Power Assist Rehabilitation Robot and Motion Intention Estimation
(1) Zulikha Ayomikun Adeola-Bello Mail (International Islamic University Malaysia (IIUM), Malaysia)
(2) * Norsinnira Zainul Azlan Mail (International Islamic University Malaysia (IIUM), Malaysia)
*corresponding author

Abstract


This article attempts to review papers on power assist rehabilitation robots, human motion intention, control laws, and estimation of power assist rehabilitation robots based on human motion intention in recent years. This paper presents the various ways in which human motion intention in rehabilitation can be estimated. This paper also elaborates on the control laws for the estimation of motion intention of the power assist rehabilitation robot. From the review, it has been found that the motion intention estimation method includes: Artificial Intelligence-based motion intention and Model-based motion intention estimation. The controllers include hybrid force/position control, EMG control, and adaptive control. Furthermore, Artificial Intelligence based motion intention estimation can be subdivided into Electromyography (EMG), Surface Electromyography (SEMG), Extreme Learning Machine (ELM), and Electromyography-based Admittance Control (EAC). Also, Model-based motion intention estimation can be subdivided into Impedance and Admittance control interaction. Having reviewed several papers, EAC and ELM are proposed for efficient motion intention estimation under artificial-based motion intention. In future works, Impedance and Admittance control methods are suggested under model-based motion intention for efficient estimation of motion intention of power assist rehabilitation robot.  In addition, hybrid force/position control and adaptive control are suggested for the selection of control laws. The findings of this review paper can be used for developing an efficient power assist rehabilitation robot with motion intention to aid people with lower or upper limb impairment.

Keywords


Power assist robot; Rehabilitation; Motion intention estimation; Controller

   

DOI

https://doi.org/10.31763/ijrcs.v2i2.650 		      

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[1] L. Alrabghi, R. Alnemari, R. Aloteebi, H. Alshammari, M. Ayyad, M. Al Ibrahim, M. Alotayfi, T. Bugshan, A. Alfaifi, and H. Aljuwayd, “Stroke types and management,” International Journal Of Community Medicine And Public Health, vol. 5, no. 9, p. 3715, 2018, https://doi.org/10.18203/2394-6040.ijcmph20183439.

[2] H.Y. Li, A.G. Dharmawan, I. Paranawithana, L. Yang, and U.X. Tan, “A Control Scheme for Physical Human-Robot Interaction Coupled with an Environment of Unknown Stiffness,” Journal of Intelligent and Robotic Systems, vol. 100, no. 1, pp. 165–182, 2020, https://doi.org/10.1007/s10846-020-01176-2.

[3] A. Umemura, Y. Saito, and K. Fujisaki, “A study on power-assisted rehabilitation robot arms operated by patient with upper limb disabilities,” 2009 IEEE International Conference on Rehabilitation Robotics, ICORR 2009, pp. 451–456, 2009, https://doi.org/10.1109/ICORR.2009.5209512.

[4] H. M. Qassim, “Applied Sciences,” Early Writings on India, pp. 124–134, 2018, https://doi.org/10.4324/9781315232140-14.

[5] P. S. Lum, C. G. Burgar, and P. C. Shor, “Evidence for improved muscle activation patterns after retraining of reaching movements with the MIME robotic system in subjects with post-stroke hemiparesis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 12, no. 2, pp. 186–194, 2004, https://doi.org/10.1109/TNSRE.2004.827225.

[6] R. Bogue, “Rehabilitation robots,” Industrial Robot, vol. 45, no. 3, pp. 301–306, 2018, https://doi.org/10.1108/IR-03-2018-0046.

[7] S. F. Atashzar, M. Shahbazi, and R. V. Patel, “Haptics-enabled Interactive NeuroRehabilitation Mechatronics: Classification, Functionality, Challenges and Ongoing Research,” Mechatronics, vol. 57, pp. 1–19, 2019, https://doi.org/10.1016/j.mechatronics.2018.03.002.

