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Configuration design and assisting effect evaluation of a novel double-support closed-chain unpowered load-carrying exoskeleton based on variable pivot gait

Published online by Cambridge University Press:  07 October 2025

Cunjin Ai Open the ORCID record for Cunjin Ai [Opens in a new window] ,
Jianjun Zhang ,
Jun Wei Open the ORCID record for Jun Wei [Opens in a new window] ,
Jingke Song Open the ORCID record for Jingke Song [Opens in a new window] ,
Chenglei Liu  and
Anyu Shi
Cunjin Ai
Affiliation:
School of Mechanical Engineering, Hebei University of Technology , Tianjin, 300401, China School of Intelligent Manufacturing Engineering/Automotive Engineering, Chongqing University of Arts and Sciences, Chongqing, 402160, China Intelligent Rehabilitation Device and Detection Technology Engineering Research Center of the Ministry of Education, Tianjin, 300401, China
Jianjun Zhang*
Affiliation:
School of Mechanical Engineering, Hebei University of Technology , Tianjin, 300401, China Intelligent Rehabilitation Device and Detection Technology Engineering Research Center of the Ministry of Education, Tianjin, 300401, China Hebei Provincial Key Laboratory of Robot Perception and Human–Machine Fusion, Tianjin, 300401, China
Jun Wei*
Affiliation:
School of Mechanical Engineering, Hebei University of Technology , Tianjin, 300401, China Intelligent Rehabilitation Device and Detection Technology Engineering Research Center of the Ministry of Education, Tianjin, 300401, China Hebei Provincial Key Laboratory of Robot Perception and Human–Machine Fusion, Tianjin, 300401, China
Jingke Song
Affiliation:
School of Mechanical Engineering, Hebei University of Technology , Tianjin, 300401, China Intelligent Rehabilitation Device and Detection Technology Engineering Research Center of the Ministry of Education, Tianjin, 300401, China Hebei Provincial Key Laboratory of Robot Perception and Human–Machine Fusion, Tianjin, 300401, China
Chenglei Liu
Affiliation:
School of Mechanical Engineering, Hebei University of Technology , Tianjin, 300401, China Intelligent Rehabilitation Device and Detection Technology Engineering Research Center of the Ministry of Education, Tianjin, 300401, China Hebei Provincial Key Laboratory of Robot Perception and Human–Machine Fusion, Tianjin, 300401, China
Anyu Shi
Affiliation:
School of Mechanical Engineering, Hebei University of Technology , Tianjin, 300401, China Intelligent Rehabilitation Device and Detection Technology Engineering Research Center of the Ministry of Education, Tianjin, 300401, China Hebei Provincial Key Laboratory of Robot Perception and Human–Machine Fusion, Tianjin, 300401, China
*
Corresponding authors: Jianjun Zhang; Email: zhjjun@hebut.edu.cn, Jun Wei; Email: jun.wei@hebut.edu.cn
Corresponding authors: Jianjun Zhang; Email: zhjjun@hebut.edu.cn, Jun Wei; Email: jun.wei@hebut.edu.cn
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Abstract

Based on the characteristics of the variable pivot gait during the human load-carrying, this paper proposes a double-leg coordination assistance principle for load-carrying: assisting support of the guiding leg at the heel-pivot stage by the spring to reduce the collision, which can reduce the ankle moment of the following leg that is performing the push-off at the toe-pivot stage. A novel unpowered load-carrying exoskeleton (ULE) with a double-support closed-chain configuration is designed, and the theoretical verification is carried out. Five subjects participate in the load-carrying and metabolic cost experiments for assisting and energy-saving effect evaluation, and the angle and moment of human joints, plantar pressure, spring compression and human net metabolic rate are analyzed. Compared with carrying load by the human alone, wearing the novel ULE with spring reduces the human peak ankle moment performing the push-off by up to 11.9 ± 1.6% (Mean±SE, 10 kg), average ankle moment over the support phase by up to 36.8 ± 9.1% (Mean±SE, 5 kg) and the average vertical plantar pressure by up to 8.1 ± 1%% (Mean±SE, 15 kg). Meanwhile, wearing the novel ULE reduces the human net metabolic rate by 5.6 ± 0.5% (Mean±SE, 10 kg), 4.1 ± 0.7% (Mean±SE, 15 kg) and 5.9 ± 1.6% (Mean±SE, 20 kg). The results show that the novel ULE can provide support and joint moment assistance over the whole support phase while reducing human net metabolic rate. This study can also be applied to the powered load-carrying exoskeleton, providing a new avenue.

