Non-invasive control interfaces for intention detection in active movement-assistive devices

AbstractActive movement-assistive devices aim to increase the quality of life for patients with neuromusculoskeletal disorders. This technology requires interaction between the user and the device through a control interface that detects the user’s movement intention. Researchers have explored a wide variety of invasive and non-invasive control interfaces. To summarize the wide spectrum of strategies, this paper presents a comprehensive review focused on non-invasive control interfaces used to operate active movement-assistive devices. A novel systematic classification method is proposed to categorize the control interfaces based on: (I) the source of the physiological signal, (II) the physiological phenomena responsible for generating the signal, and (III) the sensors used to measure the physiological signal. The proposed classification method can successfully categorize all the existing control interfaces providing a comprehensive overview of the state of the art. Each sensing modality is briefly described in the body of the paper following the same structure used in the classification method. Furthermore, we discuss several design considerations, challenges, and future directions of non-invasive control interfaces for active movement-assistive devices.

[1]  Thierry Dutoit,et al.  Control of a lower limb active prosthesis with eye movement sequences , 2011, 2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB).

[2]  R. Goebel,et al.  A Real-Time fMRI-Based Spelling Device Immediately Enabling Robust Motor-Independent Communication , 2012, Current Biology.

[3]  Junuk Chu,et al.  Wearable EMG-based HCI for Electric-Powered Wheelchair Users with Motor Disabilities , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[4]  C. Clayton,et al.  Palatal tongue controller , 1992 .

[5]  Xueliang Huo,et al.  A Magneto-Inductive Sensor Based Wireless Tongue-Computer Interface , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Kazuhiko Sagara,et al.  Evaluation of a 2-Channel NIRS-Based Optical Brain Switch for Motor Disabilities' Communication Tools , 2012, IEICE Trans. Inf. Syst..

[7]  H. Herr,et al.  Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  Takakazu Ishimatsu,et al.  Muscle stiffness sensor to control an assistance device for the disabled , 2004, Artificial Life and Robotics.

[9]  Maysam Ghovanloo,et al.  Evaluation of a wireless wearable tongue–computer interface by individuals with high-level spinal cord injuries , 2010, Journal of neural engineering.

[10]  Michele Gabrio Antonelli,et al.  Use of MMG Signals for the Control of Powered Orthotic Devices: Development of a Rectus Femoris Measurement Protocol , 2009, Assistive technology : the official journal of RESNA.

[11]  R Seliktar,et al.  Towards the control of a powered orthosis for people with muscular dystrophy , 2001, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[12]  W. Craelius,et al.  Pressure signature of forearm as predictor of grip force. , 2008, Journal of rehabilitation research and development.

[13]  Jacqueline S. Hebert,et al.  Novel Targeted Sensory Reinnervation Technique to Restore Functional Hand Sensation After Transhumeral Amputation , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Sunil Agrawal,et al.  Series elastic actuator control of a powered exoskeleton , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  J.A. Flint,et al.  Biomimetic finger control by filtering of distributed forelimb pressures , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  M. Ghovanloo,et al.  The Tongue Enables Computer and Wheelchair Control for People with Spinal Cord Injury , 2013, Science Translational Medicine.

[17]  Soo-Young Lee,et al.  Brain–computer interface using fMRI: spatial navigation by thoughts , 2004, Neuroreport.

[18]  Francois Routhier,et al.  Evaluation of the JACO robotic arm: Clinico-economic study for powered wheelchair users with upper-extremity disabilities , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[19]  José del R. Millán,et al.  Recent and Upcoming BCI progress: Overview, Analysis, and Recommendations , 2012 .

[20]  Trent J. Bradberry,et al.  Reconstructing Three-Dimensional Hand Movements from Noninvasive Electroencephalographic Signals , 2010, The Journal of Neuroscience.

[21]  A O Posatskiy,et al.  Design and evaluation of a novel microphone-based mechanomyography sensor with cylindrical and conical acoustic chambers. , 2012, Medical engineering & physics.

[22]  S. P. Levine,et al.  Voice control of a powered wheelchair , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  Khairul Anam,et al.  Active Exoskeleton Control Systems: State of the Art , 2012 .