[8] D. Tokody, L. Ady, L. F. Hudasi, P. J. Varga, and P. Hell, “Collaborative robotics research: Subiko Project,” Procedia Manufacturing, Elsevier, vol. 46, pp. 467–474, 2020, https://doi.org/10.1016/j.promfg.2020.03.068.

[9] F. Rubio, F. Valero, and C. Llopis-Albert, “A review of mobile robots: Concepts, methods, theoretical framework, and applications,” International Journal of Advanced Robotic Systems, vol. 16, no. 2, pp. 1–22, 2019, https://doi.org/10.1177/1729881419839596.

[10] S. H. Lee, G. Park, D. Y. Cho, H. Y. Kim, J. Y. Lee, S. Kim, S. B. Park, and J. H. Shin, “Comparisons between end-effector and exoskeleton rehabilitation robots regarding upper extremity function among chronic stroke patients with moderate-to-severe upper limb impairment,” Scientific Reports, vol. 10, no. 1, pp. 1–8, 2020, https://doi.org/10.1038/s41598-020-58630-2.

[11] T. Eiammanussakul and V. Sangveraphunsiri, “A lower limb rehabilitation robot in sitting position with a review of training activities,” Journal of Healthcare Engineering, vol. 2018, pp. 1–18, 2018, https://doi.org/10.1155/2018/1927807.

[12] E. Akdoǧan and M. A. Adli, “The design and control of a therapeutic exercise robot for lower limb rehabilitation: Physiotherabot,” Mechatronics, vol. 21, no. 3, pp. 509–522, 2011, https://doi.org/10.1016/j.mechatronics.2011.01.005.

[13] F. Zhang, Z.G. Hou, L. Cheng and W. Wang, Y. Chen, J. Hu, L. Peng, H. Wang, “ILeg-A Lower Limb Rehabilitation Robot: A Proof of Concept,” IEEE Transactions on Human-Machine Systems, vol. 46, no. 5, pp. 761–768, 2016, https://doi.org/10.1109/THMS.2016.2562510.

[14] J. Yoon, J. Ryu, and K. B. Lim, “Reconfigurable ankle rehabilitation robot for various exercises,” Journal of Robotic Systems, vol. 22, pp. 15–33, 2006, https://doi.org/10.1002/rob.20150.

[15] K. Zhang, X. Chen, F. Liu, H. Tang, J. Wang, and W. Wen, “System framework of robotics in upper limb rehabilitation on poststroke motor recovery,” Behavioural Neurology, vol. 2018, pp. 1–14, 2018, https://doi.org/10.1155/2018/6737056.

[16] K. Sirlantzis, L.B. Larsen, L.K. Kanumuru, P. Oprea, “Robotics,” Handbook of Electronic Assistive Technology, pp. 311–345, 2018, https://doi.org/10.1016/B978-0-12-812487-1.00011-9.

[17] U.J. Cheah, P.R. Tan, O.T. Thaddaeus, A. Huan, M.A. Bin Mohd Faisal, Z.T. Tse, C.M. Lim, and H. Ren, “Design evolution of a flexible robotic bending end-effector for transluminal explorations,” Flexible Robotics in Medicine: A Design Journey of Motion Generation Mechanisms and Biorobotic System Development, pp. 289–325, 2020, https://doi.org/10.1016/B978-0-12-817595-8.00014-6.

[18] K. Kiguchi and T. Fukuda, “A 3DOF exoskeleton for upper-limb motion assist - Consideration of the effect of bi-articular muscles,” Proceedings - IEEE International Conference on Robotics and Automation, vol. 2004, no. 3, pp. 2424–2429, 2004, https://doi.org/10.1109/robot.2004.1307424.

[19] F. Molteni, G. Gasperini, G. Cannaviello, and E. Guanziroli, “Exoskeleton and End-Effector Robots for Upper and Lower Limbs Rehabilitation: Narrative Review,” PM and R, vol. 10, no. 9, pp. 174–188, 2018, https://doi.org/10.1016/j.pmrj.2018.06.005.

