The realization of robotic neurorehabilitation in clinical: use of computational intelligence and future prospects analysis
暂无分享,去创建一个
Wei Wang | Jiali Yang | Zhiqi Zhao | Chenzhen Du | Qin Peng | Juhui Qiu | Guixue Wang | Guixue Wang | J. Qiu | Chenzhen Du | Jiali Yang | Wei Wang | Qin Peng | Zhiqi Zhao
[1] Shahid Hussain,et al. State-of-the-art robotic gait rehabilitation orthoses: design and control aspects. , 2014, NeuroRehabilitation.
[2] J. A. Cabrera,et al. Multiobjective constrained optimal synthesis of planar mechanisms using a new evolutionary algorithm , 2007 .
[3] C. Srirat,et al. Association between stroke knowledge, stroke awareness, and preventive behaviors among older people: A cross sectional study. , 2019, Nursing & health sciences.
[4] Liang Su,et al. Intelligent Medical Rehabilitation Training Instrument Based on Movement Coordination , 2020, IEEE Access.
[5] Xin-Jun Liu,et al. Kinematics, dynamics and dimensional synthesis of a novel 2-DoF translational manipulator , 2005, J. Intell. Robotic Syst..
[6] S. Carmichael,et al. Plasticity of Cortical Projections after Stroke , 2003, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[7] Robert Riener,et al. Control Strategies and Artificial Intelligence in Rehabilitation Robotics , 2015, AI Mag..
[8] D. Leifer. Neuronal Plasticity and Recovery from Stroke , 1998 .
[9] Andrew J. McDaid,et al. Design, Analysis, and Multicriteria Optimization of an Overground Pediatric Robotic Gait Trainer , 2017, IEEE/ASME Transactions on Mechatronics.
[10] Noor Azah Abd Aziz,et al. Exploring views on long term rehabilitation for people with stroke in a developing country: findings from focus group discussions , 2014, BMC Health Services Research.
[11] Joel Stein,et al. The use of robots in stroke rehabilitation: A narrative review. , 2018, NeuroRehabilitation.
[12] H. Su,et al. A Polynomial Homotopy Formulation of the Inverse Static Analysis of Planar Compliant Mechanisms , 2006 .
[13] Guoli Zhu,et al. Reviewing Clinical Effectiveness of Active Training Strategies of Platform-Based Ankle Rehabilitation Robots , 2018, Journal of healthcare engineering.
[14] Med Amine Laribi,et al. Design study of a cable-based gait training machine , 2017 .
[15] Ezequiel López-Rubio,et al. Computational Intelligence Techniques in Medicine , 2015, Comput. Math. Methods Medicine.
[16] Toshio Tsuji,et al. A human-assisting manipulator teleoperated by EMG signals and arm motions , 2003, IEEE Trans. Robotics Autom..
[17] Shahid Hussain,et al. Design optimization of a cable actuated parallel ankle rehabilitation robot: A fuzzy based multi-objective evolutionary approach , 2016, J. Intell. Fuzzy Syst..
[18] Qingsong Ai,et al. KUKA Real-Time Control through Angle Estimation of Wrist from sEMG with Support Vector Regression , 2018, CSAI '18.
[19] Nianfeng Wang,et al. Spring-joint method for topology optimization of planar passive compliant mechanisms , 2013 .
[20] Xiaojun Yang,et al. Driving torque reduction in linkage mechanisms using joint compliance for robot head , 2015 .
[21] Huosheng Hu,et al. Myoelectric control systems - A survey , 2007, Biomed. Signal Process. Control..
[22] Zahari Taha,et al. A hybrid active force control of a lower limb exoskeleton for gait rehabilitation , 2018, Biomedizinische Technik. Biomedical engineering.
[23] Jian S. Dai,et al. Orientation and Workspace Analysis of the Multifingered Metamorphic Hand—Metahand , 2009, IEEE Transactions on Robotics.
[24] Vineet Kumar,et al. Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator , 2014, Expert Syst. Appl..
[25] Jens Bo Nielsen,et al. Science-Based Neurorehabilitation: Recommendations for Neurorehabilitation From Basic Science , 2015, Journal of motor behavior.
[26] R. Riener,et al. Towards more effective robotic gait training for stroke rehabilitation: a review , 2012, Journal of NeuroEngineering and Rehabilitation.
