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Probabilistic sensitivity analysis of in-vehicle reach tasks for digital human models considering anthropometric measurement uncertainty

Published online by Cambridge University Press:  05 March 2014

Aimee Cloutier ,
Jared Gragg  and
James Yang
Aimee Cloutier
Affiliation:
Human-Centric Design Research Laboratory, Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, USA
Jared Gragg
Affiliation:
Human-Centric Design Research Laboratory, Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, USA
James Yang*
Affiliation:
Human-Centric Design Research Laboratory, Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, USA
*
*Corresponding author. E-mail: [email protected]
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Summary

For design using digital human models, human anthropometry data are required as input and are extracted from measurements. There is inherent error associated with these measurements which impacts the output of the simulation. Current techniques in digital human modeling applications primarily employ deterministic methods which are not well suited for handling variability in anthropometric measurement. An alternative to deterministic methods is probabilistic/sensitivity analysis. This study presents a probabilistic sensitivity approach to gain insights into how uncertainty in anthropometric measurements can affect the results of a digital human model with the specific application of vehicle-related reach tasks. Sensitivity levels are found to determine the importance of variability in each joint angle and link length to the final reach. A55-degree of freedom (DOF) digital human model is introduced to demonstrate the sensitivity approach for reach tasks. Seven right-hand reach target points and two left-hand reach target points (creating a total of 14 reach tasks) within a vehicle are used to compare the sensitivities in the joint angles and link lengths resulting from measurement uncertainty. The results show that the importance of each joint angle or link length is dependent on the characteristics of the reach task and sensitivities for joint angles, and link lengths are different for each reach task.

Keywords

SensitivityDigital human modelingPostureReach tasksUncertainty
Type
Articles
Information
Robotica , Volume 33 , Issue 3 , March 2015 , pp. 498 - 512
Copyright
Copyright © Cambridge University Press 2014 

