematics; reaching and neurogeometry,” Neural Networks 7 (1), 79–88 (1994).CrossRefGoogle Scholar
13.Jung, E. S., Kee, D. and Chung, M. K., “Upper body reach posture prediction for ergonomics evaluation models,” Int. J. Ind. Ergonomics 16 (2), 95 (1995).CrossRefGoogle Scholar
14.Jung, E. S. and Choe, J., “Human reach posture prediction based on psychophysical joint displacement,” Int. J. Ind. Ergonomics 18 (2–3), 173–179 (1996).CrossRefGoogle Scholar
15.Jung, E. S., Choe, J. and Kim, S. H., “Psychophysical cost function of joint movement for arm reach posture prediction,” Proceedings of the 38th Annual Meeting of the Human Factors and Ergonomics Society, Part 1 (of 2), Vol 1, Nashville, TN (Oct. 24–28, 1994) pp. 636–640.Google Scholar
16.Jung, E. S. and Park, S., “Prediction of human reach posture using a neural network for ergonomic man models,” Proceedings of the 16th Annual Conference on Computers and Industrial Engineering, Mar. 7–9 1994, Vol 27 n 1–4, Ashikaga, Japan (Sep. 1994) pp. 369–372.Google Scholar
17.Kee, D., Jung, E. S. and Chang, S., “A man-machine interface model for ergonomic design,” Comput. Ind. Eng. 27 (1–4), 365–368 (1994).CrossRefGoogle Scholar
18.Kee, D. and Kim, S. H., “Analytic generation of workspace using the robot kinematics,” Comput. Ind. Eng. 33 (3/4), 525–528 (1997).CrossRefGoogle Scholar
19.Porter, J. M., Case, K. and Bonney, M. C., “Computer workspace modelling,”. In: Evaluation of Human Work (Wilson, J. R. and Corlett, E. N., eds.), (Taylor & Francis, London, 1990) pp. 472–499.Google Scholar
20.Tracy, M. F., “Biomechanical methods in posture analysis,”. In: Evaluation of Human Work (Wilson, J. R. and Corlett, E. N., eds.) (Taylor & Francis, London, 1990) pp. 571–604.Google Scholar
21.Wang, X., “Behavior-based inverse kinematics algorithm to predict arm prehension postures for computer-aided ergonomic evaluation,” J. Biomech. 32 (5), 453–460 (1999).CrossRefGoogle ScholarPubMed
22.Zhang, X. and Chaffin, D. B., “Task effects on three-dimensional dynamic postures during seated reaching movements: An analysis method and illustration,” Proceedings of the 1996 40th Annual Meeting of the Human Factors and Ergonomics Society. Part 1 (of 2), Vol 1, Philadelphia, PA (Sep. 2–6 1996) pp. 594–598.Google Scholar