Hostname: page-component-6766d58669-kn6lq Total loading time: 0 Render date: 2026-05-14T15:31:01.376Z Has data issue: false hasContentIssue false
  • English
  • Français

Multi-objective optimization-based method for kinematic posture prediction: development and validation

Published online by Cambridge University Press:  30 April 2010

Jingzhou (James) Yang ,
Tim Marler  and
Salam Rahmatalla
Jingzhou (James) Yang*
Affiliation:
Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409, USA
Tim Marler
Affiliation:
Center for Computer-Aided Design, The University of Iowa, Iowa City, IA 52242, USA
Salam Rahmatalla
Affiliation:
Center for Computer-Aided Design, The University of Iowa, Iowa City, IA 52242, USA
*
*Corresponding author. E-mail: james.yang@ttu.edu
Get access Rights & Permissions [Opens in a new window]

Summary

Posture prediction plays an important role in product design and manufacturing. There is a need to develop a more efficient method for predicting realistic human posture. This paper presents a method based on multi-objective optimization (MOO) for kinematic posture prediction and experimental validation. The predicted posture is formulated as a multi-objective optimization problem. The hypothesis is that human performance measures (cost functions) govern how humans move. Twelve subjects, divided into four groups according to different percentiles, participated in the experiment. Four realistic in-vehicle tasks requiring both simple and complex functionality of the human simulations were chosen. The subjects were asked to reach the four target points, and the joint centers for the wrist, elbow, and shoulder and the joint angle of the elbow were recorded using a motion capture system. We used these data to validate our model. The validation criteria comprise R-square and confidence intervals. Various physics factors were included in human performance measures. The weighted sum of different human performance measures was used as the objective function for posture prediction. A two-domain approach was also investigated to validate the simulated postures. The coefficients of determinant for both within-percentiles and cross-percentiles are larger than 0.70. The MOO-based approach can predict realistic upper body postures in real time and can easily incorporate different scenarios in the formulation. This validated method can be deployed in the digital human package as a design tool.

Keywords

Digital humansPredicted postureMOOHuman performance measuresValidation

Information

Type
Article
Information
Robotica , Volume 29 , Issue 2 , March 2011 , pp. 245 - 253
Copyright
Copyright © Cambridge University Press 2010

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.)