[24]  Fan Zhang,et al.  Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion , 2011, IEEE Transactions on Biomedical Engineering.

[25]  H.T. Nguyen,et al.  Wireless Real-Time Head Movement System Using a Personal Digital Assistant (PDA) for Control of a Power Wheelchair , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[26]  Andreas Attenberger,et al.  Prostheses Control with Combined Near-Infrared and Myoelectric Signals , 2011, EUROCAST.

[27]  T. Chau,et al.  Towards a system-paced near-infrared spectroscopy brain–computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state , 2011, Journal of neural engineering.

[28]  Levi J. Hargrove,et al.  Real-Time and Offline Performance of Pattern Recognition Myoelectric Control Using a Generic Electrode Grid With Targeted Muscle Reinnervation Patients , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[29]  Erik Scheme,et al.  Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. , 2011, Journal of rehabilitation research and development.

[30]  Dan Simon,et al.  INERTIAL THIGH ANGLE SENSING FOR A SEMI-ACTIVE KNEE PROSTHESIS , 2012 .

[31]  H.J.A. Stuyt,et al.  Cost-savings and economic benefits due to the assistive robotic manipulator (ARM) , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[32]  Arno H. A. Stienen,et al.  Design and control of an experimental active elbow support for adult Duchenne Muscular Dystrophy patients , 2014, 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics.

[33]  Jing-Yi Guo,et al.  Sonomyography (SMG) control for powered prosthetic hand: a study with normal subjects. , 2010, Ultrasound in medicine & biology.

[34]  Raul Gonzalez Lima,et al.  A proposal to monitor muscle contraction through the change of electrical impedance inside a muscle , 2014, 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics.

[35]  Joel C. Perry,et al.  Real-Time Myoprocessors for a Neural Controlled Powered Exoskeleton Arm , 2006, IEEE Transactions on Biomedical Engineering.

[36]  W W Abbott,et al.  Ultra-low-cost 3D gaze estimation: an intuitive high information throughput compliment to direct brain–machine interfaces , 2012, Journal of neural engineering.

[37]  Tom Chau,et al.  A self-contained, mechanomyography-driven externally powered prosthesis. , 2005, Archives of physical medicine and rehabilitation.

[38]  T. Kuiken,et al.  Neural Interfaces for Control of Upper Limb Prostheses: The State of the Art and Future Possibilities , 2011, PM & R : the journal of injury, function, and rehabilitation.

[39]  M. Nuttin,et al.  A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots , 2008, Clinical Neurophysiology.

[40]  Claudio Castellini,et al.  Using a high spatial resolution tactile sensor for intention detection , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

[41]  M. Casadio,et al.  Body machine interface: Remapping motor skills after spinal cord injury , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[42]  Yoshiaki Hayashi,et al.  Towards Hybrid EEG-EMG-Based Control Approaches to be Used in Bio-robotics Applications: Current Status, Challenges and Future Directions , 2013, Paladyn J. Behav. Robotics.

[43]  Eric Monacelli,et al.  Force controlled upper-limb powered exoskeleton for rehabilitation , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[44]  Lotte N. S. Andreasen Struijk,et al.  An Inductive Tongue Computer Interface for Control of Computers and Assistive Devices , 2006, IEEE Transactions on Biomedical Engineering.

[45]  Hugh Herr,et al.  User-adaptive control of a magnetorheological prosthetic knee , 2003, Ind. Robot.

[46]  Alexandre Balbinot,et al.  Prototype for Managing the Wheelchair Movements by Accelerometry , 2011 .

[47]  Carlo Menon,et al.  Assisting drinking with an affordable BCI-controlled wearable robot and electrical stimulation: a preliminary investigation , 2014, Journal of NeuroEngineering and Rehabilitation.

[48]  Bo Bentsen,et al.  Fully integrated wireless inductive tongue computer interface for disabled people , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[49]  D Howard,et al.  Dimensional change in muscle as a control signal for powered upper limb prostheses: a pilot study. , 1999, Medical engineering & physics.

[50]  Bettina Sorger,et al.  Another kind of 'BOLD Response': answering multiple-choice questions via online decoded single-trial brain signals. , 2009, Progress in brain research.

