Optimum design and preliminary experiments of a novel parallel end traction apparatus for upper-limb rehabilitation

Abstract

Robot-assisted technology has been increasingly employed in the therapy of post stroke patients to deliver high-quality treatment and alleviate therapists’ burden. This paper introduces a novel parallel end traction apparatus (PETA) to supplement equipment selection. Considering the appearance and performance of the PETA, two types of special five-bar linkage mechanisms are selected as the potential configurations of the actuation execution unit because of their compact arrangement and parallel structure. Kinematic analysis of each mechanism, i.e., position solutions and Jacobian matrix, is carried out. Subsequently, a comparative study between the two mechanisms is conducted. In the established source of nondimensional parameter synthesis, the singularity, maximum continuous workspace, and performance variation trends are analyzed. Based on the evaluation results, the final scheme with determined configuration and corresponding near-optimized nondimensional parameters is obtained. Then, a prototype is constructed. By adding a lockable translational degree of freedom in the vertical direction, the PETA can provide 2D planar exercise and 3D spatial exercise. Finally, a control system is developed for passive exercise mode based on the derived inverse position solution, and preliminary experiments are performed to verify the applicability of the PETA.

Keywords

parallel mechanism / upper-limb rehabilitation / singularity and workspace analyses / performance evaluation / optimum design

参考文献

[1] ZhangK, ChenX, LiuF. System framework of robotics in upper limb rehabilitation on poststroke motor recovery. Behavioural Neurology, 2018, 2018 : 6737056–

[2] MaciejaszP, EschweilerJ, Gerlach-HahnK. A survey on robotic devices for upper limb rehabilitation. Journal of Neuroengineering and Rehabilitation, 2014, 11( 1): 3–

[3] BabaiaslM, MahdiounS H, JaryaniP. A review of technological and clinical aspects of robot-aided rehabilitation of upper-extremity after stroke. Disability and Rehabilitation, 2015, 11( 4): 263– 280

[4] LoureiroR C V, HarwinW S, NagaiK. Advances in upper limb stroke rehabilitation: a technology push. Medical & Biological Engineering & Computing, 2011, 49( 10): 1103– 1118

[5] LoH S, XieS Q. Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. Medical Engineering & Physics, 2012, 34( 3): 261– 268

[6] NordinN, XieS Q, WunscheB. Assessment of movement quality in robot-assisted upper limb rehabilitation after stroke: a review. Journal of Neuroengineering and Rehabilitation, 2014, 11( 1): 137–

[7] SanchezR J, LiuJ, RaoS. Automating arm movement training following severe stroke: functional exercises with quantitative feedback in a gravity-reduced environment. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006, 14( 3): 378– 389

[8] CarignanC, TangJ, RoderickS. Development of an exoskeleton haptic interface for virtual task training. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. St. Louis: IEEE, 2009, 2009‒3702

[9] FrisoliA, BorelliL, MontagnerA. Arm rehabilitation with a robotic exoskeleleton in virtual reality. In: Proceedings of 2007 IEEE 10th International Conference on Rehabilitation Robotics. Noordwijk: IEEE, 2007, 631‒642

[10] PerryJ C, RosenJ, BurnsS. Upper-limb powered exoskeleton design. IEEE/ASME Transactions on Mechatronics, 2007, 12( 4): 408– 417

[11] BallS J, BrownI E, ScottS H. MEDARM: a rehabilitation robot with 5DOF at the shoulder complex. In: Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Zurich: IEEE, 2007, 1‒6

[12] HuangJ, TuX, HeJ. Design and evaluation of the RUPERT wearable upper extremity exoskeleton robot for clinical and in-home therapies. IEEE Transactions on Systems, Man, and Cybernetics. Systems, 2016, 46( 7): 926– 935

[13] TuX, HanH, HuangJ. Upper limb rehabilitation robot powered by PAMs cooperates with FES arrays to realize reach-to-grasp training. Journal of Healthcare Engineering, 2017, 2017 : 1282934–