Article purchase

Temporarily unavailable

References

1.Beck, D. J. and Chaffin, D. B., “An evaluation of inverse kinematics models for posture prediction,” In: Computer Applications in Ergonomics, Occupational Safety and Health (Mattila, M. and Karwowski, W., eds.) (Elsevier, Amsterdam, 1992) pp. 329–336.Google Scholar
2.Bean, J. C., Chaffin, D. B. and Shultz, A. B., “Biomechanical model calculation of muscle contraction forces: A double linear programming method,” J. Biomech. 21, 59–66 (1988).CrossRefGoogle ScholarPubMed
3.Byun, S., Development of a Multivariate Biomechanical Posture Prediction Model Using Inverse Kinematics Ph.D. Dissertation (Ann Arbor, MI: University of Michigan, 1991).Google Scholar
4.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
5.Das, B. and Behara, D. N., “Three-dimensional workspace for industrial workstations,” Hum. Factors 40 (4), 633–646 (1998).Google Scholar
6.Das, B. and Sengupta, A. K., “Computer-aided human modeling programs for workstation design,” Ergonomics 38, 1958–1972 (1995).Google Scholar
7.Dysart, M. J. and Woldstad, J. C. “Posture prediction for static sagittal-plane lifting,” J. Biomech. 29 (10), 1393–1397 (1996).Google Scholar
8.Faraway, J. J., Zhang, X. D. and Chaffin, D. B., “Rectifying postures reconstructed from joint angles to meet constraints,” J. Biomech. 32, 733–736 (1999).Google Scholar
9.Gill, P., Murray, W. and Saunders, A., “SNOPT: An SQP algorithm for large-scale constrained optimization,” SIAM J. Optim. 12 (4), 979–1006 (2002).Google Scholar
10.Griffin, M., “The validation of biodynamic models,” Clin. Biomech. 16 (1), S81–S92 (2001).Google Scholar
11.Hagio, K., Sugano, N., Nishii, T., Miki, H., Otake, Y., Hattori, A., Suzuki, N., Yonenobu, K., Yoshikawa, H. and Ochi, T., “A novel system of four-dimensional motion analysis after total hip athroplasty,” J. Orthop. Res. 22 (3), 665–670 (2004).Google Scholar
12.Halvorsen, K., Lesser, M. and Lindberg, A., “A new method for estimating the axis of rotation and the center of rotation,” J. Biomech. 32, 1221–1227 (1999).Google Scholar
13.Jung, E. S., Kee, D. and Chung, M. K., “Reach Posture Prediction of Upper Limb for Ergonomic Workspace Evaluation,” Proceedings of the 36th Annual Meeting of the Human Factors Society, Atlanta, GA, Part 1, vol. 1 (1992), pp. 702–706.Google Scholar
14.Jung, E. S., Kee, D. and Chung, M. K., “Upper body reach posture prediction for ergonomic evaluation models,” Int. J. Ind. Ergon. 16, 95–107 (1995).CrossRefGoogle Scholar
15.Jung, E. S. and Choe, J., “Human reach posture prediction based on psychophysical discomfort,” Int. J. Ind. Ergon. 18 (2–3), 173–179 (1996).Google Scholar
16.Kee, D., Jung, E. S. and Chang, S., “A man-machine interface model for ergonomic design,” Comput. Ind. Eng. 27, 365–368 (1994).CrossRefGoogle Scholar
17.Kerk, C. J., Development and Evaluation of a Static Hand Force Exertion Capability Model Using Strength, Stability and Coefficient of Friction Ph.D. Dissertation (Ann Arbor, MI: University of Michigan, 1992).Google Scholar
18.Kim, J., Yang, J. and Abdel-Malek, K., “Multi-objective optimization approach for predicting seated posture considering balance,” Int. J. Veh. Des. 51 (3/4), 278–291 (2009).Google Scholar
19.Mi, Z., Yang, J. and Abdel-Malek, K., “Optimization-based posture prediction for human upper body,” Robotica 27 (4), 607–620 (2009).Google Scholar
20.Marler, R. T., Rahmatalla, S., Shanahan, M. and Abdel-Malek, K., “A New Discomfort Function for Optimization-Based Posture Prediction,” Paper presented at the SAE Human Modeling for Design and Engineering Conference, Iowa City, IA (June, 2005).Google Scholar
21.Marler, R. T., Farell, K., Kim, J., Rahmatalla, S. and Abdel-Malek, K., “Vision Performance Measures for Optimization-Based Posture Prediction,” Paper presented at the SAE Human Modeling for Design and Engineering Conference, Lyon, France (July, 2006).Google Scholar
