Skip to main content
SpringerLink
Log in

Optimal design of active-passive shoulder exoskeletons: a computational modeling of human-robot interaction

  • Published:
Multibody System Dynamics Aims and scope Submit manuscript

Abstract

Exoskeleton robots, which range from fully passive to fully active-assisted movements, have become an essential instrument for assisting industrial employees and stroke rehabilitation therapy. Designing the exoskeleton actuation is challenging and time-consuming due to closed kinematic loops in the 3D human-exoskeleton multibody model, complicated interactions, and interdependent selection of power transmission features. This research proposes a process for dynamic syntheses of passive and active assistive shoulder exoskeletons. First, a multibody model was developed using six components: an upper-body musculoskeletal model, an optimal controller, the exoskeleton’s rigid body, a passive mechanism, a powered actuator, and an assistance model. The desired motion was experimentally measured from six tasks: frontal reaching, left to right reaching, overhead reaching, sagittal-plane object handling, frontal-plane object handling, and over-head object handling. The system design was optimized by choosing features of the passive mechanism and exoskeleton motor such that the human joint active torque, power, muscle metabolic energy expenditure, and actuator electricity consumption were minimized. The dynamic synthesis processes were found to be successful, and the resultant optimized active-passive exoskeletons allow for the creation of lighter and smaller wearable robots that reduce the user’s muscular activation torque for the tasks being studied.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Fischer, S.L., Koltun, S., Lee, J.: A cross-sectional survey of musculoskeletal disorder hazard exposures and self-reported discomfort among on-shore wind turbine service technicians. Ergonomics 64(3), 383–395 (2021). https://doi.org/10.1080/00140139.2020.1831079

    Article  Google Scholar 

  2. Khansa, I., Khansa, L., Westvik, T.S., Ahmad, J., Lista, F., Janis, J.E.: Reply: work-related musculoskeletal injuries in plastic surgeons in the United States, Canada, and Norway. Plast. Reconstr. Surg. 141(1), 165e–175e (2018). https://doi.org/10.1097/PRS.0000000000004956

    Article  Google Scholar 

  3. Nazari, G., MacDermid, J., Cramm, H.: Prevalence of musculoskeletal disorders among Canadian firefighters: a systematic review and meta-analysis. J. Mil. Veteran Fam. Health 6(1), 83–97 (2020). https://doi.org/10.3138/jmvfh-2019-0024

    Article  Google Scholar 

  4. Van Rijn, R.M., Huisstede, B.M., Koes, B.W., Burdorf, A.: Associations between work-related factors and specific disorders of the shoulder - a systematic review of the literature. Scand. J. Work, Environ. Health 36(3), 189–201 (2010). https://doi.org/10.5271/sjweh.2895

    Article  Google Scholar 

  5. Schopflocher, D., Taenzer, P., Jovey, R.: The prevalence of chronic pain in Canada. Pain Res. Manag. 16(6), 445–450 (2011). https://doi.org/10.1155/2011/876306

    Article  Google Scholar 

  6. Linaker, C.H., Walker-Bone, K.: Shoulder disorders and occupation. Best Pract. Res. Clin. Rheumatol. 29(3), 405–423 (2015). https://doi.org/10.1016/j.berh.2015.04.001

    Article  Google Scholar 

  7. Zhou, X., Zheng, L.: Model-based comparison of passive and active assistance designs in an occupational upper limb exoskeleton for overhead lifting. IISE Trans. Occup. Ergon. Hum. Factors 9, 167–185 (2021). https://doi.org/10.1080/24725838.2021.1954565

    Article  Google Scholar 

  8. Grieve, J.R., Dickerson, C.R.: Overhead work: identification of evidence-based exposure guidelines. Occup. Ergon. 8(1), 53–66 (2008). https://doi.org/10.3233/oer-2008-8105

    Article  Google Scholar 

  9. Wang, Z., Wu, X., Zhang, Y., Chen, C., Liu, S., Liu, Y., Peng, A., Ma, Y.: A semi-active exoskeleton based on EMGs reduces muscle fatigue when squatting. Front. Neurorobot. 15, 30 (2021). https://doi.org/10.3389/fnbot.2021.625479

