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Joint fatigue-based optimal posture prediction for maximizing endurance time in box carrying task

Abstract

In this study, the three-compartment controller fatigue model is integrated with an inverse dynamics optimization routine to predict the optimal posture, joint fatigue, and endurance time for a box carrying task. The two-dimensional human model employed has 10 degrees of freedom. For the box carrying task, the feet are fixed on the ground, and the hand location and box weight are given. In the joint fatigue-based posture prediction formulation, the design variables are joint angles, three-compartment control values, and total box carrying duration (endurance time). The objective is to maximize the total time subject to task and fatigue constraints, including compartment unity constraint, residual capacity constraint, and a novel coupled failure constraint. The optimization successfully predicts the optimal posture, joint torque, endurance time, joint fatigue progression, and joint failure conditions. The proposed novel joint fatigue-based formulation predicts the optimal posture for maximizing the endurance time with a given box weight for a box-carrying task. Finally, the simulation is computationally efficient, and the optimal results are achieved in about 5 seconds CPU time on a regular computer.

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References

  1. Abbiss, C.R., Laursen, P.B.: Models to explain fatigue during prolonged endurance cycling. Sports Med. 35(10), 865–898 (2005)

    Article  Google Scholar 

  2. Barman, S., Xiang, Y.: Recursive Newton-Euler dynamics and sensitivity analysis for robot manipulator with revolute joints. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Aug. 17-19, Virtual, Online (2020). https://doi.org/10.1115/DETC2020-22646

    Chapter  Google Scholar 

  3. Barry, B.K., Enoka, R.M.: The neurobiology of muscle fatigue: 15 years later. Integr. Comp. Biol. 47(4), 465–473 (2007)

    Article  Google Scholar 

  4. Cahalan, T., Johnson, M., Liu, S., Chao, E.: Quantitative measurements of hip strength in different age-groups. Clin. Orthop. Relat. Res. R(246), 136–145 (1989)

    Google Scholar 

  5. Charney, W., Zimmerman, K., Walara, E.: The lifting team. A design method to reduce lost time back injury in nursing. AAOHN J. 39(5), 231–234 (1991)

    Article  Google Scholar 

  6. Cheng, H., Obergefell, L., Rizer, A.: Generator of body (GEBOD) manual. AL/CF-TR-1994-0051, Armstrong Laboratory, Air Force Materiel Command. Wright-Patterson Air Force Base, Ohio (1994)

  7. Daynard, D., Yassi, A., Cooper, J., Tate, R., Norman, R., Wells, R.: Biomechanical analysis of peak and cumulative spinal loads during simulated patient-handling activities: a substudy of a randomized controlled trial to prevent lift and transfer injury of health care workers. Appl. Ergon. 32(3), 199–214 (2001)

    Article  Google Scholar 

  8. Fitts, R.H.: Cellular mechanisms of muscle fatigue. Physiol. Rev. 74(1), 49–94 (1994)

    Article  Google Scholar 

  9. Frey-Law, L.A., Looft, J.M., Heitsman, J.: A three-compartment muscle fatigue model accurately predicts joint-specific maximum endurance times for sustained isometric tasks. J. Biomech. 45(10), 1803–1808 (2012)

    Article  Google Scholar 

  10. Fu, K.S., Gonzalez, R., Lee, C.: Robotics: Control, Sensing, Vision, and Intelligence. McGraw-Hill, New York (1987)

    Google Scholar 

  11. Giat, Y., Mizrahi, J., Levy, M.: A musculotendon model of the fatigue profiles of paralyzed quadriceps muscle under FES. IEEE Trans. Biomed. Eng. 40(7), 664–674 (1993)

    Article  Google Scholar 

  12. Gill, P.E., Murray, W., Saunders, M.A.: SNOPT: an SQP algorithm for large-scale constrained optimization. J. Soc. Ind. Appl. Math. 47(1), 99–131 (2005)

    MathSciNet  MATH  Google Scholar 

  13. Hawkins, D., Hull, M.L.: Muscle force as affected by fatigue: mathematical model and experimental verification. J. Biomech. 26(9), 1117–1128 (1993)

    Article  Google Scholar 

  14. Howard, B., Cloutier, A., Yang, J.: Physics-based seated posture prediction for pregnant women and validation considering ground and seat pan contacts. J. Biomech. Eng. 134(7), 071004 (2012)

    Article  Google Scholar 

  15. Kaminski, T., Perrin, D., Gansneder, B.: Eversion strength analysis of uninjured and functionally unstable ankles. J. Athl. Train. 34(3), 239–245 (1999)

    Google Scholar 

  16. Kumar, S.: Isolated planar trunk strengths measurement in normals. 3. Results and database. Int. J. Ind. Ergon. 17(2), 103–111 (1996)

    Article  Google Scholar 

  17. Kwon, H.J., Chung, H.J., Xiang, Y.: Human gait prediction with a high dof upper body: a multi-objective optimization of discomfort and energy cost. Int. J. Humanoid Robot. 14(1), 1650025 (2017)