[20] X. Wang, X. Li, and J. Wang, “Modeling and identification of the human-exoskeleton interaction dynamics for upper limb rehabilitation,” Lecture Notes in Electrical Engineering, vol. 338, pp. 51–60, 2015, https://doi.org/10.1007/978-3-662-46466-3_6.

[21] J. Huang, W. Huo, W. Xu, S. Mohammed, and Y. Amirat, “Control of Upper-Limb Power-Assist Exoskeleton Using a Human-Robot Interface Based on Motion Intention Recognition,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 4, pp. 1257–1270, 2015, https://doi.org/10.1109/TASE.2015.2466634.

[22] M. Witkowski, M. Cortese, M. Cempini, J. Mellinger, N. Vitiello, and S. R. Soekadar, “Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG),” Journal of NeuroEngineering and Rehabilitation, vol. 11, no. 1, pp. 1–6, 2014, https://doi.org/10.1186/1743-0003-11-165.

[23] W. Hassani, S. Mohammed, H. Rifai, and Y. Amirat, “EMG based approach for wearer-centered control of a knee joint actuated orthosis,” IEEE International Conference on Intelligent Robots and Systems, pp. 990–995, 2013, https://doi.org/10.1109/IROS.2013.6696471.

[24] T.D. Lalitharatne, K. Teramoto and Y. Hayashi, K. Tamura, and K. Kazuo, “EEG-based evaluation for perception-assist in upper- limb power-assist exoskeletons,” IEEE Xplore, vol. 1, pp. 1–6, 2014, https://doi.org/10.1109/WAC.2014.6935909.

[25] L. Manoni, C. Turchetti, L. Falaschetti, and P. Crippa, “A comparative study of computational methods for compressed sensing reconstruction of EMG signal,” Sensors (Switzerland), vol. 19, no. 16, 2019, https://doi.org/10.3390/s19163531.

[26] W. Hassani, S. Mohammed, and Y. Amirat, “Real-Time EMG Driven Lower Limb Actuated Orthosis for Assistance As Needed Movement Strategy,” Robotics: Science and Systems, vol. 9, 2016, https://doi.org/10.15607/rss.2013.ix.054.

[27] J. M. P. Gunasekara, R. A. R. C. Gopura, T. S. S. Jayawardane, and S. W. H. M. T. D. Lalitharathne, “Control methodologies for upper limb exoskeleton robots,” 2012 IEEE/SICE International Symposium on System Integration, SII 2012, pp. 19–24, 2012, https://doi.org/10.1109/SII.2012.6427387.

[28] A. Ali, S. F. Ahmed, K. A. Kadir, M. K. Joyo, and R. N. S. Yarooq, “Fuzzy PID controller for upper limb rehabilitation robotic system,” 2018 IEEE International Conference on Innovative Research and Development, ICIRD 2018, pp. 1–5, 2018, https://doi.org/10.1109/ICIRD.2018.8376291.

[29] H. Kazerooni, “The human power amplifier technology at the University of California, Berkeley,” Robotics and Autonomous Systems, vol. 19, pp. 179–187, 1996, https://doi.org/10.1016/s0921-8890(96)00045-0.

[30] K. Kiguchi and Y. Hayashi, “A study of EMG and EEG during perception-assist with an upper-limb power-assist robot,” Proceedings - IEEE International Conference on Robotics and Automation, pp. 2711–2716, 2012, https://doi.org/10.1109/ICRA.2012.6225027.

[31] T. Higuma, K. Kiguchi, and J. Arata, “Low-Profile Two-Degree-of-Freedom Wrist Exoskeleton Device Using Multiple Spring Blades,” IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 305–311, 2018, https://doi.org/10.1109/LRA.2017.2739802.

[32] T. Desplenter, Y. Zhou, B. P. Edmonds, M. Lidka, A. Goldman, and A. L. Trejos, “Rehabilitative and assistive wearable mechatronic upper-limb devices: A review,” Journal of Rehabilitation and Assistive Technologies Engineering, vol. 7, pp. 1–26, 2020, https://doi.org/10.1177/2055668320917870.