    Article  Google Scholar 

  10. Dunning, K.K., Davis, K.G., Cook, C., Kotowski, S.E., Hamrick, C., Jewell, G., Lockey, J.: Costs by industry and diagnosis among musculoskeletal claims in a state workers compensation system: 1999-2004. Am. J. Ind. Med. 53(3), 276–284 (2010). https://doi.org/10.1002/ajim.20774

    Article  Google Scholar 

  11. Blanchet, L., Achiche, S., Docquier, Q., Fisette, P., Raison, M.: A procedure to optimize the geometric and dynamic designs of assistive upper limb exoskeletons. Multibody Syst. Dyn. 51(2), 221–245 (2020). https://doi.org/10.1007/s11044-020-09766-6

    Article  MathSciNet  MATH  Google Scholar 

  12. de Looze, M.P., Bosch, T., Krause, F., Stadler, K.S., O’Sullivan, L.W.: Exoskeletons for industrial application and their potential effects on physical work load. Ergonomics 59(5), 671–681 (2016). https://doi.org/10.1080/00140139.2015.1081988

    Article  Google Scholar 

  13. McFarland, T., Fischer, S.: Considerations for industrial use: a systematic review of the impact of active and passive upper limb exoskeletons on physical exposures. IISE Trans. Occup. Ergon. Hum. Factors 7(3–4), 322–347 (2019). https://doi.org/10.1080/24725838.2019.1684399

    Article  Google Scholar 

  14. Veale, A.J., Xie, S.Q.: Towards compliant and wearable robotic orthoses: a review of current and emerging actuator technologies. Med. Eng. Phys. 38(4), 317–325 (2016). https://doi.org/10.1016/j.medengphy.2016.01.010

    Article  Google Scholar 

  15. Nguiadem, C., Raison, M., Achiche, S.: Motion planning of upper-limb exoskeleton robots: a review. Appl. Sci. 10(21), 1–21 (2020). https://doi.org/10.3390/app10217626

    Article  Google Scholar 

  16. Bougrinat, Y., Achiche, S., Raison, M.: Design and development of a lightweight ankle exoskeleton for human walking augmentation. Mechatronics 64, 102297 (2019). https://doi.org/10.1016/j.mechatronics.2019.102297

    Article  Google Scholar 

  17. Miakovic, L., Dezman, M., Petric, T.: Modular quasi-passive mechanism for energy storage applications: towards lightweight high-performance exoskeleton. In: Proceedings of the 20th International Conference on Advanced Robotics, IEEE, Ljubljana, Slovenia, pp. 588–593 (2022). https://doi.org/10.1109/icar53236.2021.9659353

    Chapter  Google Scholar 

  18. Winter, A., Mohajer, N., Nahavandi, D.: Semi-active assistive exoskeleton system for elbow joint. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Melbourne, Australia, pp. 2347–2353 (2022). https://doi.org/10.1109/smc52423.2021.9658720

    Chapter  Google Scholar 

  19. Nasr, A., Ferguson, S., McPhee, J.: Model-based design and optimization of passive shoulder exoskeletons. J. Comput. Nonlinear Dyn. 17(5), 051004 (2022). https://doi.org/10.1115/1.4053405

    Article  Google Scholar 

  20. Rahman, T., Sample, W., Seliktar, R., Scavina, M.T., Clark, A.L., Moran, K., Alexander, M.A.: Design and testing of a functional arm orthosis in patients with neuromuscular diseases. IEEE Trans. Neural Syst. Rehabil. Eng. 15(2), 244–251 (2007). https://doi.org/10.1109/TNSRE.2007.897026

    Article  Google Scholar 

  21. Pehlivan, A.U., Celik, O., O’Malley, M.K.: Mechanical design of a distal arm exoskeleton for stroke and spinal cord injury rehabilitation. In: Proceedings of the IEEE International Conference on Rehabilitation Robotics, IEEE, Zurich, Switzerland, pp. 1–5 (2011). https://doi.org/10.1109/ICORR.2011.5975428