[14] KleinJ, SpencerS, AllingtonJ. Optimization of a parallel shoulder mechanism to achieve a high-force, low-mass, robotic-arm exoskeleton. IEEE Transactions on Robotics, 2010, 26( 4): 710– 715

[15] NefT, Mihelj M, ColomboG. ARMin−Robot for rehabilitation of the upper extremities. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation. Orlando: IEEE, 2006, 2006‒3157

[16] MiheljM, NefT, Riener R. ARMin II−7 DOF rehabilitation robot: mechanics and kinematics. In: Proceedings of the 2007 IEEE International Conference on Robotics and Automation. Roma: IEEE, 2007, 2007‒4125

[17] RianiA, MadaniT, BenallegueA. Adaptive integral terminal sliding mode control for upper-limb rehabilitation exoskeleton. Control Engineering Practice, 2018, 75 : 108– 117

[18] StienenA H A, HekmanE E G, Van der HelmF C T. Dampace: dynamic force-coordination trainer for the upper extremities. In: Proceedings of 2007 IEEE 10th International Conference on Rehabilitation Robotics. Noordwijk: IEEE, 2007, 820– 826

[19] OttenA, VoortC, StienenA. LIMPACT: a hydraulically powered self-aligning upper limb exoskeleton. IEEE/ASME Transactions on Mechatronics, 2015, 20( 5): 2285– 2298

[20] LiJ, ZuoS, XuC. Influence of a compatible design on physical human-robot interaction force: a case study of a self-adapting lower-limb exoskeleton mechanism. Journal of Intelligent & Robotic Systems, 2020, 98( 2): 525– 538

[21] GopuraR A R C, KiguchiK, LiY. SUEFUL-7: a 7DOF upper-limb exoskeleton robot with muscle-model-oriented EMG-based control. In: Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. St. Louis: IEEE, 2009, 2009‒1131

[22] WuQ, WangX, ChenB. Development of an RBFN-based neural-fuzzy adaptive control strategy for an upper limb rehabilitation exoskeleton. Mechatronics, 2018, 53( 8): 85– 94

[23] LiJ, ZhangZ, TaoC. A number synthesis method of the self-adapting upper-limb rehabilitation exoskeletons. International Journal of Advanced Robotic Systems, 2017, 14( 3): 1– 14

[24] LiJ, CaoQ, ZhangC. Position solution of a novel four-DOFs self-aligning exoskeleton mechanism for upper limb rehabilitation. Mechanism and Machine Theory, 2019, 141 : 14– 39

[25] MaoY, JinX, Gera DuttaG. Human movement training with a cable driven ARm EXoskeleton (CAREX). IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015, 23( 1): 84– 92

[26] CuiX, ChenW, JinX. Design of a 7-DOF cable-driven arm exoskeleton (CAREX-7) and a controller for dexterous motion training or assistance. IEEE/ASME Transactions on Mechatronics, 2017, 22( 1): 161– 172

[27] AvizzanoC A, SatlerM, CappielloG. MOTORE: a mobile haptic interface for neuro-rehabilitation. In: Proceedings of the 20th IEEE International Symposium on Robot and Human Interactive Communication. Atlanta: IEEE, 2011, 383– 388

[28] KrebsH I, VolpeB T, AisenM L. Robotic applications in neuromotor rehabilitation. Robotica, 2003, 21( 1): 3– 11

[29] CasadioM, SanguinetiV, MorassoP G. Braccio di Ferro: a new haptic workstation for neuromotor rehabilitation. Technology and Health Care, 2006, 14( 3): 123– 142

[30] LiuY, LiC, JiL. Development and implementation of an end-effector upper limb rehabilitation for hemiplegic patients with line and circle tracking training. Journal of Healthcare Engineering, 2017, 2017 : 4931217–