Keywords

variable pivot gaitunpowered exoskeletondouble-support closed-chainload-carrying

Information

Type
Research Article
Information
Robotica , Volume 43 , Issue 10 , October 2025 , pp. 3657 - 3681
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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References

Liew, B., Morris, S. and Netto, K., “The effect of backpack carriage on the biomechanics of walking: A systematic review and preliminary meta-analysis,” J. Appl. Biomech. 32(6), 614–629 (2016).CrossRefGoogle ScholarPubMed
Qu, X. D. and Yeo, J. C., “Effects of load carriage and fatigue on gait characteristics,” J. Biomech. 44(7), 1259–1263 (2011).CrossRefGoogle ScholarPubMed
Vickery-Howe, D. M., Drain, J. R., Clarke, A. C., Dascombe, B. J., McWilliam, J. T. and Middleton, K. J., “Treadmill load carriage overestimates energy expenditure of overground load carriage,” Ergonomics 64(4), 521–531 (2021).CrossRefGoogle ScholarPubMed
Seay, J. F., “Biomechanics of load carriage-historical perspectives and recent insights,” J. Strength Cond. Res. 29(11), S129–S133 (2015).CrossRefGoogle ScholarPubMed
Walsh, G. and Low, D. C., “Military load carriage effects on the gait of military personnel: A systematic review,” Appl. Ergon. 93(1), 103376 (2021).CrossRefGoogle ScholarPubMed
Park, J. H., Jin, X. and Agrawal, S. K., “Second spine: Upper body assistive device for human load carriage,” J. Mech. Rob. 7(1), 11 (2015).Google Scholar
Park, J. H., Stegall, P., Zhang, H. H. and Agrawal, S., “Walking with a backpack using load distribution and dynamic load compensation reduces metabolic cost and adaptations to loads,” IEEE Trans. Neural Syst. Rehabil. Eng. 25(9), 1419–1430 (2017).CrossRefGoogle ScholarPubMed
Gregorczyk, K. N., Hasselquist, L., Schiffman, J. M., Bensel, C. K., Obusek, J. P. and Gutekunst, D. J., “Effects of a lower-body exoskeleton device on metabolic cost and gait biomechanics during load carriage,” Ergonomics 53(10), 1263–1275 (2010).CrossRefGoogle ScholarPubMed
Zhu, J., Wang, Y., Jiang, J. L., Sun, B. and Cao, H., “Unidirectional variable stiffness hydraulic actuator for load-carrying knee exoskeleton,” Int. J. Adv. Rob. Syst. 14(1), 1–12 (2017).Google Scholar
Zoss, A. B., Kazerooni, H. and Chu, A., “Biomechanical design of the berkeley lower extremity exoskeleton (BLEEX),” IEEE/ASME Trans. Mechatron. 11(2), 128–138 (2006).CrossRefGoogle Scholar
Mooney, L. M. and Herr, H. M., “Biomechanical walking mechanisms underlying the metabolic reduction caused by an autonomous exoskeleton,” J. Neuroeng. Rehabil. 13(4), 1–12 (2016).CrossRefGoogle ScholarPubMed
Panizzolo, F. A., Galiana, I., Asbeck, A. T., Siviy, C., Schmidt, K., Holt, K. G. and Walsh, C. J., “A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking,” J. Neuroeng. Rehabil. 13(1), 43–56 (2016).CrossRefGoogle ScholarPubMed
Zhan, Y. J., Zhang, W. J., Hou, Z. M., Kang, R. J., Dai, J. S. and Song, Z. B., “Non-anthropomorphic passive load-bearing lower-limb exoskeleton with a reconfigurable mechanism based on mechanical intelligence,” Mech. Mach. Theory 201, 105753 (2024).CrossRefGoogle Scholar
Zhang, W. J., Zhan, Y. J., Ju, W. J., Hou, Z. M., Kang, R. J., Dai, J. S. and Song, Z. B., “A novel quasi-passive non-anthropomorphic lower limb exoskeleton for load-bearing,” IEEE Rob. Autom. Lett. 9(11), 9891–9898 (2024).CrossRefGoogle Scholar
