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Multiple User Defined End-Effectors with Shared Memory Communication for Posture Prediction
ISSN: 0148-7191, e-ISSN: 2688-3627
Published June 17, 2008 by SAE International in United States
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Inverse Kinematics on a human model combined with optimization provides a powerful tool to predict realistic human postures. A human posture prediction tool brings up the need for greater flexibility for the user, as well as efficient computation performance. This paper demonstrates new methods that were developed for the application of digital human simulation as a software package by allowing for any number of user specified end-effectors and increasing communication efficiency for posture prediction. The posture prediction package for the digital human, Santos™, uses optimization constrained by end-effectors on the body with targets in the environment, along with variable cost functions that are minimized, to solve for all joint angles in a human body. This results in realistic human postures which can be used to create optimal designs for things that humans can physically interact with. Previously the end-effectors could only be specified in relation to the left and right wrist and ankle joints. Since the tool was still in developmental phases, communication between the software used to visualize the digital human and environment was done through file I/O. A new optimization method has been developed and implemented to allow for any number of user specified end-effectors, which can be in relation to any joint in the body. Each end-effector can be constrained to any individual target in the environment, which allows for much more flexible interface for a user to define the boundaries of predicting human posture. Communication speeds were increased on average by almost six times through the use of creating a shared memory block, which can be accessed by posture prediction code and the application to visualize the resulting postures at the same time. The combined results of these additional features for posture prediction allow for dynamically updating and visualizing of posture prediction results as new targets for any part of the human body are created or changed in the environment. This in turn provides a new intuitive method for creating a posture prediction simulation which is more interactive with the user.
CitationRochambeau, B., Marler, T., Mathai, A., and Abdel-Malek, K., "Multiple User Defined End-Effectors with Shared Memory Communication for Posture Prediction," SAE Technical Paper 2008-01-1922, 2008, https://doi.org/10.4271/2008-01-1922.
- Abdel-Malek, K., Yang, J., Marler, T., Beck, S., Mathai, A., Zhou, X., Patrick, A., and Arora, J. (2006), “Towards a New Generation of Virtual Humans,” International Journal of Human Factors Modeling and Simulation, 1 (1), 2-39.
- Abdel-Malek, K., Yu, W., and Jaber, M., (2001), “Realistic Posture Prediction,” 2001 SAE Digital Human Modeling and Simulation.
- Beck, D. J., and Chaffin, D. B. (1992), “An Evaluation of Inverse Kinematics Models for Posture Prediction”, Computer Applications in Ergonomics, Occupational Safety and Health, Elsevier, Amsterdam, The Netherlands, 329-336.
- Chaffin, D. B. (2002), “On Simulating Human Reach Motions for Ergonomic Analysis”, Human Factors and Ergonomics in Manufacturing, 12, (3), 235-247.
- Das, B., and Behara, D. N. (1998), “Three-Dimensional Workspace for Industrial Workstations”, Human Factors, 40, (4), 633-646.
- Denavit, J. and Hartenberg, R.S., (1955), “A kinematic notation for lower-pair mechanisms based on matrices”, Journal of Applied Mechanics, Vol. 77, pp. 215-221.
- Faraway, J. J. (1997), Regression Analysis for a Functional Response”, Techometrics, 39, (3), 254-262.
- Faraway, J. J., Zhang, X. D., and Chaffin, D. B. (1999), “Rectifying Postures Reconstructed from Joint Angles to Meet Constraints”, Journal of Biomechanics, 32, 733-736.
- Farrell, K. and Marler, R.T., (2004), “Optimization-Based Kinematic Models for Human Posture”, University of Iowa, Virtual Soldier Research Program, Technical Report Number VSR-04.11.
- Farrell, K., Marler, R.T., and Abdel-Malek, K., (2005) “Modeling Dual-Arm Coordination for Posture: An Optimization-Based Approach”, University of Iowa, Virtual Soldier Research Program, SAE paper 2005-01-2686.
- Jung, E.S. and Choe, J., (1996), “Human reach posture prediction based on psychophysical discomfort”, International Journal of Industrial Ergonomics, Vol. 18, pp. 173-179.
- Jung, E.S., Kee, D., and Chung, M.K., (1995), “Upper body reach posture prediction for ergonomic evaluation models”, International Journal of Industrial Ergonomics, Vol. 16, pp. 95-107.
- Marler, R.T., Rahmatalla, S, Shanahan, M, and Abdel-Malek, K., (2005) “A New Discomfort Function for Optimization-Based Posture Prediction”, University of Iowa, Virtual Soldier Research Program, SAE paper 2005-01-2680.
- Mi, Z., Yang, J., and Abdel-Malek, K., (2002), “Real-Time Inverse Kinematics for Humans,” Proceedings of 2002 ASME Design Engineering Technical Conferences, DETC2002/MECH-34239, September 29-October 2, Montreal, Canada.
- Tolani, D., Goswami, A., and Badler, N., (2000), “Real-Time Inverse Kinematics Techniques for Anthropomorphic Limbs”, Graphical Models, Vol. 62, No. 5, pp. 353-388.
- Wang, X.G., (1999), “A behavior-based inverse kinematics algorithm to predict arm prehension postures for computer-aided ergonomic evaluation”, Journal of Biomechanics, Vol. 32, pp. 453-460.
- Wang, Shi-You, Guo, Fu-Shun, Shuo-Ben, Bi, and Zang, Tian-Yi, (1999), “An improved inter-process communication mechanism using shared memory”, Mini-Micro Systems, v 20.
- Yang, J., Marler, R.T., Kim, H., Arora, J., and Abdel-Malek, K., (2004), “Multi-Objective Optimization for Upper Body Posture Prediction,” 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Aug. 30-Sept. 1, 2004, Albany, New York, USA.
- Yang, J., Marler, R.T., Beck, S., Abdel-Malek, K, and Kim, J., (2006) “Real-Time Optimal Reach-Posture Prediction in a New Interactive Virtual Environment,” Journal of Computer Science and Technology, Vol. 21, No. 2, 2006, pp. 189-198.
- Yang, J., Kim, J., Abdel-Malek, K., Marler, T., Beck, S., and Kopp, G., (2007) “A New Digital Human Environment and Assessment of Vehicle Interior Design,” Computer-Aided Design, Vol. 39, 2007, 548-558.
- Zhang, X., and Chaffin, D. B. (1996), “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, Philadelphia, PA, Part 1, 1, 594-598.