    Chapter  Google Scholar 

  22. Di Natali, C., Sadeghi, A., Mondini, A., Bottenberg, E., Hartigan, B., De Eyto, A., O’Sullivan, L.W., Rocon, E., Stadler, K.S., Mazzolai, B., Caldwell, D.G., Ortiz, J.: Pneumatic quasi-passive actuation for soft assistive lower limbs exoskeleton. Front. Neurorobot. 14, 31 (2020). https://doi.org/10.3389/fnbot.2020.00031

    Article  Google Scholar 

  23. Otten, A., Voort, C., Stienen, A.H., Aarts, R., Van Asseldonk, E., Van Der Kooij, H.: LIMPACT: a hydraulically powered self-aligning upper limb exoskeleton. IEEE/ASME Trans. Mechatron. 20(5), 2285–2298 (2015). https://doi.org/10.1109/TMECH.2014.2375272

    Article  Google Scholar 

  24. Altenburger, R., Scherly, D., Stadler, K.S.: Design of a passive, iso-elastic upper limb exoskeleton for gravity compensation. Robomech J. 3(1), 1–7 (2016). https://doi.org/10.1186/s40648-016-0051-5

    Article  Google Scholar 

  25. de Vries, A.W., de Looze, M.P.: The effect of arm support exoskeletons in realistic work activities: a review study. J. Ergon. 9(4), 1–9 (2019). https://doi.org/10.35248/2165-7556.19.9.255

    Article  Google Scholar 

  26. Bogue, R.: Exoskeletons – a review of industrial applications. Ind. Robot 45(5), 585–590 (2018). https://doi.org/10.1108/IR-05-2018-0109

    Article  Google Scholar 

  27. Shank, T., Eppes, M., Hossain, J., Gunn, M., Rahman, T.: Outcome measures with COPM of children using a Wilmington robotic exoskeleton. Open J. Occup. Ther. 5(1), 3 (2017). https://doi.org/10.15453/2168-6408.1262

    Article  Google Scholar 

  28. Luque, E.P., Högberg, D., Iriondo, A., Thorvald, P.: Evaluation of the use of exoskeletons in the range of motion of workers. PhD thesis, University of Skovde, Skovde (2019). https://doi.org/10.1080/10255840008908000

  29. Gopura, R.A., Bandara, D.S., Kiguchi, K., Mann, G.K.: Developments in hardware systems of active upper-limb exoskeleton robots: a review. Robot. Auton. Syst. 75, 203–220 (2016). https://doi.org/10.1016/j.robot.2015.10.001

    Article  Google Scholar 

  30. Nasr, A., Laschowski, B., McPhee, J.: Myoelectric control of robotic leg prostheses and exoskeletons: a review. In: Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference and Computers and Information in Engineering Conference, ASME, Online, Virtual, vol. 85444, pp. 2021–69203 (2021). https://doi.org/10.1115/DETC2021-69203

    Chapter  Google Scholar 

  31. Jamsek, M., Petric, T., Jan, B.: Gaussian mixture models for control of quasi-passive spinal exoskeletons. Sensors 20(9), 2705 (2020). https://doi.org/10.3390/s20092705

    Article  Google Scholar 

  32. Nasr, A., Hashemi, A., McPhee, J.: Model-based mid-level regulation for assist-as-needed hierarchical control of wearable robots: a computational study of human-robot adaptation. Robotics 11(1), 20 (2022). https://doi.org/10.3390/robotics11010020

    Article  Google Scholar 

  33. du Plessis, T., Djouani, K., Oosthuizen, C.: A review of active hand exoskeletons for rehabilitation and assistance. Robotics 10(1), 5351–5356 (2021). https://doi.org/10.3390/robotics10010040

    Article  Google Scholar 

  34. Sajadi, M.R., Nasr, A., Moosavian, S.A.A., Zohoor, H.: Mechanical design, fabrication, kinematics and dynamics modeling, multiple impedance control of a wrist rehabilitation robot. In: Proceedings of the International Conference on Robotics and Mechatronics, IEEE, Tehran, Iran, pp. 290–295 (2015). https://doi.org/10.1109/ICRoM.2015.7367799

    Chapter  Google Scholar 

  35. Gaudet, G., Raison, M., Achiche, S.: Current trends and challenges in pediatric access to sensorless and sensor-based upper limb exoskeletons. Sensors 21(10), 3561 (2021). https://doi.org/10.3390/s21103561

