Since the rigid constructions of the traditional exoskeletons are liable to increase inertia of the joint and limit wearer flexibility, the soft-type exoskeletons have been developed in recent years, which can reduce system inertia, increase interaction compliance, and ensure operation safety. In this article, we present the detailed design and preliminary control of a soft body-worn and tendon-sheath-driven exoskeleton that can apply appropriate torques to the biological knee joints during rehabilitation training. This article focuses on the design of compliance tendon-sheath actuation system based on Hill muscle model, which utilizes multi-wrap pulleys and Hill-based series elastic actuator to transmit effective forces. Besides, the overall control system and strategy architectures of knee soft exoskeleton are also proposed. A fuzzy proportional–integral–derivative controller is developed for the passive rehabilitation training. On this basis, a back-propagation-neural-network-based adaptive impedance control scheme is presented to provide adaptive force based on the disease condition of the patient. Finally, preliminary experiments are conducted to demonstrate the effectiveness of proposed exoskeleton and control strategies.
ChenBMaHQinLY, et al. Recent developments and challenges of lower extremity exoskeletons. J Orthop Transl2016; 5: 26–37.
2.
WuQCChenY.Development of an intention-based adaptive neural cooperative control strategy for upper-limb robotic rehabilitation. IEEE Robot Autom Lett2021; 6(2): 335–342.
3.
LuengasYRLopez-GutierrezRSalazarSC, et al. Robust controls for upper limb exoskeleton, real-time results. Proc IMechE, Part I: J Systems and Control Engineering2018; 232(7): 797–806.
4.
ChenLLWangCSongXW, et al. Dynamic trajectory adjustment of lower limb exoskeleton in swing phase based on impedance control strategy. Proc IMechE, Part I: J Systems and Control Engineering2020; 234: 1120–1132.
5.
WuQCWangXSDuFP, et al. Design and control of a powered hip exoskeleton for walking assistance. Int J Adv Robot Syst2015; 12(3): 18.
6.
RupalBSRafiqueSSinglaA, et al. Lower-limb exoskeletons: research trends and regulatory guidelines in medical and non-medical applications. Int J Adv Robot Syst2017; 14(6): 1–27.
7.
WuQCChenBWuHT.Neural-network-enhanced torque estimation control of a soft wearable exoskeleton for elbow assistance. Mechatronics2019; 63: 102279.
8.
CornwallW.In pursuit of the perfect power suit. Science2015; 350(6258): 270–273.
9.
AwadLNBaeJO’DonnellK, et al. A soft robotic exosuit improves walking in patients after stroke. Sci Transl Med2017; 9: eaai9084.
10.
DingYKimMKuindersmaS, et al. Human-in-the-loop optimization of hip assistance with a soft exosuit during walking. Sci Robot2018; 3: eaar5438.
11.
JinSIwamotoNHashimotoK, et al. Experimental evaluation of energy efficiency for a soft wearable robotic suit. IEEE T Neur Sys Reh2017; 25(8): 1192–1201.
12.
OrtizJPolieroTCairoliG, et al. Energy efficiency analysis and design optimization of an actuation system in a soft modular lower limb exoskeleton. IEEE Robot Autom Lett2017; 3(1): 484–491.
13.
FangJYuanJWangM, et al. Novel accordion-inspired foldable pneumatic actuators for knee assistive devices. Soft Robot2019; 7(1): 95–108.
LessardSPansodteePRobbinsA, et al. A soft exosuit for flexible upper-extremity rehabilitation. IEEE T Neur Sys Reh2018; 26(8): 1604–1617.
16.
RognonCMintchevSAgnolaF, et al. FlyJacket: an upper-body soft exoskeleton for immersive drone control. IEEE Robot Autom Lett2018; 3(3): 2362–2369.
17.
YapHKLimJHNasrallahF, et al. A soft exoskeleton for hand assistive and rehabilitation application using pneumatic actuators with variable stiffness. In: Proceedings of the IEEE international conference on robotics and automation, Seattle, WA, 26–30 May 2015, pp.4967–4972. New York: IEEE.