[27] Shahid Hussain,et al. Three-Stage Design Analysis and Multicriteria Optimization of a Parallel Ankle Rehabilitation Robot Using Genetic Algorithm , 2015, IEEE Transactions on Automation Science and Engineering.
[28] M. Rausch,et al. Recovery and brain reorganization after stroke in adult and aged rats , 2005, Annals of neurology.
[29] Millie Pant,et al. An efficient Differential Evolution based algorithm for solving multi-objective optimization problems , 2011, Eur. J. Oper. Res..
[30] Marcelo C. M. Teixeira,et al. Electrical stimulation tracking control for paraplegic patients using T-S fuzzy models , 2017, Fuzzy Sets Syst..
[31] Isao Shimoyama,et al. A skeletal framework artificial hand actuated by pneumatic artificial muscles , 1998 .
[32] Claysson Bruno Santos Vimieiro,et al. Effectiveness of robot therapy on body function and structure in people with limited upper limb function: A systematic review and meta-analysis , 2018, PloS one.
[33] Amit Kumar Singh,et al. A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path , 2017, Sādhanā.
[34] Rong Yang,et al. sEMG-based shoulder-elbow composite motion pattern recognition and control methods for upper limb rehabilitation robot , 2019, Assembly Automation.
[35] P. Langhorne,et al. Stroke rehabilitation , 2011, The Lancet.
[36] Maureen K. Holden,et al. Virtual Environments for Motor Rehabilitation: Review , 2005, Cyberpsychology Behav. Soc. Netw..
[37] Boubaker Daachi,et al. On the robust PID adaptive controller for exoskeletons: A particle swarm optimization based approach , 2017, Appl. Soft Comput..
[38] Hermano Igo Krebs,et al. Rehabilitation Robotics: Performance-Based Progressive Robot-Assisted Therapy , 2003, Auton. Robots.
[39] A. Cristian,et al. Patient safety and quality improvement in rehabilitation medicine. , 2012, Physical medicine and rehabilitation clinics of North America.
[40] Ramanpreet Singh,et al. A novel gait-based synthesis procedure for the design of 4-bar exoskeleton with natural trajectories , 2018, Journal of orthopaedic translation.
[41] Adel Akbarimajd,et al. Intelligent Control Method of a 6-DOF parallel robot Used for Rehabilitation Treatment in lower limbs , 2016 .
[42] Rory C. Flemmer,et al. A review of artificial intelligence , 2000, 2009 4th International Conference on Autonomous Robots and Agents.
[43] Carlos Balaguer,et al. Robotics in Health Care: Perspectives of Robot-Aided Interventions in Clinical Practice for Rehabilitation of Upper Limbs , 2019, Applied Sciences.
[44] Robert Riener,et al. Controlling patient participation during robot-assisted gait training , 2011, Journal of NeuroEngineering and Rehabilitation.
[45] Graham Parker. Are robots smart , 2001 .
[46] Manukid Parnichkun,et al. Haptics control of an arm exoskeleton for virtual reality using PSO-based fixed structure H∞ control , 2019 .
[47] Mustafa Sinasi Ayas,et al. Fractional order based trajectory tracking control of an ankle rehabilitation robot , 2018, Trans. Inst. Meas. Control.
[48] Congzhe Wang,et al. Multi-objective optimization of a parallel ankle rehabilitation robot using modified differential evolution algorithm , 2015 .
[49] Xin-She Yang,et al. Computational Intelligence and Metaheuristic Algorithms with Applications , 2014, TheScientificWorldJournal.
[50] A. A. Pasha Zanoosi,et al. Investigation of the effects of human body stability on joint angles’ prediction , 2015 .
[51] Di Shi,et al. A Review on Lower Limb Rehabilitation Exoskeleton Robots , 2019, Chinese Journal of Mechanical Engineering.
[52] Thanh-Phong Dao,et al. Design and Optimization for a New Compliant Planar Spring of Upper Limb Assistive Device Using Hybrid Approach of RSM–FEM and MOGA , 2019, Arabian Journal for Science and Engineering.
[53] Antonio Frisoli,et al. Convex polygon fitting in robot-based neurorehabilitation , 2018, Appl. Soft Comput..