    Article  Google Scholar 

  36. Kashiri, N., Abate, A., Abram, S.J., Albu-Schaffer, A., Clary, P.J., Daley, M., Faraji, S., Furnemont, R., Garabini, M., Geyer, H., Grabowski, A.M., Hurst, J., Malzahn, J., Mathijssen, G., Remy, D., Roozing, W., Shahbazi, M., Simha, S.N., Song, J.B., Smit-Anseeuw, N., Stramigioli, S., Vanderborght, B., Yesilevskiy, Y., Tsagarakis, N.G.: An overview on principles for energy efficient robot locomotion. Front. Robot. AI 5, 129 (2018). https://doi.org/10.3389/frobt.2018.00129

    Article  Google Scholar 

  37. Al-Hayali, N.K., Nacy, S.M., Chiad, J.S., Hussein, O.: Analysis and evaluation of a quasi-passive lower limb exoskeleton for gait rehabilitation. Al-Khwarizmi Eng. J. 17(4), 36–47 (2021). https://doi.org/10.22153/kej.2021.12.007

    Article  Google Scholar 

  38. Kh Al-Hayali, N., Chiad, J.S., Nacy, S.M., Hussein, O.: A review of passive and quasi-passive lower limb exoskeletons for gait rehabilitation. J. Mech. Eng. Res. Dev. 44(9), 436–447 (2021)

    Google Scholar 

  39. Matthew, R.P., Mica, E.J., Meinhold, W., Loeza, J.A., Tomizuka, M., Bajcsy, R.: Introduction and initial exploration of an active/passive exoskeleton framework for portable assistance. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, IEEE, Hamburg, Germany, vol. 2015–Decem, pp. 5351–5356 (2015). https://doi.org/10.1109/IROS.2015.7354133

    Chapter  Google Scholar 

  40. Naito, J., Nakayama, A., Obinata, G., Hase, K.: Development of a wearable robot for assisting carpentry workers. Int. J. Adv. Robot. Syst. 4(4), 48 (2007). https://doi.org/10.5772/5667

    Article  Google Scholar 

  41. Otten, B.M., Weidner, R., Argubi-Wollesen, A.: Evaluation of a novel active exoskeleton for tasks at or above head level. IEEE Robot. Autom. Lett. 3(3), 2408–2415 (2018). https://doi.org/10.1109/LRA.2018.2812905

    Article  Google Scholar 

  42. Smith, R.L., Lobo-Prat, J., Van Der Kooij, H., Stienen, A.H.: Design of a perfect balance system for active upper-extremity exoskeletons. In: Proceedings of the IEEE International Conference on Rehabilitation Robotics, IEEE, Seattle, WA, USA, pp. 1–6 (2013). https://doi.org/10.1109/ICORR.2013.6650376

    Chapter  Google Scholar 

  43. Laschowski, B., Razavian, R.S., McPhee, J.: Simulation of stand-to-sit biomechanics for robotic exoskeletons and prostheses with energy regeneration. IEEE Trans. Med. Robot. Bionics 3(2), 455–462 (2021). https://doi.org/10.1109/TMRB.2021.3058323

    Article  Google Scholar 

  44. Ren, L., Cong, M., Zhang, W., Tan, Y.: Harvesting the negative work of an active exoskeleton robot to extend its operating duration. Energy Convers. Manag. 245, 114640 (2021). https://doi.org/10.1016/j.enconman.2021.114640

    Article  Google Scholar 

  45. Hassan, Z., Sadik, W.: Design quasi passive exoskeleton for below knee prosthesis. J. Eng. Appl. Sci. 13(23), 8994–9001 (2018)

    Google Scholar 

  46. Lambrecht, B.G., Kazerooni, H.: Design of a semi-active knee prosthesis. In: Proceedings of the IEEE International Conference on Robotics and Automation, IEEE, Kobe, Japan, pp. 639–645 (2009). https://doi.org/10.1109/ROBOT.2009.5152828

    Chapter  Google Scholar 

  47. Pillai, M.V., Van Engelhoven, L., Kazerooni, H.: Evaluation of a lower leg support exoskeleton on floor and below hip height panel work. Hum. Factors 62(3), 489–500 (2020). https://doi.org/10.1177/0018720820907752