    Article  Google Scholar 

  18. Ma, L., Chablat, D., Bennis, F., Zhang, W.: A new simple dynamic muscle fatigue model and its validation. Int. J. Ind. Ergon. 39(1), 211–220 (2009)

    Article  Google Scholar 

  19. Marler, R.T., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Struct. Multidiscip. Optim. 26(6), 369–395 (2004)

    MathSciNet  Article  Google Scholar 

  20. Marras, W.S., Davis, K.G., Kirking, B.C., Granata, K.P.: Spine loading and trunk kinematics during team lifting. Ergonomics 42(10), 1258–1273 (1999)

    Article  Google Scholar 

  21. Pereira, A.F., Silva, M.T., Martins, J.M., de Carvalho, M.: Implementation of an efficient muscle fatigue model in the framework of multibody systems dynamics for analysis of human movements. J. Multi-Body Dyn. 225(4), 359–370 (2011)

    Google Scholar 

  22. Peternel, L., Fang, C., Tsagarakis, N., Ajoudani, A.: A selective muscle fatigue management approach to ergonomic human-robot co-manipulation. Robot. Comput.-Integr. Manuf. 58, 69–79 (2019)

    Article  Google Scholar 

  23. Reed, M.P., Manary, M.A., Flannagan, C.A.C., Schneider, L.W.: A statistical method for predicting automobile driving posture. Hum. Factors 44(4), 557–568 (2002)

    Article  Google Scholar 

  24. Rohmert, W.: Determination of recovery pause for static work of man. Int. Z. Angew. Physiol. Einschl. Arbeitsphysiol. 18, 123–164 (1960)

    Google Scholar 

  25. Song, J., Qu, X., Chen, C.-H.: Simulation of lifting motions using a novel multi-objective optimization approach. Int. J. Ind. Ergon. 53(100), 37–47 (2016)

    Article  Google Scholar 

  26. Wang, X.: A behavior-based inverse kinematics algorithm to predict arm prehension postures for computer-aided ergonomic evaluation. J. Biomech. 32(5), 453–460 (1999)

    Article  Google Scholar 

  27. Xia, T., Frey Law, L.A.: A theoretical approach for modeling peripheral muscle fatigue and recovery. J. Biomech. 41(14), 3046–3052 (2008)

    Article  Google Scholar 

  28. Xiang, Y., Arefeen, A.: Two-dimensional team lifting prediction with floating-base box dynamics and grasping force coupling. Multibody Syst. Dyn. 50(2), 211–231 (2020)

    MathSciNet  Article  Google Scholar 

  29. Xiang, Y., Arora, J., Abdel-Malek, K.: Optimization-based motion prediction of mechanical systems: sensitivity analysis. Struct. Multidiscip. Optim. 37, 595–608 (2009)

    MathSciNet  Article  Google Scholar 

  30. Xiang, Y., Arora, J., Rahmatalla, S., Abdel-Malek, K.: Optimization-based dynamic human walking prediction: one step formulation. Int. J. Numer. Methods Eng. 76(6), 667–695 (2009)

    Article  Google Scholar 

  31. Xiang, Y., Arora, J., Rahmatalla, S., Marler, R., Bhatt, R., Abdel-Malek, K.: Human lifting simulation using a multi-objective optimization approach. Multibody Syst. Dyn. 23, 431–451 (2010)

    MathSciNet  Article  Google Scholar 

  32. Xiang, Y., Arora, J., chung, H., Kwon, H., Rahmatalla, S., Bhatt, R., Abdel-Malek, K.: Predictive simulation of human walking transitions using an optimization formulation. Struct. Multidiscip. Optim. 45(5), 759–772 (2012)

    MathSciNet  Article  Google Scholar 

  33. Yang, J., Marler, R.T., Kim, H., Arora, J., Abdel-Malek, K.: Multi-objective optimization for upper body posture prediction. In: 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Aug. 30 - Sep. 1, Albany, New York (2004). https://doi.org/10.2514/6.2004-4506.

    Chapter  Google Scholar 

  34. Yang, J., Marler, T., Rahmatalla, S.: Multi-objective optimization-based method for kinematic posture prediction: development and validation. Robotica 29(2), 245–253 (2011)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Science Foundation (CBET 2014281 and 2014278).

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Correspondence to Yujiang Xiang.

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Barman, S., Xiang, Y., Rakshit, R. et al. Joint fatigue-based optimal posture prediction for maximizing endurance time in box carrying task. Multibody Syst Dyn 55, 323–339 (2022). https://doi.org/10.1007/s11044-022-09832-1

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  • DOI: https://doi.org/10.1007/s11044-022-09832-1

Keywords

  • Fatigue prediction
  • Endurance time prediction
  • Posture prediction
  • Box carrying
  • Three-compartment controller fatigue model