[51]  José del R. Millán,et al.  Brain-Computer Interfaces , 2020, Handbook of Clinical Neurology.

[52]  Alfredo Gardel Vicente,et al.  Commands generation by face movements applied to the guidance of a wheelchair for handicapped people , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[53]  Niels Birbaumer,et al.  Hemodynamic brain-computer interfaces for communication and rehabilitation , 2009, Neural Networks.

[54]  Øyvind Stavdahl,et al.  Upper Limb Prosthetic Outcome Measures (ULPOM): A Working Group and Their Findings , 2009 .

[55]  Panagiotis Artemiadis,et al.  Embedded Human Control of Robots Using Myoelectric Interfaces , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[56]  Hung T. Nguyen,et al.  Neural network control of wheelchairs using telemetric head movement , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[57]  K. Abbruzzese,et al.  An innovative design for an Assistive Arm Orthosis for stroke and muscle dystrophy , 2011, 2011 IEEE 37th Annual Northeast Bioengineering Conference (NEBEC).

[58]  D. Childress,et al.  An analysis of extended physiological proprioception as a prosthesis-control technique. , 1984, Journal of rehabilitation research and development.

[59]  Takashi Komeda,et al.  Development of a grip aid system using air cylinders , 2009, 2009 IEEE International Conference on Robotics and Automation.

[60]  M. Nilsson,et al.  The Soft Extra Muscle system for improving the grasping capability in neurological rehabilitation , 2012, 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences.

[61]  Robert D. Lipschutz,et al.  Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. , 2009, JAMA.

[62]  Nicola Vitiello,et al.  Intention-Based EMG Control for Powered Exoskeletons , 2012, IEEE Transactions on Biomedical Engineering.

[63]  Robert D. Lipschutz,et al.  Robotic leg control with EMG decoding in an amputee with nerve transfers. , 2013, The New England journal of medicine.

[64]  Kimberly A. Ingraham,et al.  Powered prosthesis control during walking, sitting, standing, and non-weight bearing activities using neural and mechanical inputs , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[65]  Michael L Boninger,et al.  Recent trends in assistive technology for mobility , 2012, Journal of NeuroEngineering and Rehabilitation.

[66]  Lauren H Smith,et al.  A comparison of the real-time controllability of pattern recognition to conventional myoelectric control for discrete and simultaneous movements , 2012, Journal of NeuroEngineering and Rehabilitation.

[67]  Panagiotis K. Artemiadis,et al.  EMG-Based Control of a Robot Arm Using Low-Dimensional Embeddings , 2010, IEEE Transactions on Robotics.

[68]  Lotte N. S. Andreasen Struijk,et al.  Alternative design of inductive pointing device for oral interface for computers and wheelchairs , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[69]  Robert E. Kass,et al.  Comparison of brain–computer interface decoding algorithms in open-loop and closed-loop control , 2010, Journal of Computational Neuroscience.

[70]  Zoran Nenadic,et al.  Brain-computer interface controlled robotic gait orthosis , 2012, Journal of NeuroEngineering and Rehabilitation.

[71]  Mandayam A. Srinivasan,et al.  Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces , 2006, IEEE Transactions on Biomedical Engineering.

[72]  R Jiménez-Fabián,et al.  Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. , 2012, Medical engineering & physics.

[73]  G. R. Muller,et al.  Brain oscillations control hand orthosis in a tetraplegic , 2000, Neuroscience Letters.

[74]  Fan Zhang,et al.  Decoding movement intent of patient with multiple sclerosis for the powered lower extremity exoskeleton , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[75]  Carlos Hitoshi Morimoto,et al.  Eye gaze tracking techniques for interactive applications , 2005, Comput. Vis. Image Underst..

[76]  Aidan D. Roche,et al.  Prosthetic Myoelectric Control Strategies: A Clinical Perspective , 2014, Current Surgery Reports.

[77]  P. Dario,et al.  Control of multifunctional prosthetic hands by processing the electromyographic signal. , 2002, Critical reviews in biomedical engineering.