    Article  Google Scholar 

  48. Bai, S., Islam, M.R., Hansen, K., Nørgaard, J., Chen, C.Y., Yang, G.: A semi-active upper-body exoskeleton for motion assistance. Biosyst. Biorobot. 27, 301–305 (2022). https://doi.org/10.1007/978-3-030-69547-7_49

    Article  Google Scholar 

  49. Zahedi, A., Wang, Y., Martinez-Hernandez, U., Zhang, D.: A wearable elbow exoskeleton for tremor suppression equipped with rotational semi-active actuator. Mech. Syst. Signal Process. 157, 107674 (2021). https://doi.org/10.1016/j.ymssp.2021.107674

    Article  Google Scholar 

  50. Inkol, K.A., McPhee, J.: Assessing control of fixed-support balance recovery in wearable lower-limb exoskeletons using multibody dynamic modelling. In: Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, IEEE, New York, NY, USA, pp. 54–60 (2020). https://doi.org/10.1109/BioRob49111.2020.9224430

    Chapter  Google Scholar 

  51. Galinski, D., Sapin, J., Dehez, B.: Optimal design of an alignment-free two-DOF rehabilitation robot for the shoulder complex. In: Proceedings of the IEEE International Conference on Rehabilitation Robotics, IEEE, Seattle, Washington, USA, pp. 1–7 (2013). https://doi.org/10.1109/ICORR.2013.6650502

    Chapter  Google Scholar 

  52. Sarac, M., Solazzi, M., Sotgiu, E., Bergamasco, M., Frisoli, A.: Design and kinematic optimization of a novel underactuated robotic hand exoskeleton. Meccanica 52(3), 749–761 (2017). https://doi.org/10.1007/s11012-016-0530-z

    Article  MathSciNet  Google Scholar 

  53. Manns, P., Sreenivasa, M., Millard, M., Mombaur, K.D.: Motion optimization and parameter identification for a human and lower back exoskeleton model. IEEE Robot. Autom. Lett. 2(3), 1564–1570 (2017). https://doi.org/10.1109/LRA.2017.2676355

    Article  Google Scholar 

  54. Shi, Y., Peng, Q.: Improved benchmarking method using kinematics analysis in design of an upper limb exoskeleton rehabilitation device. In: Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, ASME, Quebec City, Quebec, Canada, vol. 4, p. V004T05A001. (2018). https://doi.org/10.1115/DETC2018-85465

    Chapter  Google Scholar 

  55. Hayashi, Y., Dubey, R., Kiguchi, K.: Torque optimization for a 7DOF upper-limb power-assist exoskeleton robot. In: Proceedings of the IEEE Workshop on Robotic Intelligence in Informationally Structured Space, IEEE, Paris, France, pp. 49–54 (2011). https://doi.org/10.1109/RIISS.2011.5945786

    Chapter  Google Scholar 

  56. Marinou, G.D., Mombaur, K.D.: Optimizing active spinal exoskeletons to minimize low back loads. Biosyst. Biorobot. 27, 455–460 (2022). https://doi.org/10.1007/978-3-030-69547-7_73

    Article  Google Scholar 

  57. Aoustin, Y., Formalskii, A.M.: Walking of biped with passive exoskeleton: evaluation of energy consumption. Multibody Syst. Dyn. 43(1), 71–96 (2018). https://doi.org/10.1007/s11044-017-9602-7

    Article  MathSciNet  MATH  Google Scholar 

  58. Harbauer, C.M., Fleischer, M., Bandmann, C.E., Bengler, K.: Optimizing force transfer in a soft exoskeleton using biomechanical modeling. In: Proceedings of the 21st Congress of the International Ergonomics Association, Springer, Vancouver, BC, Canada, vol. 223, pp. 274–281 (2022). https://doi.org/10.1007/978-3-030-74614-8_33

    Chapter  Google Scholar 

  59. Zhou, L., Li, Y., Bai, S.: A human-centered design optimization approach for robotic exoskeletons through biomechanical simulation. Robot. Auton. Syst. 91, 337–347 (2017). https://doi.org/10.1016/j.robot.2016.12.012