[31] SivanM, GallagherJ, MakowerS. Home-based computer assisted arm rehabilitation (hCAAR) robotic device for upper limb exercise after stroke: results of a feasibility study in home setting. Journal of Neuroengineering and Rehabilitation, 2014, 11( 1): 163–

[32] HuangH, JiL, ShiY. Dynamic modeling and interactive performance of PARM: a parallel upper-limb rehabilitation robot using impedance control for patients after stroke. Journal of Healthcare Engineering, 2018, 2018 : 1817479–

[33] PengL, HouZ, PengL. Design of CASIA-ARM: a novel rehabilitation robot for upper limbs. In: Proceedings of 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems. Hamburg: IEEE, 2015, 5611– 5616

[34] PengL, HouZ, PengL. Robot assisted rehabilitation of the arm after stroke: prototype design and clinical evaluation. Science China. Information Sciences, 2017, 60( 7): 073201–

[35] ColomboR, PisanoF, MiceraS. Robotic techniques for upper limb evaluation and rehabilitation of stroke patients. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2005, 13( 3): 311– 324

[36] FraileJ C, Perez-TurielJ, BaeyensE. E2Rebot: a robotic platform for upper limb rehabilitation in patients with neuromotor disability. Advances in Mechanical Engineering, 2016, 8( 8): 1– 13

[37] WuF, Lin Y, KuoT. Clinical effects of combined bilateral arm training with functional electrical stimulation in patients with stroke. In: Proceedings of 2011 IEEE International Conference on Rehabilitation Robotics. Zurich: IEEE, 2011

[38] LamP, HebertD, BogerJ. A haptic-robotic platform for upper-limb reaching stroke therapy: preliminary design and evaluation results. Journal of Neuroengineering and Rehabilitation, 2008, 5( 1): 15–

[39] PerryJ C, ZabaletaH, BellosoA. ArmAssist: development of a functional prototype for at-home telerehabilitation of post-stroke arm impairment. In: Proceedings of the 4th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics. Rome: IEEE, 2012, 2012‒1566

[40] ZanottoD, RosatiG, MintoS. Sophia-3: a semiadaptive cable-driven rehabilitation device with a tilting working plane. IEEE Transactions on Robotics, 2014, 30( 4): 974– 979

[41] LumP S, BurgarC G, Van der LoosM. MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: a follow-up study. Journal of Rehabilitation Research and Development, 2006, 43( 5): 631– 642

[42] LoureiroR, AmirabdollahianF, ToppingM. Upper limb robot mediated stroke therapy-GENTLE/s approach. Autonomous Robots, 2003, 15( 1): 35– 51

[43] JohnsonM J, WisneskiK J, AndersonJ. Development of ADLER: the activities of daily living exercise robot. In: Proceedings of the First IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics. Pisa: IEEE, 2006

[44] StienenA H A, McPhersonJ G, SchoutenA C. The ACT-4D: a novel rehabilitation robot for the quantification of upper limb motor impairments following brain injury. In: Proceedings of 2011 IEEE International Conference on Rehabilitation Robotics. Zurich: IEEE, 2011, 5975460–

[45] OctaviaJ R, FeysP, ConinxK. Development of activity-related muscle fatigue during robot-mediated upper limb rehabilitation training in persons with multiple sclerosis: a pilot trial. Multiple Sclerosis International, 2015, 2015 : 650431–

[46] TrlepM, MiheljM, PuhU. Rehabilitation robot with patient-cooperative control for bimanual training of hemiparetic subjects. Advanced Robotics, 2011, 25( 15): 1949– 1968

[47] CaiS, ChenY, HuangS. SVM-based classification of sEMG signals for upper-limb self-rehabilitation training. Frontiers in Neurorobotics, 2019, 13 : 31–

[48] SongA, PanL, XuG. Adaptive motion control of arm rehabilitation robot based on impedance identification. Robotica, 2015, 33( 9): 1795– 1812