[54] D. Cadilhac,et al. Rehabilitation assessments for patients with stroke in Australian hospitals do not always reflect the patients' rehabilitation requirements. , 2015, Archives of physical medicine and rehabilitation.
[55] Antonio Frisoli,et al. A Linear Approach to Optimize an EMG-Driven Neuromusculoskeletal Model for Movement Intention Detection in Myo-Control: A Case Study on Shoulder and Elbow Joints , 2018, Front. Neurorobot..
[56] E. Roth,et al. 3. Rehabilitation evaluation and management , 1994 .
[57] Cheng Wu,et al. Trends in In-Hospital Mortality among Patients with Stroke in China , 2014, PloS one.
[58] Wenjie Ling,et al. Lower Limb Exercise Rehabilitation Assessment Based on Artificial Intelligence and Medical Big Data , 2019, IEEE Access.
[59] Jinghui Cao,et al. Reviewing high-level control techniques on robot-assisted upper-limb rehabilitation , 2018, Adv. Robotics.
[60] Maarten J. IJzerman,et al. Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. , 2006, Journal of rehabilitation research and development.
[61] Doo Han Yoo,et al. Effects of upper limb robot-assisted therapy in the rehabilitation of stroke patients , 2015, Journal of physical therapy science.
[62] Min Song,et al. Adopting Text Mining on Rehabilitation Therapy Repositioning for Stroke , 2019, Front. Neuroinform..
[63] N. Hogan,et al. Is robot-aided sensorimotor training in stroke rehabilitation a realistic option? , 2001, Current opinion in neurology.
[64] Haiqing Zheng,et al. SVM-Based Classification of sEMG Signals for Upper-Limb Self-Rehabilitation Training , 2019, Front. Neurorobot..
[65] G. Morone,et al. Robotic Technologies and Rehabilitation: New Tools for Stroke Patients' Therapy , 2013, BioMed research international.
[66] Jacek S. Tutak. Virtualna stvarnost i vježbe za gornji ud zahvaćen paralizom za preživjele nakon moždanog udara , 2017 .
[67] Kanendra Naidu,et al. Optimized Proportional-Integral-Derivative Controller for Upper Limb Rehabilitation Robot , 2019, Electronics.
[68] Agnes Roby-Brami,et al. Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies , 2016, IEEE Reviews in Biomedical Engineering.
[69] Paulo J. G. Lisboa,et al. A review of evidence of health benefit from artificial neural networks in medical intervention , 2002, Neural Networks.
[70] Shahid Hussain,et al. Review on design and control aspects of ankle rehabilitation robots , 2015, Disability and rehabilitation. Assistive technology.
[71] Jing Wang,et al. Hand Rehabilitation Robotics on Poststroke Motor Recovery , 2017, Behavioural neurology.
[72] Bing Chen,et al. Recent developments and challenges of lower extremity exoskeletons , 2015, Journal of orthopaedic translation.
[73] S. K. Lakshmanaprabu,et al. Multiloop FOPID Controller Design for TITO Process Using Evolutionary Algorithm , 2019, International Journal of Energy Optimization and Engineering.
[74] Shahid Hussain,et al. Multicriteria Design Optimization of a Parallel Ankle Rehabilitation Robot: Fuzzy Dominated Sorting Evolutionary Algorithm Approach , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[75] Dan Zhang,et al. Design and Optimization of a Hybrid-Driven Waist Rehabilitation Robot , 2016, Sensors.
[76] Vicente Feliú Batlle,et al. Fractional order control strategies for power electronic buck converters , 2006, Signal Process..
[77] Shane Xie,et al. A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot , 2017, Journal of healthcare engineering.
[78] Zhou Wan,et al. The Recognition of Motion Intention of Knee Joint Based on Piezoelectric Signals , 2019 .
[79] G. Allison,et al. Optimal dimensional synthesis of a symmetrical five-bar planar upper-extremity neuromotor device , 2015 .
[80] Zhe Wang,et al. sEMG Based Human Motion Intention Recognition , 2019, J. Robotics.
[81] M. Merrick. Secondary injury after musculoskeletal trauma: a review and update. , 2002, Journal of athletic training.
[82] Robert Riener,et al. To integrate and to empower: Robots for rehabilitation and assistance , 2017, Science Robotics.