    Article  Google Scholar 

  60. Park, D., Cho, K.J.: Development and evaluation of a soft wearable weight support device for reducing muscle fatigue on shoulder. PLoS ONE 12(3), e0173730 (2017). https://doi.org/10.1371/journal.pone.0173730

    Article  Google Scholar 

  61. Mehrabi, N., Razavian, R.S., Ghannadi, B., McPhee, J.: Predictive simulation of reaching moving targets using nonlinear model predictive control. Front. Comput. Neurosci. 10, 143 (2017). https://doi.org/10.3389/fncom.2016.00143

    Article  Google Scholar 

  62. Mallat, R., Khalil, M., Venture, G., Bonnet, V., Mohammed, S.: Human-exoskeleton joint misalignment: a systematic review. In: International Conference on Advances in Biomedical Engineering, ICABME, IEEE, Tripoli, Lebanon, vol. 2019–Octob, pp. 1–4 (2019). https://doi.org/10.1109/ICABME47164.2019.8940321

    Chapter  Google Scholar 

  63. Panero, E., Muscolo, G.G., Gastaldi, L., Pastorelli, S.: Multibody analysis of a 3D human model with trunk exoskeleton for industrial applications. Comput. Methods Appl. Sci. 53, 43–51 (2020). https://doi.org/10.1007/978-3-030-23132-3_6

    Article  Google Scholar 

  64. Cortes, C., De Los Reyes-Guzman, A., Scorza, D., Bertelsen, A., Carrasco, E., Gil-Agudo, A., Ruiz-Salguero, O., Florez, J.: Inverse kinematics for upper limb compound movement estimation in exoskeleton-assisted rehabilitation. BioMed Res. Int. 2016, 1–14 (2016). https://doi.org/10.1155/2016/2581924

    Article  Google Scholar 

  65. Laitenberger, M., Raison, M., Périé, D., Begon, M.: Refinement of the upper limb joint kinematics and dynamics using a subject-specific closed-loop forearm model. Multibody Syst. Dyn. 33(4), 413–438 (2015). https://doi.org/10.1007/s11044-014-9421-z

    Article  MathSciNet  Google Scholar 

  66. Nasr, A., Bell, S., He, J., Whittaker, R.L., Jiang, N., Dickerson, C.R., McPhee, J.: MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables. J. Neural Eng. 18(4), 0460d3 (2021). https://doi.org/10.1088/1741-2552/ac1adc

    Article  Google Scholar 

  67. Nasr, A., McPhee, J.: Biarticular MuscleNET: a machine learning model of biarticular muscles. In: Proceedings of the North American Congress on Biomechanics, Ottawa, Canada (2022)

    Google Scholar 

  68. Alabdulkarim, S., Nussbaum, M.A.: Influences of different exoskeleton designs and tool mass on physical demands and performance in a simulated overhead drilling task. Appl. Ergon. 74, 55–66 (2019). https://doi.org/10.1016/j.apergo.2018.08.004

    Article  Google Scholar 

  69. Grazi, L., Trigili, E., Proface, G., Giovacchini, F., Crea, S., Vitiello, N.: Design and experimental evaluation of a semi-passive upper-limb exoskeleton for workers with motorized tuning of assistance. IEEE Trans. Neural Syst. Rehabil. Eng. 28(10), 2276–2285 (2020). https://doi.org/10.1109/TNSRE.2020.3014408

    Article  Google Scholar 

  70. Kim, S., Nussbaum, M.A.: A follow-up study of the effects of an arm support exoskeleton on physical demands and task performance during simulated overhead work. IISE Trans. Occup. Ergon. Hum. Factors 7(3–4), 163–174 (2019). https://doi.org/10.1080/24725838.2018.1551255

    Article  Google Scholar 

  71. Kim, S., Nussbaum, M.A., Mokhlespour Esfahani, M.I., Alemi, M.M., Jia, B., Rashedi, E.: Assessing the influence of a passive, upper extremity exoskeletal vest for tasks requiring arm elevation: Part II – “Unexpected” effects on shoulder motion, balance, and spine loading. Appl. Ergon. 70, 323–330 (2018). https://doi.org/10.1016/j.apergo.2018.02.024