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References

1.Barratt, D. C., Chan, C. S. K., Edwards, P. J., Penney, G. P., Slomczykowsi, M., Carter, T. J. and Hawkes, D. J., “Instantiation and registration of statistical shape models of the femur and pelvis using 3D ultrasound imaging,” Med. Image Anal. 12 (3), 358–374 (2008).Google Scholar
2.Browne, M., Langley, R. S. and Gregson, P. J., “Reliability theory for load bearing biomedical implants,” Biomaterials 20 (14), 1285–1292 (1999).Google Scholar
3.Chaffin, D. B., “Human motion simulation for vehicle and workplace design,” Hum. Factors Ergon. Manuf. 17 (5), 475–484 (2007).Google Scholar
4.Cloutier, A., Gragg, J. and Yang, J. “Sensitivity analysis of achieving a reach task within a vehicle considering joint angle variability,” In. SAE World Congress. Detroit, MI (2012).Google Scholar
5.Cloutier, A., Gragg, J. and Yang, J., “Probabilistic Analysis of Joint Angle and Link Length Uncertainty on Reach Tasks,” Proceeding of the 2nd International Conference on Applied Digital Human Modeling, July 20–25, 2012, Hilton San Francisco, CA, USA (2012).Google Scholar
6.Dar, F. H., Meakin, J. R. and Aspden, R. M., “Statistical methods in finite element analysis,” J. Biomech. 35 (9), 1155–1161 (2002).CrossRefGoogle ScholarPubMed
7.Denavit, J. and Hartenberg, R., “A kinematic notation for lower-pair mechanisms based on matrices,” J. Appl. Mech. 22, 215–221 (1955).CrossRefGoogle Scholar
8.Dong, D., Wang, L., Yuan, X. and Fu, S., “Research on Digital Human Model Used in Human Factor Simulation and Evaluation of Load Carriage Equipment,” International Conference on Human-Computer Interaction, July 2011, Orlando, FL, USA (2011).Google Scholar
9.Dopico-Gonzalez, C., New, A. M. and Browne, M., “Probabilistic analysis of an uncemented total hip replacement,” Med. Eng. Phys. 31 (4), 470–476 (2009).CrossRefGoogle ScholarPubMed
10.Dopico-Gonzalez, C., New, A. M. and Browne, M., “Probabilistic finite element analysis of the uncemented hip replacement—effect of femur characteristics and implant design geometry,” J. Biomech. 43 (3), 512–520 (2009).Google Scholar
11.Easley, S. K., Pal, S., Tomaszewski, P. R., Petrella, A. J., Rullkoetter, P. J. and Laz, P. J., “Finite element-based probabilistic analysis tool for orthopaedic applications,” Comput. Methods Programs Biomed. 85 (1), 32–40 (2007).Google Scholar
12.Edwards, W. B., Taylor, D., Rudolphi, T. J., Gillette, J. C. and Derrick, T. R., “Effects of running speed on a probabilistic stress fracture model,” Clin. Biomech. 25 (4), 372–377 (2010).Google Scholar
13.Faraway, J. J. and Hu, J., “Modeling Variability in Reaching Motions,” SAE Digital Human Modeling Conference, Arlington, Virginia (2001).Google Scholar
14.Faraway, J., “Regression modeling of motion with endpoint constraints,” J. Vis. Comput. Animat. 14, 31–41 (2003).Google Scholar
15.Fitzpatrick, C. K., Baldwin, M. A., Rullkoetter, P. J. and Laz, P. J., “Combined probabilistic and principal component analysis approach for multivariate sensitivity evaluation and application to implanted patellofemoral mechanics,” J. Biomech. 44 (1), 513–521 (2010).Google ScholarPubMed
16.Galbusera, F., Anasetti, F., Bellini, C., Costa, F. and Fornari, M., “The influence of the axial, antero-posterior and lateral positions of the center of rotation of a ball-and-socket disc prosthesis on the cervical spine biomechanics,” Clin. Biomech. 25, 397–401 (2010).CrossRefGoogle Scholar
17.Gragg, J., Yang, J., Cloutier, A. and Pena-Pitrarch, E., “Effect of human link length determination on posture reconstruction,” Appl. Ergon. 44 (1), 93–100 (2013).Google Scholar
18.Haldar, A. and Mahadevan, S., Probability, Reliability and Statistical Methods in Engineering Design (John Wiley & Sons, Inc., New York, NY, 2000) pp. 149.Google Scholar
19.Holden, J. P. and Stanhope, S. J., “The effect of variation in knee center location estimates on net knee joint moments,” Gait Posture 7, 1–6 (1998).CrossRefGoogle ScholarPubMed
20.Horiuchi, T., Kanehara, M., Kagami, S. and Ehara, Y., “A Probabilistic Walk Path Model Focused on Foot Landing Points and Human Step Measurement System,” Proceedings of the 2006 IEEE International Conference on Systems, Man, and Cybernetics, Taipei, Taiwan (2006).Google Scholar
21.Howard, B., Yang, J., Cloutier, A., Howard, B., Cloutier, A. and Yang, J., “Physics-Based Seated Posture Prediction for Pregnant Women and Validation Considering Ground and Seat Pan Contacts,” Trans. ASME, J. Biomech. Eng. 134 (7), 071004-1–071004-10 (2012).Google Scholar
22.Jung, K., Kwon, O. and You, H., “Development of a digital human model generation method for ergonomic design in virtual environment,” Int. J. Ind. Ergon. 39, 744–748 (2009).CrossRefGoogle Scholar