[33] B. Brahmi, M. H. Laraki, M. Saad, M. H. Rahman, C. Ochoa-Luna, and A. Brahmi, “Compliant adaptive control of human upper-limb exoskeleton robot with unknown dynamics based on a Modified Function Approximation Technique (MFAT),” Robotics and Autonomous Systems, vol. 117, pp. 92–102, 2019, https://doi.org/10.1016/j.robot.2019.02.017.

[34] M. Li, J. Deng, F. Zha, S. Qiu, and X. Wang, “Motion Intention Estimation for Active Power-Assist Lower Limb Exoskeleton Robot (APAL),” Preprints, pp. 1–20, 2018, https://doi.org/10.20944/preprints201806.0149.v1.

[35] K. Kiguchi and M. Chathuramali, “A Study on Real-Time Detection of Interacting Motion Intention for Perception-Assist with an Upper-Limb Wearable Power-Assist Robot,” Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, pp. 900–905, 2019, https://doi.org/10.1109/SMC.2018.00160.

[36] A. Riani, T. Madani, A. El Hadri, and A. Benallegue, “Adaptive control based on an on-line parameter estimation of an upper limb exoskeleton,” IEEE International Conference on Rehabilitation Robotics, pp. 695–701, 2017, https://doi.org/10.1109/ICORR.2017.8009329.

[37] H. Liu and L. Wang, “Human motion prediction for human-robot collaboration,” Journal of Manufacturing Systems, vol. 44, pp. 287–294, 2017, https://doi.org/10.1016/j.jmsy.2017.04.009.

[38] K. G. M. Chathuramali and K. Kiguchi, “Real-time detection of the interaction between an upper-limb power-assist robot user and another person for perception-assist,” Cognitive Systems Research, vol. 61, pp. 53–63, 2020, https://doi.org/10.1016/j.cogsys.2020.01.002.

[39] H. Seki, K. Ishihara, and S. Tadakuma, “Novel regenerative braking control of electric power-assisted wheelchair for safety downhill road driving,” IEEE Transactions on Industrial Electronics, vol. 56, no. 5, pp. 1393–1400, 2009, https://doi.org/10.1109/TIE.2009.2014747.

[40] T. Kawashima, “Study on intelligent baby carriage with power assist system and comfortable basket,” Journal of Mechanical Science and Technology, vol. 23, no. 4, pp. 974–979, 2009, https://doi.org/10.1007/s12206-009-0324-5.

[41] M. Ding, J. Ueda, and T. Ogasawara, “Pinpointed muscle force control using a power-assisting device: System configuration and experiment,” Proceedings of the 2nd Biennial IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2008, pp. 181–186, 2008, https://doi.org/10.1109/BIOROB.2008.4762829.

[42] K. Kiguchi, Y. Hayashi, and T. Asami, “An upper-limb power-assist robot with tremor suppression control,” IEEE International Conference on Rehabilitation Robotics, pp. 8–11, 2011, https://doi.org/10.1109/ICORR.2011.5975390.

[43] S. M. M. Rahman, R. Ikeura, M. Nobe, and H. Sawai, “Control of a power assist robot for lifting objects based on human operator’s perception of object weight,” Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, pp. 84–90, 2009, https://doi.org/10.1109/ROMAN.2009.5326343.

[44] A.M. Okamura, N. Smaby, and M. R. Cutkosky, “An overview of dexterous manipulation,” Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), vol. 1, pp. 255–262, 2000, http://ieeexplore.ieee.org/document/844067/.

[45] K. Hang, W. G. Bircher, A. S. Morgan, and A. M. Dollar, “Manipulation for self-identification, and self-identification for better manipulation,” Science Robotics, vol. 6, no. 54, pp. 1–12, 2021, https://doi.org/10.1126/scirobotics.abe1321.

[46] M. Liarokapis and A. M. Dollar, “Deriving Dexterous, In-Hand Manipulation Primitives for Adaptive Robot Hands,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, https://doi.org/10.1109/IROS.2017.8206014.