    Article  Google Scholar 

  72. Ackermann, M., van den Bogert, A.J.: Optimality principles for model-based prediction of human gait. J. Biomech. 43(6), 1055–1060 (2010). https://doi.org/10.1016/j.jbiomech.2009.12.012

    Article  Google Scholar 

  73. Theurel, J., Desbrosses, K., Roux, T., Savescu, A.: Physiological consequences of using an upper limb exoskeleton during manual handling tasks. Appl. Ergon. 67, 211–217 (2018). https://doi.org/10.1016/j.apergo.2017.10.008

    Article  Google Scholar 

  74. Liu, Z.H., Qiu, Y.X., Zhu, Z.H., Zhou, Y.W., Tang, Z.: Effects of memory foam on optimizing shoulder fatigue of wearable exoskeleton. J. Donghua Univ. 33(4), 536–539 (2016)

    Google Scholar 

  75. Liu, S., Hemming, D., Luo, R.B., Reynolds, J., Delong, J.C., Sandler, B.J., Jacobsen, G.R., Horgan, S.: Solving the surgeon ergonomic crisis with surgical exosuit. Surg. Endosc. 32(1), 236–244 (2018). https://doi.org/10.1007/s00464-017-5667-x

    Article  Google Scholar 

  76. Nasr, A., Inkol, K.A., Bell, S., McPhee, J.: InverseMuscleNET: alternative machine learning solution to static optimization and inverse muscle modelling. Front. Comput. Neurosci. 15, 759489 (2021). https://doi.org/10.3389/fncom.2021.759489

    Article  Google Scholar 

  77. Inkol, K.A., Brown, C., McNally, W., Jansen, C., McPhee, J.: Muscle torque generators in multibody dynamic simulations of optimal sports performance. Multibody Syst. Dyn. 50(4), 435–452 (2020). https://doi.org/10.1007/s11044-020-09747-9

    Article  MathSciNet  MATH  Google Scholar 

  78. Ghannadi, B., Mehrabi, N., Razavian, R.S., McPhee, J.: Nonlinear model predictive control of an upper extremity rehabilitation robot using a two-dimensional human-robot interaction model. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, IEEE, Vancouver, BC, Canada, vol. 2017–Septe, pp. 502–507 (2017). https://doi.org/10.1109/IROS.2017.8202200

    Chapter  Google Scholar 

  79. Mombaur, K.D., Clever, D.: Inverse optimal control as a tool to understand human movement. In: Mansard, N., Lasserre, J. (eds.) Geometric and Numerical Foundations of Movements, vol. 117, pp. 163–186. Springer, Berlin (2017). https://doi.org/10.1007/978-3-319-51547-2_8

    Chapter  Google Scholar 

  80. Reinkensmeyer, D.J., Wynne, J.H., Harkema, S.J.: A robotic tool for studying locomotor adaptation and rehabilitation. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology, IEEE, Houston, TX, USA, vol. 3, pp. 2353–2354 (2002). https://doi.org/10.1109/iembs.2002.1053318

    Chapter  Google Scholar 

  81. Dumas, R., Chèze, L., Verriest, J.P.: Adjustments to McConville et al. and Young et al. body segment inertial parameters. J. Biomech. 40(3), 543–553 (2007). https://doi.org/10.1016/j.jbiomech.2006.02.013

    Article  Google Scholar 

  82. Yamaguchi, G.T.: Dynamic Modeling of Musculoskeletal Motion: A Vectorized Approach for Biomechanical Analysis in Three Dimensions, 1st edn. Springer, Boston (2006). https://doi.org/10.1007/978-0-387-28750-8

    Book  MATH  Google Scholar 

  83. Angold, R., Lubin, J., Solano, M., Paretich, C., Mastaler, T.: Exoskeleton and Method of Providing an Assistive Torque to an Arm of a Wearer (2016)

    Google Scholar 

  84. Gopura, R.A., Kiguchi, K., Bandara, D.S.: A brief review on upper extremity robotic exoskeleton systems. In: Proceedings of the 6th International Conference on Industrial and Information Systems, IEEE, Kandy, Sri Lanka, pp. 346–351 (2011). https://doi.org/10.1109/ICIINFS.2011.6038092