22.Marler, T., Yang, J., Rahmatalla, S., Abdel-Malek, K. and Harrison, C., “New Validation Protocol for Predicted Posture,” Paper presented at the SAE Digital Human Modeling for Design and Engineering, Seattle, University of Washington, WA (June 12–14, 2007).Google Scholar
23.Marler, R. T., Arora, J. S., Yang, J., Kim, H.-J. and Abdel-Malek, K., “Use of multi-objective optimization for digital human posture prediction,” Eng. Optim. 41 (10), 925–943 (2009).Google Scholar
24.Park, K. S., A Computerized Simulation Model of Postures during Manual Materials Handling Ph.D. Dissertation (Ann Arbor, MI: University of Michigan, 1973).Google Scholar
25.Pena Pitarch, E., Yang, J. and Abdel-Malek, K., “Santos Hand: A 25-Degree-of-Freedom Model,” Paper presented at the SAE Digital Human Modeling for Design and Engineering, Iowa City, IA (June 14–16, 2005).Google Scholar
26.Porter, J. M., Case, K. and Bonney, M. C., “Computer Workspace Modeling,” In: Evaluation of Human Work (Wilson, J. R. and Corlett, E. N., eds.) (Taylor & Francis, London, 1990) pp. 472–499.Google Scholar
27.Robert, J. J., Michele, O. and Gordon, L. H., “Validation of the Vicon 460 Motion Capture System for Whole-Body Vibration Acceleration Determination,” Paper presented at the ISB XXth Congress-ASB 29th Annual Meeting, Cleveland, OH (Jul. 31–Aug. 5, 2005).Google Scholar
28.Rahmatalla, S., Xia, T., Contratto, M., Wilder, D., Frey-Law, L., Kopp, G. and Grosland, N., “3D Displacement, Velocity, and Acceleration of Seated Operators in Whole-Body Vibration Environment using Optical Motion Capture Systems,” Paper presented at the Ninth International Symposium on the 3-D Analysis of Human Movement, Valenciennes (France) (June 28–30, 2006).Google Scholar
29.Tolani, D., Goswami, A. and Badler, N., “Real-time inverse kinematics techniques for anthropomorphic limbs,” Graph. Models 62 (5), 353–388 (2000).Google Scholar
30.Wang, X. G. and Verriest, J. P., “A geometric algorithm to predict the arm reach posture for computer-aided ergonomic evaluation,” J. Vis. Comput. Animat. 9, 33–47 (1998).Google Scholar
31.Wang, X., “A behavior-based inverse kinematics algorithm to predict arm prehension postures for computer-aided ergonomic evaluation,” J. Biomech. 32, 453–460 (1999).Google Scholar
32.Wang, X., Chevalet, N., Monnier, G., Ausejo, S., Suescun, A. and Celigueta, J., “Validation of a Model-Based Motion Reconstruction Method Developed in the REALMAN Project,” Proceedings of SAE Digital Human Modeling for Design and Engineering Symposium, Paper no. 2005-01-2743, Iowa City, IA (June 14–16, 2005).Google Scholar
33.Yang, J., Marler, R. T., Kim, H., Arora, J. and Abdel-Malek, K., Multi-Objective Optimization for Upper Body Posture Prediction, The 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY, (Aug. 30–Sep. 1, 2004).Google Scholar
34.Yang, J., Marler, T., Kim, H. J., Farrell, K., Mathai, A., Beck, S., Abdel-Malek, K., Arora, J. and Nebel, K., “A New Generation of Virtual Humans,” SAE 2005 World Congress, Cobo Center, Detroit, MI (Apr. 11–14, 2005).Google Scholar
35.Yang, J., Rahmatalla, S., Marler, T., Abdel-Malek, K. and Harrison, C., “Validation of Predicted Posture for the Virtual Human Santos,” The 12th International Conference on Human-Computer Interaction (HCI), Beijing International Convention Center, Beijing, China (Jul. 22–27, 2007a).Google Scholar
36.Yang, J., Marler, R. T., Beck, S., Abdel-Malek, K. and Kim, J., “Real-time optimal reach-posture prediction in a new interactive virtual environment,” J. Comput. Sci. Technol. 21 (2), 189–198 (2006).CrossRefGoogle Scholar
37.Yang, J., Kim, J., Abdel-Malek, K., Marler, T., Beck, S. and Kopp, G., “A new digital human environment and assessment of vehicle interior design,” Comput.-Aided Des. 39, 548–558 (2007b).Google Scholar
38.Zou, Q., Zhang, Q. and Yang, J., “Determining Weights of Joint Displacement Objective Function in Optimization-Based Posture Prediction,” First International Conference on Applied Digital Human Modeling, Miami, FL (Jul. 17–20, 2010).Google Scholar