[78]  Chang-Soo Han,et al.  Development of a muscle circumference sensor to estimate torque of the human elbow joint , 2014 .

[79]  Tadanobu Misawa,et al.  A Development of NIRS-based Brain-Computer Interface for Robot Control , 2012 .

[80]  D S Childress,et al.  Cineplasty as a control input for externally powered prosthetic components. , 2001, Journal of rehabilitation research and development.

[81]  Tobias Kaufmann,et al.  Comparison of tactile, auditory, and visual modality for brain-computer interface use: a case study with a patient in the locked-in state , 2013, Front. Neurosci..

[82]  Winnie Jensen,et al.  Introduction to Neural Engineering for Motor Rehabilitation , 2013 .

[83]  Jun Shi,et al.  Feasibility of controlling prosthetic hand using sonomyography signal in real time: preliminary study. , 2010, Journal of rehabilitation research and development.

[84]  Dennis J. McFarland,et al.  Brain-Computer Interface Operation of Robotic and Prosthetic Devices , 2008, Computer.

[85]  A. M. Simon,et al.  Patient Training for Functional Use of Pattern Recognition–Controlled Prostheses , 2012, Journal of prosthetics and orthotics : JPO.

[86]  Panagiotis Artemiadis,et al.  A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements , 2017, Journal of neural engineering.

[87]  Aaron M. Dollar,et al.  Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art , 2008, IEEE Transactions on Robotics.

[88]  Y. Zheng,et al.  Sonomyography: monitoring morphological changes of forearm muscles in actions with the feasibility for the control of powered prosthesis. , 2006, Medical engineering & physics.

[89]  Tom Chau,et al.  Coupled microphone-accelerometer sensor pair for dynamic noise reduction in MMG signal recording , 2003 .

[90]  David Howard,et al.  A comparative evaluation of sonomyography, electromyography, force, and wrist angle in a discrete tracking task. , 2011, Ultrasound in medicine & biology.

[91]  P. Veltink Sensory feedback in artificial control of human mobility. , 1999, Technology and health care : official journal of the European Society for Engineering and Medicine.

[92]  Claudio Castellini,et al.  Ultrasound image features of the wrist are linearly related to finger positions , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[93]  Kevin Englehart,et al.  Evaluation of shoulder complex motion-based input strategies for endpoint prosthetic-limb control using dual-task paradigm. , 2011, Journal of rehabilitation research and development.

[94]  Sunil Agrawal,et al.  Quantifying Anti-Gravity Torques for the Design of a Powered Exoskeleton , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[95]  Jian Huang,et al.  Control of a rehabilitation robotic exoskeleton based on intentional reaching direction , 2010, 2010 International Symposium on Micro-NanoMechatronics and Human Science.

[96]  Gernot R. Müller-Putz,et al.  Control of an Electrical Prosthesis With an SSVEP-Based BCI , 2008, IEEE Transactions on Biomedical Engineering.

[97]  Blair A. Lock,et al.  Determining the Optimal Window Length for Pattern Recognition-Based Myoelectric Control: Balancing the Competing Effects of Classification Error and Controller Delay , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[98]  Rajendra Katti,et al.  OPTICALLY-BASED CONTROL OF A PROSTHETIC DEVICE , .

[99]  Michael L Boninger,et al.  Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury. , 2013, Journal of rehabilitation research and development.

[100]  Nikolaus Weiskopf,et al.  Real-time fMRI and its application to neurofeedback , 2012, NeuroImage.

[101]  M. Feng,et al.  A sensor to measure hardness of human tissue , 2006, 2006 5th IEEE Conference on Sensors.

[102]  Juan Pablo Wachs,et al.  Integrated vision-based robotic arm interface for operators with upper limb mobility impairments , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

[103]  Dario Farina,et al.  Extracting Signals Robust to Electrode Number and Shift for Online Simultaneous and Proportional Myoelectric Control by Factorization Algorithms , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[104]  Claudio Castellini,et al.  A realistic implementation of ultrasound imaging as a human-machine interface for upper-limb amputees , 2013, Front. Neurorobot..

[105]  Andrew Jackson,et al.  Learning a Novel Myoelectric-Controlled Interface Task , 2008, Journal of neurophysiology.