    Chapter  Google Scholar 

  85. Hunter, I., Ballantyne, J., Hollerbach, J.M.: A comparative analysis of actuator technologies for robotics. Robot. Rev. 2, 299–342 (1991)

    Google Scholar 

  86. García, P.L., Crispel, S., Saerens, E., Verstraten, T., Lefeber, D.: Compact gearboxes for modern robotics: a review. Front. Robot. AI 7, 103 (2020). https://doi.org/10.3389/frobt.2020.00103

    Article  Google Scholar 

  87. Seok, S., Wang, A., Chuah, M.Y., Hyun, D.J., Lee, J., Otten, D.M., Lang, J.H., Kim, S.: Design principles for energy-efficient legged locomotion and implementation on the MIT Cheetah robot. IEEE/ASME Trans. Mechatron. 20(3), 1117–1129 (2015). https://doi.org/10.1109/TMECH.2014.2339013

    Article  Google Scholar 

  88. Wensing, P.M., Wang, A., Seok, S., Otten, D.M., Lang, J., Kim, S.: Proprioceptive actuator design in the MIT cheetah: impact mitigation and high-bandwidth physical interaction for dynamic legged robots. IEEE Trans. Robot. 33(3), 509–522 (2017). https://doi.org/10.1109/TRO.2016.2640183

    Article  Google Scholar 

  89. Hughes, A., Drury, B.: Electric Motors and Drives: Fundamentals, Types and Applications. Elsevier, Amsterdam (2019). https://doi.org/10.1016/B978-0-08-102615-1.09989-X

    Book  Google Scholar 

  90. Inkol, K.A., McPhee, J.: Towards compliant human-exoskeleton interactions within multibody dynamics simulations of assisted human motor control. In: ECCOMAS Thematic Conference on Multibody Dynamics, Budapest, Hungary (2021)

    Google Scholar 

  91. Jarrasse, N., Morel, G.: Connecting a human limb to an exoskeleton. IEEE Trans. Robot. 28(3), 697–709 (2012). https://doi.org/10.1109/TRO.2011.2178151

    Article  Google Scholar 

  92. Stienen, A.H., Hekman, E.E., van der Helm, F.C., van der Kooij, H.: Self-aligning exoskeleton axes through decoupling of joint rotations and translations. IEEE Trans. Robot. 25(3), 628–633 (2009). https://doi.org/10.1109/TRO.2009.2019147

    Article  Google Scholar 

  93. Gull, M.A., Bai, S., Bak, T.: A review on design of upper limb exoskeletons. Robotics 9(1), 16 (2020). https://doi.org/10.3390/robotics9010016

    Article  Google Scholar 

  94. Anderson, D.E., Madigan, M.L., Nussbaum, M.A.: Maximum voluntary joint torque as a function of joint angle and angular velocity: model development and application to the lower limb. J. Biomech. 40(14), 3105–3113 (2007). https://doi.org/10.1016/j.jbiomech.2007.03.022

    Article  Google Scholar 

  95. Kim, J.H., Roberts, D.: A joint-space numerical model of metabolic energy expenditure for human multibody dynamic system. Int. J. Numer. Methods Biomed. Eng. 31(9), e02721 (2015). https://doi.org/10.1002/cnm.2721

    Article  MathSciNet  Google Scholar 

  96. Laschowski, B., McPhee, J., Andrysek, J.: Lower-limb prostheses and exoskeletons with energy regeneration: mechatronic design and optimization review. J. Mech. Robot. 11(4), 040801 (2019). https://doi.org/10.1115/1.4043460

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by funding from the Canada Research Chairs Program and the Natural Sciences and Engineering Research Council of Canada. The authors wish to thank Ekso Bionics Holdings Inc. for providing the Ekso EVO passive shoulder exoskeleton.

Author information

Authors and Affiliations

  1. Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada

    Ali Nasr, Sydney Bell & John McPhee

Authors
  1. Ali Nasr
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sydney Bell
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John McPhee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Nasr.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nasr, A., Bell, S. & McPhee, J. Optimal design of active-passive shoulder exoskeletons: a computational modeling of human-robot interaction. Multibody Syst Dyn 57, 73–106 (2023). https://doi.org/10.1007/s11044-022-09855-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11044-022-09855-8

Keywords

Search

Navigation