[83] G. Rosati. The place of robotics in post-stroke rehabilitation , 2010, Expert review of medical devices.
[84] Thamer Alhussain,et al. Medical Big Data Analysis Using Big Data Tools and Methods , 2018 .
[85] Yoshihiko Nakamura,et al. Motion capture based human motion recognition and imitation by direct marker control , 2008, Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots.
[86] P. Gallina,et al. Design, Implementation and Clinical Tests of a Wire-Based Robot for Neurorehabilitation , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[87] Xin-She Yang,et al. Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).
[88] G.R. Vossoughi,et al. The Control of an Exoskeleton and The Reduction of Interaction Force Using Human Intent Detection by EMG Signals and Torque Estimation , 2018, 2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM).
[89] Qiang Luo,et al. Adaptive Sliding Mode Control of Functional Electrical Stimulation (FES) for Tracking Knee Joint Movement , 2017, 2017 10th International Symposium on Computational Intelligence and Design (ISCID).
[90] Christian Blum,et al. Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.
[91] Wei Li,et al. Big Health Application System based on Health Internet of Things and Big Data , 2017, IEEE Access.
[92] Wei Li,et al. Bifurcation control of a generalized VDP system driven by color-noise excitation via FOPID controller , 2019, Chaos, Solitons & Fractals.
[93] Hui Liang,et al. Upper limb rehabilitation using robotic exoskeleton systems: a systematic review , 2018, International Journal of Intelligent Robotics and Applications.
[94] Mubarak Shah,et al. Motion-based recognition a survey , 1995, Image Vis. Comput..
[95] Bin Zi,et al. Design and analysis of a pneumatic muscle driven parallel mechanism for imitating human pelvis , 2014 .
[96] K. Nas,et al. The relationship between physical impairment and disability during stroke rehabilitation: effect of cognitive status , 2004, International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation.
[97] Long Cheng,et al. An sEMG-driven neuromusculoskeletal model of upper limb for rehabilitation robot control , 2016, 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO).
[98] H. Krebs,et al. Effects of Robot-Assisted Therapy on Upper Limb Recovery After Stroke: A Systematic Review , 2008, Neurorehabilitation and neural repair.
[99] A. Houtrow,et al. Meeting the Growing Need for Pediatric Rehabilitation Medicine Physicians. , 2016, Archives of physical medicine and rehabilitation.
[100] Giuseppe Carbone,et al. On the Optimal Design of Cable Driven Parallel Robot with a Prescribed Workspace for Upper Limb Rehabilitation Tasks , 2019, Journal of Bionic Engineering.
[101] N. Hogan,et al. Robot-aided neurorehabilitation. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[102] Robert Riener,et al. Virtual reality for enhancement of robot-assisted gait training in children with central gait disorders. , 2011, Journal of rehabilitation medicine.
[103] Antonio Frisoli,et al. Design and Evaluation of a Novel 5 DoF Underactuated Thumb-Exoskeleton , 2018, IEEE Robotics and Automation Letters.
[104] Jian S. Dai,et al. Control Strategies for Patient-Assisted Training Using the Ankle Rehabilitation Robot (ARBOT) , 2013, IEEE/ASME Transactions on Mechatronics.
[105] S. Carmichael,et al. Molecular, cellular and functional events in axonal sprouting after stroke , 2017, Experimental Neurology.
[106] RAMANPREET SINGH,et al. A NOVEL GAIT-INSPIRED FOUR-BAR LOWER LIMB EXOSKELETON TO GUIDE THE WALKING MOVEMENT , 2019, Journal of Mechanics in Medicine and Biology.
[107] Mustafa Sinasi Ayas,et al. Fuzzy logic based adaptive admittance control of a redundantly actuated ankle rehabilitation robot , 2017 .
[108] Xiang Zhongxia,et al. Conceptual design and dimensional synthesis of cam-linkage mechanisms for gait rehabilitation , 2016 .
[109] Mohamed Abderrahim,et al. Dynamic biomechanical model for assessing and monitoring robot-assisted upper-limb therapy. , 2007, Journal of rehabilitation research and development.
[110] Javad Enferadi,et al. On the position analysis of a new spherical parallel robot with orientation applications , 2016 .
[111] C. Neuper,et al. Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges , 2010, Front. Neurosci..