[106]  Lauren H. Smith,et al.  Intramuscular EMG after targeted muscle reinnervation for pattern recognition control of myoelectric prostheses , 2013, International IEEE/EMBS Conference on Neural Engineering.

[107]  Glyn H Heath Control of proportional grasping using a myokinemetric signal , 2003 .

[108]  Eric Monacelli,et al.  Intelligent Assistive Exoskeleton with Vision Based Interface , 2008, ICOST.

[109]  F C van der Helm,et al.  Biomechatronics – Assisting the Impaired Motor System , 2001, Archives of physiology and biochemistry.

[110]  Todd A Kuiken,et al.  Target Achievement Control Test: evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses. , 2011, Journal of rehabilitation research and development.

[111]  Sylvain Chevallier,et al.  Fast calibration of hand movements-based interface for arm exoskeleton control , 2012, ESANN.

[112]  G. Dumanian,et al.  Advances in Transfemoral Amputee Rehabilitation: Early Experience with Targeted Muscle Reinnervation , 2014, Current Surgery Reports.

[113]  Fang Wang,et al.  Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[114]  Ann M. Simon,et al.  A comparison of proportional control methods for pattern recognition control , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[115]  Rory A Cooper,et al.  Joystick control for powered mobility: current state of technology and future directions. , 2010, Physical medicine and rehabilitation clinics of North America.

[116]  F. Jolesz,et al.  Brain–machine interface via real-time fMRI: Preliminary study on thought-controlled robotic arm , 2009, Neuroscience Letters.

[117]  M. Betke,et al.  The Camera Mouse: visual tracking of body features to provide computer access for people with severe disabilities , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[118]  Ke Yong Li,et al.  The Speech Control System of Intelligent Robot Prosthesis , 2010, 2010 Second WRI Global Congress on Intelligent Systems.

[119]  Christian Cipriani,et al.  Abstract and Proportional Myoelectric Control for Multi-Fingered Hand Prostheses , 2013, Annals of Biomedical Engineering.

[120]  Yousef Mazaheri,et al.  Information transfer rate in fMRI experiments measured using mutual information theory , 2008, Journal of Neuroscience Methods.

[121]  Michael W Neumeister,et al.  Targeted muscle reinnervation of a muscle-free flap for improved prosthetic control in a shoulder amputee: case report. , 2011, The Journal of hand surgery.

[122]  D T Barry,et al.  Acoustic myography as a control signal for an externally powered prosthesis. , 1986, Archives of physical medicine and rehabilitation.

[123]  Strahinja Došen,et al.  Cognitive vision system for control of dexterous prosthetic hands: Experimental evaluation , 2010, Journal of NeuroEngineering and Rehabilitation.

[124]  N. Birbaumer,et al.  Brain–computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients? , 2008, Clinical Neurophysiology.

[125]  Alessandro Tognetti,et al.  An Innovative Multisensor Controlled Prosthetic Hand , 2014 .

[126]  Rory A. Cooper,et al.  Performance assessment of a pushrim-activated power-assisted wheelchair control system , 2002, IEEE Trans. Control. Syst. Technol..

[127]  Minglu Zhang,et al.  Robot control based on voice command , 2008, 2008 IEEE International Conference on Automation and Logistics.

[128]  Robert D. Lipschutz,et al.  Targeted reinnervation for enhanced prosthetic arm function in a woman with a proximal amputation: a case study , 2007, The Lancet.

[129]  Edwin van Asseldonk,et al.  Actively controlled lateral gait assistance in a lower limb exoskeleton , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[130]  Wolfgang Rosenstiel,et al.  An MEG-based brain–computer interface (BCI) , 2007, NeuroImage.

[131]  Michael Erb,et al.  Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data , 2003, NeuroImage.

[132]  A. Kübler,et al.  Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials , 2014, Journal of NeuroEngineering and Rehabilitation.

[133]  A O Posatskiy,et al.  The effects of motion artifact on mechanomyography: A comparative study of microphones and accelerometers. , 2012, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[134]  Ganwen Zeng,et al.  An overview of robot force control , 1997, Robotica.