Hao, M., Zhang, J. W., Chen, K., Asada, H. and Fu, C. L., “Supernumerary robotic limbs to assist human walking with load carriage,” J. Mech. Rob. 12(6), 9 (2020).Google Scholar
Liao, Z. Y., Chen, B., Chang, T. Z., Zheng, Q., Liu, K. M. and Lv, J. N., “A human augmentation device design review: Supernumerary robotic limbs,” Ind. Rob. 50(2), 256–274 (2023).CrossRefGoogle Scholar
Fan, W., Dai, Z., Zhang, B., He, L., Pan, M., Yi, J. A. and Liu, T., “HyExo: A novel quasi-passive hydraulic exoskeleton for load-carrying augmentation,” IEEE-ASME Trans. Mechatron. 30(1), 144–155 (2025).CrossRefGoogle Scholar
Kong, P. W., Koh, A. H., Ho, M. Y. M., Iskandar, M. N. S. and Lim, C. X. E., “Effectiveness of a passive military exoskeleton in off-loading weight during static and dynamic load carriage: A randomised cross-over study,” Appl. Ergon. 119, 104293 (2024).CrossRefGoogle ScholarPubMed
Walsh, C. J., Endo, K. and Herr, H., “A quasi-passive leg exoskeleton for load-carrying augmentation,” Int. J. Hum. Rob. 4(3), 487–506 (2007).CrossRefGoogle Scholar
Wei, J., Zhang, S. and Zhang, J., “Biofusion design and parameter optimization for a novel passive assisted knee exoskeleton robot based on eight-bar mechanism,” Robotica 42(6), 1959–1985 (2024).CrossRefGoogle Scholar
Lim, H. and Park, S., “Kinematics of lower limbs during walking are emulated by springy walking model with a compliantly connected, off-centered curvy foot,” J. Biomech. 71(1), 119–126 (2018).CrossRefGoogle ScholarPubMed
Tsai, Y. Y., Lin, W. C., Cheng, K. Y., Lee, J. and Lee, T. Y., “Real-time physics-based 3D biped character animation using an inverted pendulum model,” IEEE Trans. Vis. Comput. Graph 16(2), 325–337 (2010).CrossRefGoogle ScholarPubMed
Whittington, B. R. and Thelen, D. G., “A simple mass-spring model with roller feet can induce the ground reactions observed in human walking,” J. Biomech. Eng. 131(1), 011013 (2008).CrossRefGoogle Scholar
Aoustin, Y. and Formalskii, A. M., “Walking of biped with passive exoskeleton: Evaluation of energy consumption,” Multibody Syst. Dyn. 43(1), 71–96 (2020).CrossRefGoogle Scholar
Kanjanapas, K. and Masayoshi, T., “7 Degrees of Freedom Passive Exoskeleton for Human Gait Analysis: Human Joint Motion Sensing and Torque Estimation during Walking In,” 6th IFAC Symposium on Mechatronic Systems (ELSEVIER, Hangzhou, China, 2013) pp. 285–292.Google Scholar
Kazerooni, H., Racine, J. L., Huang, L. and Steger, R., “On the Control of the Berkeley Lower Extremity Exoskeleton (BLEEX),” IEEE International Conference on Robotics and Automation (ICRA) (IEEE, Barcelona, SPAIN, 2006) pp. 4353–4360.CrossRefGoogle Scholar
Huang, T. and Kuo, A. D., “Mechanics and energetics of load carriage during human walking,” J. Exp. Biol. 217(4), 605–613 (2014).Google ScholarPubMed
Hansen, A. H., Childress, D. S. and Knox, E. H., “Roll-over shapes of human locomotor systems: Effects of walking speed,” Clin. Biomech. 19(4), 407–414 (2004).CrossRefGoogle ScholarPubMed
Levens, A. S., Inman, V. T. and Blosser, J. A., “Transverse rotation of the segments of the lower extremity in locomotion,” J. Bone Joint Surg. 30(4), 859–872 (1948).CrossRefGoogle Scholar
Ai, C. J., Wei, J., Zhang, J. J., Song, J. K., Lv, W. L. and Liu, C. L., “Variable pivot gait based a novel dynamics correction method for human lower limbs model,” J. Biomech. Eng.-Trans. ASME 146(4), 10 (2024).CrossRefGoogle ScholarPubMed