[49] TothA T, FazekasG, ArzG. Passive robotic movement therapy of the spastic hemiparetic arm with REHAROB: report of the first clinical test and the follow-up system improvement. In: Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics. Chicago: IEEE, 2005, 127‒130

[50] JacksonA E, HoltR J, CulmerP R. Dual robot system for upper limb rehabilitation after stroke: the design process. Proceedings of the Institution of Mechanical Engineers. Part C, Journal of Mechanical Engineering Science, 2007, 221( 7): 845– 857

[51] MasieroS, CeliaA, RosatiG. Robotic-assisted rehabilitation of the upper limb after acute stroke. Archives of Physical Medicine and Rehabilitation, 2007, 88( 2): 142– 149

[52] RosatiG, GallinaP, MasieroS. Design of a new 5 d.o.f. wire-based robot for rehabilitation. In: Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics. Chicago: IEEE, 2005, 430‒433

[53] BeerR F, NaujokasC, BachrachB. Development and evaluation of a gravity compensated training environment for robotic rehabilitation of post-stroke reaching. In: Proceedings of the 2nd Biennial IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. Scottsdale: IEEE, 2008, 205‒210

[54] LiuX, WangJ, PritschowG. Kinematics, singularity and workspace of planar 5R symmetrical parallel mechanisms. Mechanism and Machine Theory, 2006, 41( 2): 145– 169

[55] LiuX, WangJ, PritschowG. Performance atlases and optimum design of planar 5R symmetrical parallel mechanisms. Mechanism and Machine Theory, 2006, 41( 2): 119– 144

[56] ZuoS, LiJ, DongM. Design and performance evaluation of a novel wearable parallel mechanism for ankle rehabilitation. Frontiers in Neurorobotics, 2020, 14 : 9–

[57] WuC, LiuX, WangL. Optimal design of spherical 5R parallel manipulators considering the motion/force transmissibility. Journal of Mechanical Design, 2010, 132( 3): 031002–

[58] XieF, LiuX, WangJ. Kinematic optimization of a five-degrees-of-freedom spatial parallel mechanism with large orientational workspace. Journal of Mechanisms and Robotics, 2017, 9( 5): 051005–

[59] EnferadiJ, NikroozR. The performance indices optimization of a symmetrical fully spherical parallel mechanism for dimensional synthesis. Journal of Intelligent & Robotic Systems, 2018, 90(3‒4): 305– 321

[60] MaN, YuJ, DongX. Design and stiffness analysis of a class of 2-DoF tendon driven parallel kinematics mechanism. Mechanism and Machine Theory, 2018, 129 : 202– 217

[61] MaN, DongX, PalmerD. Parametric vibration analysis and validation for a novel portable hexapod machine tool attached to surfaces with unequal stiffness. Journal of Manufacturing Processes, 2019, 47 : 192– 201

[62] CarboneG. Stiffness analysis and experimental validation of robotic system. Frontiers of Mechanical Engineering, 2011, 6( 2): 182– 196

[63] ChenJ, XieF, LiuX. Stiffness evaluation of an adsorption robot for large-scale structural parts processing. Journal of Mechanisms and Robotics, 2021, 13( 4): 040907–

[64] ChenC, PengF, YanR. Stiffness performance index based posture and feed orientation optimization in robotic milling process. Robotics and Computer-Integrated Manufacturing, 2019, 55 : 29– 40

[65] ISO/TR-7250-2:2010. Basic human body measurements for technological design Part 2: statistical summaries of body measurements from national populations. 2010

[66] GB/T10000-1988. Human dimensions of Chinese adults. 1988 (in Chinese)

[67] DongM, FanW, LiJ. A new ankle robotic system enabling whole-stage compliance rehabilitation training. IEEE/ASME Transactions on Mechatronics, 2021, 26( 3): 1490– 1500