[135]  F. Giannini,et al.  Characterization of piezoresistive sensors for goniometric glove in hand prostheses , 2009, 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology.

[136]  David T. Gibbons,et al.  An Above-Elbow Prosthesis Employing Programmed Linkages , 1987, IEEE Transactions on Biomedical Engineering.

[137]  E-J Hoogerwerf,et al.  Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller , 2014, Journal of neural engineering.

[138]  Wyatt S. Newman,et al.  A human-robot interface based on electrooculography , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[139]  Todd A Kuiken,et al.  Non-weight-bearing neural control of a powered transfemoral prosthesis , 2013, Journal of NeuroEngineering and Rehabilitation.

[140]  Sheng Quan Xie,et al.  Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. , 2012, Medical engineering & physics.

[141]  Urbano Lugrís,et al.  Simulation and design of an active orthosis for an incomplete spinal cord injured subject , 2011 .

[142]  Jung Kim,et al.  Optical muscle activation sensors for estimating upper limb force level , 2011, 2011 IEEE International Instrumentation and Measurement Technology Conference.

[143]  Dudley S Childress,et al.  The effects of static friction and backlash on extended physiological proprioception control of a powered prosthesis. , 2005, Journal of rehabilitation research and development.

[144]  Bernd Freisleben,et al.  HaWCoS: the "hands-free" wheelchair control system , 2002, ASSETS.

[145]  G R Johnson,et al.  The design of a five-degree-of-freedom powered orthosis for the upper limb , 2001, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[146]  S. Coyle,et al.  Brain–computer interfaces: a review , 2003 .

[147]  R.F. Kirsch,et al.  Evaluation of Head Orientation and Neck Muscle EMG Signals as Command Inputs to a Human–Computer Interface for Individuals With High Tetraplegia , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[148]  Erik J. Scheme,et al.  Validation of a Selective Ensemble-Based Classification Scheme for Myoelectric Control Using a Three-Dimensional Fitts' Law Test , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[149]  Wolfgang Borutzky,et al.  Bond Graph Methodology , 2010 .

[150]  Eduardo Rocon de Lima,et al.  Design and implementation of an inertial measurement unit for control of artificial limbs: Application on leg orthoses , 2006 .

[151]  Mark R. Pitkin Prosthetic restoration and rehabilitation of the upper and lower extremity , 2015 .

[152]  Wolfgang Borutzky,et al.  Bond Graph Methodology: Development and Analysis of Multidisciplinary Dynamic System Models , 2009 .

[153]  Jung Kim,et al.  Development of real-time muscle stiffness sensor based on resonance frequency for physical Human Robot Interactions , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[154]  Shinobu Masaki,et al.  A pressure sensitive palatography: application of new pressure sensitive sheet for measuring tongue-palatal contact pressure , 1998, ICSLP.

[155]  Barbara Caputo,et al.  Stable myoelectric control of a hand prosthesis using non-linear incremental learning , 2014, Front. Neurorobot..

[156]  Blair A. Lock,et al.  Redirection of cutaneous sensation from the hand to the chest skin of human amputees with targeted reinnervation , 2007, Proceedings of the National Academy of Sciences.

[157]  E A BRAV,et al.  Cineplasty; an end-result study. , 1957, The Journal of bone and joint surgery. American volume.

[158]  Eugene Coyle Electronic wheelchair controller designed for operation by hand operated joystick, ultrasonic non-contact head control and utterance from a small word-command vocabulary , 1995 .

[159]  Mario E. Giardini,et al.  An electrooptical muscle contraction sensor , 2010, Medical & Biological Engineering & Computing.

[160]  Shiqian Wang,et al.  Design and Control of the MINDWALKER Exoskeleton , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[161]  Dario Farina,et al.  Real time simultaneous and proportional control of multiple degrees of freedom from surface EMG: Preliminary results on subjects with limb deficiency , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[162]  John B. Hinkel Head-guided wheelchair control system , 2010, ASSETS '10.

[163]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[164]  Jeong-Su Han,et al.  Human-machine interface for wheelchair control with EMG and its evaluation , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[165]  C. Orizio Muscle sound: bases for the introduction of a mechanomyographic signal in muscle studies. , 1993, Critical reviews in biomedical engineering.