[47] S. M. M. Rahman and R. Ikeura, “International symposium on robotics and intelligent sensors 2012 (IRIS 2012) estimating and validating relationships between actual and perceived weights for lifting objects with a power assist robot: The psychophysical approach,” Procedia Engineering, vol. 41, pp. 685–693, 2012, https://doi.org/10.1016/j.proeng.2012.07.230.

[48] S. M. M. Rahman and R. Ikeura, “Improving interactions between a power-assist robot system and its human user in horizontal transfer of objects using a novel adaptive control method,” Advances in Human-Computer Interaction, vol. 2012, pp. 1–12, 2012, https://doi.org/10.1155/2012/745216.

[49] S. M. M. Rahman, R. Ikeura, M. Nobe, and H. Sawai, “Controlling a power assist robot for lifting objects considering human’s unimanual, bimanual and cooperative weight perception,” Proceedings - IEEE International Conference on Robotics and Automation, pp. 2356–2362, 2010, https://doi.org/10.1109/ROBOT.2010.5509321.

[50] D. J. Reinkensmeyer, “Rehabilitation robot,” Encyclopedia Britannica, 2021, https://doi.org/Https://www.britannica.com/technology/rehabilitation-robot.

[51] Y. Hayashi and K. Kiguchi, “A lower-limb power-assist robot with perception-assist,” IEEE International Conference on Rehabilitation Robotics, 2011, https://doi.org/10.1109/ICORR.2011.5975445.

[52] M. Mansour, “Conceptual Design of EMG Based Upper Limb Power Assist Rehabilitation Device,” Journal of Smart Systems Research (JOINSSR), vol. 2, no. 1, pp. 1–17, 2021, https://doi.org/https://dergipark.org.tr/en/pub/joinssr/issue/64451/980300.

[53] K. Kiguchi, Y. Kose, and Y. Hayashi, “An upper-limb power-assist exoskeleton robot with task-oriented perception-assist,” 2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2010, pp. 88–93, 2010, https://doi.org/10.1109/BIOROB.2010.5626025.

[54] K. Kadota, M. Akai, K. Kawashima, and T. Kagawa, “Development of power-assist robot arm using pneumatic rubber muscles with a balloon sensor,” Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, pp. 546–551, 2009, https://doi.org/10.1109/ROMAN.2009.5326335.

[55] D. P. Losey and M. K. O’Malley, “Learning the Correct Robot Trajectory in Real-Time from Physical Human Interactions,” ACM Transactions on Human-Robot Interaction, vol. 9, no. 1, pp. 1–19, 2020, https://doi.org/10.1145/3354139.

[56] M. S. Erden and T. Tomiyama, “Human-intent detection and physically interactive control of a robot without force sensors,” IEEE Transactions on Robotics, vol. 26, no. 2, pp. 370–382, 2010, https://doi.org/10.1109/TRO.2010.2040202.

[57] D. P. Losey, C. G. McDonald, E. Battaglia, and M. K. O’Malley, “A review of intent detection, arbitration, and communication aspects of shared control for physical human–robot interaction,” Applied Mechanics Reviews, vol. 70, no. 1, 2018, https://doi.org/10.1115/1.4039145.

[58] Z. Liu and J. Hao, “Intention Recognition in Physical Human-Robot Interaction Based on Radial Basis Function Neural Network,” Journal of Robotics, vol. 2019, pp. 1–8, 2019, https://doi.org/10.1155/2019/4141269.

[59] R. M. Singh, S. Chatterji, and A. Kumar, “A review on surface EMG based control schemes of exoskeleton robot in stroke rehabilitation,” Proceedings - 2013 International Conference on Machine Intelligence Research and Advancement, ICMIRA 2013, pp. 310–315, 2014, https://doi.org/10.1109/ICMIRA.2013.65.