Nomenclatures

Abbreviations
DOF	Degree of freedom
MCW	Maximum continuous workspace
PETA	Parallel end traction apparatus
ROM	Range of motion
RTTA	Rectangular target treatment area
SPTM	Special parallelogram type mechanism
SSTM	Special symmetrical type mechanism
Variables
Bi	Coordinate of point Bi
Di	DOFs permitted by joints
f1	Output force along X-axis
f2	Output force along Y-axis
f	Vector of output forces
F	DOFs of SSTM and SPTM
Gris	Global maximum force index
GηJ	Global motion isotropy index
GηS	Global structural stiffness index
h	Number of joints
JFoSPTM	Forward Jacobian matrix of SPTM
JFoSSTM	Forward Jacobian matrix of SSTM
JIoSPTM	Inverse Jacobian matrix of SPTM
JIoSSTM	Inverse Jacobian matrix of SSTM
JS	Stiffness Jacobian matrix
JV	Velocity Jacobian matrix
JVoSPTM	Velocity Jacobian matrix of SPTM
JVoSSTM	Velocity Jacobian matrix of SSTM
K	Scalar matrix representing the stiffness of the active joints
k1, k2	Stiffness of of joints A1 and A2, respectively
l1,l2	Nondimensional form of L1 andL2, respectively
lvlp	Length of long principal axis of velocity ellipsoid
lvsp	Length of short principal axis of velocity ellipsoid
L	Average value of L1 and L2
L1	Length of links AiBi of SSTM, lengths of links A1B1 and B2P of SPTM
L2	Length of links BiP of SSTM, lengths of links A2B2 and B1P of SPTM
n	Number of links
P	Coordinate of point P
\dot{\mathit{q}}	Vector of input velocities

ris	Local maximum force index, i.e., radius of inscribed circle contained in Tf
Tf	Generalized set of output forces of end effector
Tτ	Set of allowable torques of active joints
\dot{\mathit{u}}	Vector of output velocities
v	Number of parallel redundant constraints
w	MCW of mechanism
XP	Coordinate of point P in X-axis direction
{\dot{X}}_P	Output velocity in X-axis direction
YP	Coordinate of point P in Y-axis direction
{\dot{Y}}_P	Output velocity in Y-axis direction
ηJ	Local motion isotropy index, i.e., inverse value of condition number of velocity ellipsoid
ηS	Local structural stiffness index, i.e., maximum micro deformation of end effector
θ1	Input angle of joint A1
{\dot{\theta }}_1	Input angular velocity of joint A1
θ2	Input angle of joint A2
{\dot{\theta }}_2	Input angular velocity of joint A2
λ	Number of common constraints
λi	Eigenvalues of matrix JSTJS
τ	Vector of driving torques
τ1, τ2	Driving torque of joints A1 and A2, respectively
τ1max, τ2max	Maximum torque applied by actuator on joints A1 and A2, respectively
Δq	Vector of virtual angular displacements associated with active joints
Δu	Vector of virtual deformations of end effector
ΔXP, ΔYP	Virtual deformation of end effector in X- and Y-axis directions, respectively
Δθ1, Δθ2	Virtual angular displacement associated with joint A1 and A2, respectively

Acknowledgements

The authors thank Henan Huibo Medical Co., Ltd. for several useful suggestions on this apparatus. Additionally, this research is funded in part by Beijing Natural Science Foundation (Grant No. 3204036), in part by the National Key R&D Program of China (Grant Nos. 2018YFB1307004 and 2020YFC2004200), in part by the National Natural Science Foundation of China (Grant No. 61903011), and in part by the General Program of Science and Technology Development Project of the Beijing Municipal Education Commission (Grant No. KM202010005021).

Electronic Supplementary Materials

The supplementary materials can be found in the online version of this article at https://doi.org/10.1007/s11465-021-0651-5 and are accessible to authorized users.

版权

2021 Higher Education Press 2021.