[166]  T. Kuiken,et al.  Control of a six degree of freedom prosthetic arm after targeted muscle reinnervation surgery. , 2008, Archives of physical medicine and rehabilitation.

[167]  Ethan R. Buch,et al.  Think to Move: a Neuromagnetic Brain-Computer Interface (BCI) System for Chronic Stroke , 2008, Stroke.

[168]  Masashi Kiguchi,et al.  A Communication Means for Totally Locked-in ALS Patients Based on Changes in Cerebral Blood Volume Measured with Near-Infrared Light , 2007, IEICE Trans. Inf. Syst..

[169]  Max Ortiz-Catalan,et al.  Real-Time and Simultaneous Control of Artificial Limbs Based on Pattern Recognition Algorithms , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[170]  Dean J Krusienski,et al.  Brain-computer interfaces in medicine. , 2012, Mayo Clinic proceedings.

[171]  Ryuta Kawashima,et al.  A NIRS-based brain-computer interface system during motor imagery: System development and online feedback training , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[172]  Dario Farina,et al.  The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[173]  Ashok Muzumdar Powered upper limb prostheses : control, implementation and clinical application , 2004 .

[174]  R. Dillmann,et al.  Using gesture and speech control for commanding a robot assistant , 2002, Proceedings. 11th IEEE International Workshop on Robot and Human Interactive Communication.

[175]  Robert D. Lipschutz,et al.  Use of two-axis joystick for control of externally powered shoulder disarticulation prostheses. , 2011, Journal of rehabilitation research and development.

[176]  T. Hoellinger,et al.  MINDWALKER: Going one step further with assistive lower limbs exoskeleton for SCI condition subjects , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[177]  Linda Resnik,et al.  Self-reported and performance-based outcomes using DEKA Arm. , 2014, Journal of rehabilitation research and development.

[178]  W Craelius,et al.  A biomimetic controller for a multifinger prosthesis. , 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[179]  Ann M. Simon,et al.  Prosthesis-Guided Training For Practical Use Of Pattern Recognition Control Of Prostheses , 2011 .

[180]  A. M. Simon,et al.  Real-time myoelectric control of knee and ankle motions for transfemoral amputees. , 2011, JAMA.

[181]  Joan Lobo-Prat,et al.  Evaluation of EMG, force and joystick as control interfaces for active arm supports , 2014, Journal of NeuroEngineering and Rehabilitation.

[182]  Todd A Kuiken,et al.  Comparison of electromyography and force as interfaces for prosthetic control. , 2011, Journal of rehabilitation research and development.

[183]  Roland Kadefors,et al.  Electrical impedance as a source of information in man-machine systems , 1972 .

[184]  Thomas Bianchi,et al.  NIRS monitoring of muscle contraction to control a prosthetic device , 1999, European Conference on Biomedical Optics.

[185]  José del R. Millán,et al.  Transferring brain-computer interfaces beyond the laboratory: Successful application control for motor-disabled users , 2013, Artif. Intell. Medicine.

[186]  R.F. Weir,et al.  The Optimal Controller Delay for Myoelectric Prostheses , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[187]  Hasan Ayaz,et al.  An optical brain computer interface for environmental control , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[188]  Doubler Ja,et al.  An analysis of extended physiological proprioception as a prosthesis-control technique. , 1984 .

[189]  W. A. Sarnacki,et al.  Electroencephalographic (EEG) control of three-dimensional movement , 2010, Journal of neural engineering.

[190]  Levi J Hargrove,et al.  A real-time comparison between direct control, sequential pattern recognition control and simultaneous pattern recognition control using a Fitts’ law style assessment procedure , 2014, Journal of NeuroEngineering and Rehabilitation.

[191]  Dario Farina,et al.  Intuitive, Online, Simultaneous, and Proportional Myoelectric Control Over Two Degrees-of-Freedom in Upper Limb Amputees , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[192]  Hung T. Nguyen,et al.  Performance of a head-movement interface for wheelchair control , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[193]  Jose L Pons,et al.  Wearable Robots: Biomechatronic Exoskeletons , 2008 .