[60] N. Karavas, A. Ajoudani, N. Tsagarakis, J. Saglia, A. Bicchi, and D. Caldwell, “Tele-impedance based assistive control for a compliant knee exoskeleton,” Robotics and Autonomous Systems, vol. 73, pp. 78–90, 2015, https://doi.org/10.1016/j.robot.2014.09.027.

[61] V. Khoshdel and A. Akbarzadeh, “An optimized artificial neural network for human-force estimation: consequences for rehabilitation robotics,” Industrial Robot, vol. 45, no. 3, pp. 416–423, 2018, https://doi.org/10.1108/IR-10-2017-0190.

[62] R. A. R. C. Gopura, D. S. V. Bandara, K. Kiguchi, and G. K. I. Mann, “Developments in hardware systems of active upper-limb exoskeleton robots: A review,” Robotics and Autonomous Systems, vol. 75, pp. 203–220, 2016, https://doi.org/10.1016/j.robot.2015.10.001.

[63] W. Huo, S. Mohammed, J. C. Moreno, and Y. Amirat, “Lower Limb Wearable Robots for Assistance and Rehabilitation: A State of the Art,” IEEE Systems Journal, vol. 10, no. 3, pp. 1068–1081, 2016, https://doi.org/10.1109/JSYST.2014.2351491.

[64] F. Bian, R. Li, and P. Liang, “SVM based simultaneous hand movements classification using sEMG signals,” 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017, pp. 427–432, 2017, https://doi.org/10.1109/ICMA.2017.8015855.

[65] P. Xia, J. Hu, and Y. Peng, “EMG-Based Estimation of Limb Movement Using Deep Learning With Recurrent Convolutional Neural Networks,” Artificial Organs, vol. 42, no. 5, pp. 67–77, 2018, https://doi.org/10.1111/aor.13004.

[66] K. Asai and N. Takase, “Finger motion estimation based on frequency conversion of EMG signals and image recognition using convolutional neural network,” International Conference on Control, Automation and Systems, pp. 1366–1371, 2017, https://doi.org/10.23919/ICCAS.2017.8204206.

[67] S. Huanghuan, S. Quanjun, D. Xiaohong, Z. Yibo, Y. Yong, and G. Yunjian, “Recognition of phases in sit-to-stand motion by Neural Network Ensemble (NNE) for power assist robot,” 2007 IEEE International Conference on Robotics and Biomimetics, ROBIO, pp. 1703–1708, 2007, https://doi.org/10.1109/ROBIO.2007.4522422.

[68] L. Zhang, G. Liu, B. Han, Z. Wang, and T. Zhang, “SEMG Based Human Motion Intention Recognition,” Journal of Robotics, vol. 2019, pp. 1–12, 2019, https://doi.org/10.1155/2019/3679174.

[69] K. Kiguchi, S. Kariya, K. Watanabe, K. Izumi, and T. Fukuda, “An exoskeletal robot for human elbow motion support - Sensor fusion, adaptation, and control,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 31, no. 3, pp. 353–361, 2001, https://doi.org/10.1109/3477.931520.

[70] F. Dominici, T. Popa, F. Ginanneschi, R. Mazzocchio, and A. Rossi, “Cortico-motoneuronal output to intrinsic hand muscles is differentially influenced by static changes in shoulder positions,” Experimental Brain Research, vol. 164, no. 4, pp. 500–504, 2005, https://doi.org/10.1007/s00221-005-2270-5.

[71] K. Kiguchi, “A study on EMG-based human motion prediction for power assist exoskeletons,” Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007, pp. 190–195, 2007, https://doi.org/10.1109/CIRA.2007.382917.

[72] S. Struk, N. Correia, Y. Guenane, M. Revol, and S. Cristofari, “Full-thickness skin grafts for lower leg defects coverage: Interest of postoperative immobilization,” Annales de Chirurgie Plastique Esthetique, vol. 63, no. 3, pp. 229–233, 2018, https://doi.org/10.1016/j.anplas.2017.08.003.