23.Kedgley, A., Birmingham, T. and Jenkyn, T., “Comparative accuracy of radiostereometric and optical tracking systems,” J. Biomech. 42, 1350–1354 (2009).CrossRefGoogle ScholarPubMed
24.Kim, S. H., Pyun, J. K. and Choi, H. Y., “Digital human body model for seat comfort simulation,” Int. J. Automot. Technol. 11 (2), 239–244 (2010).Google Scholar
25.Langenderfer, J. E., Rullkoetter, P. J., Mell, A. G. and Laz, P. J., “A multi-subject evaluation of uncertainty in anatomical landmark location on shoulder kinematic description,” Comput. Methods Biomech. Biomed. Eng. 12 (2), 211–216 (2009).Google Scholar
26.Laz, P. J. and Browne, M., “A review of probabilistic analysis in orthopaedic biomechanics,” Eng. Med. 224 (8), 927–943 (2009).CrossRefGoogle Scholar
27.Li, K. and Zhang, X., “A probabilistic finger biodynamic model better depicts the roles of the flexors during unloaded flexion,” J. Biomech. 43 (13), 2618–2624 (2010).Google Scholar
28.McDowell, M. A., Fryar, C. D., Ogden, C. L. and Flegal, K. M., “Anthropometric reference data for children and adults: United States, 2003–2006,” National Health Statistics Report 10 (2008).Google Scholar
29.Morton, N. A., Maletsky, L. P., Pal, S. and Laz, P. J., “Effect of variability in anatomical landmark location on knee kinematic description,” J. Orthop. Res. 25, 1221–1230 (2007).Google Scholar
30.Nelson, E. S., Lewandowski, B., Licata, A., Myers, J. G., Licata, A. and Myers, J. G., “Development and validation of a predictive bone fracture risk model for astronauts,” Ann. Biomed. Eng. 37 (11), 2337–2359 (2009).Google Scholar
31.Nicolella, D. P., Thacker, B. H., Katoozian, H. and Davy, D. T., “The effect of three-dimensional shape optimization on the probabilistic response of a cemented femoral hip prosthesis,” J. Biomech. 39 (7), 1265–1278 (2006).Google Scholar
32.Pal, S., Haider, H., Laz, P. J., Knihgt, L. A. and Rullkoetter, P. J., “Probabilistic computational modeling of total knee replacement wear,” Wear 264 (7–8), 701–707 (2008).Google Scholar
33.Parkinson, M. B. and Reed, M. P., “Standing Reach Envelopes Incorporating Anthropometric Variance and Postural Cost,” Digital Human Modeling for Design and Engineering Conference and Exhibition, Seattle, WA (2007).Google Scholar
34.Parkinson, M. B. and Reed, M. P., “Creating virtual user populations by analysis of anthropometric data,” Int. J. Ind. Ergon. 40 (1), 106–111 (2010).Google Scholar
35.Rajamani, K. T., Styner, M. A., Talib, H., Zheng, G., Nolte, L. P. and Ballester, G. M. A., “Statistical deformable bone models for robust 3D surface extrapolation from sparse data,” Med. Image Anal. 11 (2), 99–109 (2007).Google Scholar
36.Rao, G., Amarantini, D., Berton, E. and Favier, D., “Influence of body segments' parameters estimation models on inverse dynamics solutions during gait,” J. Biomech. 39 (8), 1531–1536 (2006).Google Scholar
37.Reed, M. P., Parkinson, M. B. and Chaffin, D. B., “A New Approach to Modeling Driver Reach,” 2003 SAE World Congress, Detroit, MI (2003).Google Scholar
38.Reinbolt, J. A., Haftka, R. T., Chmielewski, T. L. and Fregly, B. J., “Are patient-specific joint and inertial parameters necessary for accurate inverse dynamics analyses of gait?,” IEEE Trans. Biomed. Eng. 54 (5), 782–793 (2007).Google Scholar
39.Rohlmann, A., Boustani, H., Bergmann, G. and Zander, T., “Effect of pedicle-screw-based motion preservation system on lumbar spine biomechanics: A probabilistic finite element study with subsequent sensitivity analysis,” J. Biomech. 43 (15), 2963–2969 (2010).Google Scholar
40.Santos, V. J. and Valero-Cuevas, F. J., “Reported anatomical variability naturally leads to multimodal distributions of Denavit–Hartenberg parameters for the human thumb,” IEEE Trans. Biomed. Eng. 53 (2), 155–163 (2006).Google Scholar
41.Thompson, S., Horiuchi, T. and Kagami, S., “A Probabilistic Model of Human Motion and Navigation Intent for Mobile Robot Path Planning,” Proceedings of the 4th International Conference on Autonomous Robots and Agents, Wellington, New Zealand (2009).Google Scholar
42.Valero-Cuevas, F. J., Johanson, M. E. and Towles, J. D., “Towards a realistic biomechanical model of the thumb: the choice of kinematic description may be more critical than the solution method or the variability/uncertainty in musculoskeletal parameters,” J. Biomech. 36, 1019–1030 (2003).CrossRefGoogle ScholarPubMed
43.Yang, J., Marler, R., Kim, H., Arora, J., and Abdel-Malek, K., “Multi-Objective Optimization for Upper Body Posture Prediction,” Proceedings of the 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY (2004).Google Scholar
44.Zhang, L., Wang, Z., Chen, J., Zhou, W. and Zhang, S., “Probabilistic fatigue analysis of all-ceramic crowns based on the finite element method,” J. Biomech. 43 (12), 2321–2326 (2010).Google Scholar