18.
EngJJWinterDA.Kinetic analysis of the lower limbs during walking: what information can be gained from a three-dimensional model. J Biomech1995; 28: 753–758.
19.
AsbeckATDyerRLarussonA, et al. Biologically inspired soft exosuit. In: Proceedings of the 2013 IEEE international conference on rehabilitation robotics, Seattle, WA, 24–26 June 2013, pp.1–8. New York: IEEE.
20.
SiviyCBaeJBakerL, et al. Offline assistance optimization of a soft exosuit for augmenting ankle power of stroke survivors during walking. IEEE Robot Autom Lett2020; 5(2): 828–835.
21.
WuQCWangXSChenL, et al. Transmission model and compensation control of double-tendon-sheath actuation system. IEEE T Ind Electron2015; 62(3): 1599–1609.
22.
DingYGalianaIAsbeckA, et al. Multi-joint actuation platform for lower extremity soft exosuits. In: Proceedings of the 2014 IEEE international conference on robotics and automation, Hong Kong, China, 31 May–7 June 2014, pp.1327–1334. New York: IEEE.
23.
FalisseARossomSVJonkersI.EMG-driven optimal estimation of subject-SPECIFIC Hill model muscle–tendon parameters of the knee joint actuators. IEEE T Biomed Eng2017; 64(9): 2253–2262.
24.
WintersJM.Hill-based muscle models: a system engineering perspective. In: WintersJWooS (eds) Multiple muscle systems. New York: Springer-Verlag, 1990, pp.69–93.
25.
ZajacFE.Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Crit Rev Biomed Eng1989; 17(4): 359–411.
26.
LuLHWuQCChenX, et al. Development of a sEMG-based torque estimation control strategy for a soft elbow exoskeleton. Robot Auton Syst2018; 111: 88–98.
27.
HanYLWangXS.The biomechanical study of lower limb during human walking. Sci China Technol Sci2011; 54(4): 983–991.
28.
AsbeckATDe RossiSMMHoltKG, et al. A biologically-inspired soft exosuit for walking assistance. Int J Robot Res2015; 34(6): 744–762.
29.
BennettDJHollerbachJMXuY.Time-varying stiffness of human elbow joint during cyclic voluntary movement. Exp Brain Res1992; 88(2): 433–442.
30.
KamperDGRymerWZ. A biomechanical simulation of the effect of the extrinsic flexor muscles on finger joint flexion. In: 2001 conference proceedings of the 23rd annual international conference of the IEEE Engineering in Medicine and Biology Society, vol. 23, Istanbul, 25–28 October 2001, pp.1256–1259. New York: IEEE.
31.
WuQCWangXSChenB, et al. Design and fuzzy sliding mode admittance control of a soft wearable exoskeleton for elbow rehabilitation. IEEE Access2018; 6: 60249–60263.
32.
BorboniAMorMFagliaR.Gloreha—hand robotic rehabilitation: design, mechanical model, and experiments. J Dyn Syst: T ASME2016; 138(11): 111003.
33.
WuQCWangXSChenB, et al. Development of a minimal-intervention-based admittance control strategy for upper extremity rehabilitation exoskeleton. IEEE T Syst Man Cy: S2018; 48(6): 1005–1016.
34.
WuQCWangXSChenB, et al. Development of an RBFN-based neural-fuzzy adaptive control strategy for an upper limb rehabilitation exoskeleton. Mechatronics2018; 53: 85–94.
35.
LinCCJuMSLinCW.The pendulum test for evaluating spasticity of the elbow joint. Arch Phys Med Rehab2003; 84(1): 69–74.
36.
PizziACarlucciGFalsiniC, et al. Evaluation of upper-limb spasticity after stroke: a clinical and neurophysiologic study. Arch Phys Med Rehab2005; 86(3): 410–415.