[73] D. Staudenmann, K. Roeleveld, D. F. Stegeman, and J. H. van Dieen, “Methodological aspects of SEMG recordings for force estimation - A tutorial and review,” Journal of Electromyography and Kinesiology, vol. 20, no. 3, pp. 375–387, 2010, https://doi.org/10.1016/j.jelekin.2009.08.005.

[74] C. W. Antuvan, “Decoding Human Motion Intention using Myoelectric Signals for Assistive Technologies,” DR-NTU, pp. 1–146, 2019, https://dr.ntu.edu.sg.

[75] G. Bin Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006, https://doi.org/10.1016/j.neucom.2005.12.126.

[76] G. Huang, G. Bin Huang, S. Song, and K. You, “Trends in extreme learning machines: A review,” Neural Networks, vol. 61, pp. 32–48, 2015, https://doi.org/10.1016/j.neunet.2014.10.001.

[77] S. Atsawaraungsuk and P. Horata, “Evolutionary circular-ELM for the reduced-reference assessment of perceived image quality,” Lecture Notes in Electrical Engineering, vol. 339, pp. 657–664, 2015, https://doi.org/10.1007/978-3-662-46578-3_77.

[78] S. Poria, E. Cambria, A. Gelbukh, F. Bisio, and A. Hussain, “Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns,” IEEE Computational Intelligence Magazine, vol. 10, no. 4, pp. 26–36, 2015, https://doi.org/10.1109/MCI.2015.2471215.

[79] E. Principi, S. Squartini, E. Cambria, and F. Piazza, “Acoustic template-matching for automatic emergency state detection: An ELM based algorithm,” Neurocomputing, vol. 149, pp. 426–434, 2015, https://doi.org/10.1016/j.neucom.2014.01.067.

[80] S. Poria, E. Cambria, A. Hussain, and G. Bin Huang, “Towards an intelligent framework for multimodal affective data analysis,” Neural Networks, vol. 63, pp. 104–116, 2015, https://doi.org/10.1016/j.neunet.2014.10.005.

[81] R. Savitha, S. Suresh, and H. J. Kim, “A Meta-Cognitive Learning Algorithm for an Extreme Learning Machine Classifier,” Cognitive Computation, vol. 6, no. 2, pp. 253–263, 2014, https://doi.org/10.1007/s12559-013-9223-2.

[82] E. Cambria, P. Gastaldo, F. Bisio, and R. Zunino, “An ELM-based model for affective analogical reasoning,” Neurocomputing, vol. 149, pp. 443–455, 2015, https://doi.org/10.1016/j.neucom.2014.01.064.

[83] X. Tu, J. Huang, L. Yu, Q. Xu, and J. He, “Design of a wearable rehabilitation robot integrated with functional electrical stimulation,” Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 1555–1560, 2012, https://doi.org/10.1109/BioRob.2012.6290720.

[84] A. M. Khan, F. Khan, and C. Han, “Estimation of desired motion intention using extreme learning machine for upper limb assist exoskeleton,” IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, pp. 919–923, 2016, https://doi.org/10.1109/AIM.2016.7576886.

[85] J. Lee, M. Kim, H. Ko, and K. Kim, “A control method of power-assisted robot for upper limb considering intention-based motion by using sEMG signal,” 2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2014, pp. 385–390, 2014, https://doi.org/10.1109/URAI.2014.7057374.

[86] L. Xing, X. Wang, and J. Wang, “A motion intention-based upper limb rehabilitation training system to stimulate motor nerve through virtual reality,” International Journal of Advanced Robotic Systems, vol. 14, no. 6, pp. 1–8, 2017, https://doi.org/10.1177/1729881417743283.

[87] Y. Zhuang, S. Yao, C. Ma, and R. Song, “Admittance Control Based on EMG-Driven Musculoskeletal Model Improves the Human-Robot Synchronization,” IEEE Transactions on Industrial Informatics, vol. 15, no. 2, pp. 1211–1218, 2019, https://doi.org/10.1109/TII.2018.2875729.