Doerks, F., Riedel, L., Einfeldt, A. K., Windhagen, H., Hurschler, C. and Jakubowitz, E., “Contribution of various forefoot areas to push-off peak at different speeds and slopes during walking,” Gait Posture 108(1), 264–269 (2024).CrossRefGoogle Scholar
Winter, D. A., “Energy generation and absorption at the ankle and knee during fast, natural, and slow cadences,” Clin. Orthop. Relat. Res. 175(175), 147–154 (1983).CrossRefGoogle Scholar
Collins, S. H., Wiggin, M. B. and Sawicki, G. S., “Reducing the energy cost of human walking using an unpowered exoskeleton,” Nature 522(7555), 212–215 (2015).CrossRefGoogle ScholarPubMed
Wang, X. Y., Guo, S., Qu, B. J., Song, M. J. and Qu, H. B., “Design of a passive gait-based ankle–foot exoskeleton with self-adaptive capability,” Chin. J. Mech. Eng. 33(3), 1–11 (2020).CrossRefGoogle Scholar
Soo, C. H. and Donelan, J. M., “Coordination of push-off and collision determine the mechanical work of step-to-step transitions when isolated from human walking,” Gait Posture 35(2), 292–297 (2012).CrossRefGoogle ScholarPubMed
Zelik, K. E., Takahashi, K. Z. and Sawicki, G. S., “Six degree-of-freedom analysis of hip, knee, ankle and foot provides updated understanding of biomechanical work during human walking,” J. Exp. Biol. 218(6), 876–886 (2015).CrossRefGoogle ScholarPubMed
Ho, W. H., Shiang, T. Y., Lee, C. C. and Cheng, S. Y., “Body segment parameters of young Chinese men determined with magnetic resonance imaging,” Med. Sci. Sports Exercise 45(9), 1759–1766 (2013).CrossRefGoogle ScholarPubMed
Hoy, M. G., Zajac, F. E. and Gordon, M. E., “A musculoskeletal model of the human lower extremity: The effect of muscle, tendon, and moment arm on the moment–angle relationship of musculotendon actuators at the hip, knee, and ankle,” J. Biomech. 23(2), 157–169 (1990).CrossRefGoogle ScholarPubMed
Delp, S. L., Anderson, F. C., Arnold, A. S., Loan, P. and Thelen, D. G., “OpenSim: Open-source software to create and analyze dynamic simulations of movement,” IEEE Trans. Bio-MED Eng. 54(11), 1940–1950 (2007).CrossRefGoogle ScholarPubMed
Falisse, A., Serrancolí, G., Dembia, C. L., Gillis, J. and Groote, F. D., “Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement,” PLOS ONE 14(10), e0217730 (2019).CrossRefGoogle ScholarPubMed
Antonie, J., Blana, D. and Dieter, H., “Implicit methods for efficient musculoskeletal simulation and optimal control,” Proc. IUTAM 2(1), 297–316 (2011).Google Scholar
De Groote, F., Kinney, A. L., Rao, A. V. and Fregly, B. J., “Evaluation of direct collocation optimal control problem formulations for solving the muscle redundancy problem,” Ann. Biomed. Eng. 44(10), 2922–2936 (2016).CrossRefGoogle ScholarPubMed
Westenskow, D. R., Schipke, C. A., Raymond, J. L., Saffle, J. R., Becker, J. M., Young, E. W. and Cutler, C. A., “Calculation of metabolic expenditure and substrate utilization from gas exchange measurements,” Jpen J. Parenteral Enteral Nutr. 12(1), 20 (1988).CrossRefGoogle ScholarPubMed
Kim, H. J., Lim, D. H., Kim, W. S., Han, C. S., “Development of a passive modular knee mechanism for a lower limb lxoskeleton robot and its effectiveness in the workplace,” Int. J. Precis. Eng. Manuf. 21(2), 227–236 (2020).CrossRefGoogle Scholar