[88] Y. Li and S. S. Ge, “Human-robot collaboration based on motion intention estimation,” IEEE/ASME Transactions on Mechatronics, vol. 19, no. 3, pp. 1007–1014, 2014, https://doi.org/10.1109/TMECH.2013.2264533.

[89] C. Wang, W., Zhang, J., Kong, D., Su, S., Yuan, X., & Zhao, “Research on control method of upper limb exoskeleton based on mixed perception model,” Robotica, pp. 1–17, 2022, https://doi.org/doi:10.1017/S0263574722000480.

[90] J. Lee, M. Kim, and K. Kim, “A control scheme to minimize muscle energy for power assistant robotic systems under unknown external perturbation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 12, pp. 2313–2327, 2017, https://doi.org/10.1109/TNSRE.2017.2723609.

[91] S. Nomura, Y. Takahashi, M. K. K. Sahashi, S. Murai, and Y. Taniai, and T. Naniwa, “Power assist control based on human motion estimation using motion sensors for powered exoskeleton without binding legs,” Applied Sciences (Switzerland), vol. 9, no. 1, pp. 14–16, 2019, https://doi.org/10.3390/app9010164.

[92] Z. Tang, K. Zhang, S. Sun, Z. Gao, L. Zhang, and Z. Yang, “An upper-limb power-assist exoskeleton using proportional myoelectric control,” Sensors (Switzerland), vol. 14, no. 4, pp. 6677–6694, 2014, https://doi.org/10.3390/s140406677.

[93] Y. Zeng, J. Yang, and Y. Yin, “Gaussian process-integrated state space model for continuous joint angle prediction from EMG and interactive force in a Human-Exoskeleton System,” Applied Sciences (Switzerland), vol. 9, no. 8, 2019, https://doi.org/10.3390/app9081711.

[94] Q. Yang, C. Xie, R. Tang, H. Liu, and R. Song, “Hybrid active control with human intention detection of an upper-limb cable-driven rehabilitation robot,” IEEE Access, vol. 8, pp. 195206–195215, 2020, https://doi.org/10.1109/ACCESS.2020.3033301.

[95] W. Wang, L. Qin, X. Yuan, X. Ming, T. Sun, and Y. Liu, “Bionic control of exoskeleton robot based on motion intention for rehabilitation training,” Advanced Robotics, vol. 33, no. 12, pp. 590–601, 2019, https://doi.org/10.1080/01691864.2019.1621774.

[96] J. Huang, W. Huo, W. Xu, S. Mohammed, and Y. Amirat, “Control of Upper-Limb Power-Assist Exoskeleton Using a Human-Robot Interface Based on Motion Intention Recognition,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 4, pp. 1257–1270, 2015, https://doi.org/10.1109/TASE.2015.2466634.

[97] C. Yang, C. Chen, W. He, R. Cui, and Z. Li, “Robot Learning System Based on Adaptive Neural Control and Dynamic Movement Primitives,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 3, pp. 777–787, 2019, https://doi.org/10.1109/TNNLS.2018.2852711.

[98] A. Karamali Ravandi, E. Khanmirza, and K. Daneshjou, “Hybrid force/position control of robotic arms manipulating in uncertain environments based on adaptive fuzzy sliding mode control,” Applied Soft Computing Journal, vol. 70, pp. 864–874, 2018, https://doi.org/10.1016/j.asoc.2018.05.048.

[99] G. Liu and L. Fang, “Frequency-division based hybrid force / position control of robotic arms manipulating in uncertain environments,” Industrial Robot: The international journal of robotics research and application, vol. 3, pp. 445–452, 2020, https://doi.org/10.1108/IR-11-2019-0228.

[100] S. A. M. Dehghan, M. Danesh, and F. Sheikholeslam, “Adaptive hybrid force/position control of robot manipulators using an adaptive force estimator in the presence of parametric uncertainty,” Advanced Robotics, vol. 29, no. 4, pp. 209–223, 2015, https://doi.org/10.1080/01691864.2014.985609.


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International Journal of Robotics and Control Systems
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