Using principles of motor control to analyze performance of human machine interfaces

[1]  Qiang Zhang,et al.  Evaluation of a Fused Sonomyography and Electromyography-Based Control on a Cable-Driven Ankle Exoskeleton , 2023, IEEE Transactions on Robotics.

[2]  A. M. Simon,et al.  User Performance With a Transradial Multi-Articulating Hand Prosthesis During Pattern Recognition and Direct Control Home Use , 2022, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  Chih-Chung Huang,et al.  Development of a Wearable Ultrasound Transducer for Sensing Muscle Activities in Assistive Robotics Applications , 2022, 2022 IEEE International Ultrasonics Symposium (IUS).

[4]  Honghai Liu,et al.  A Wearable Ultrasound Interface for Prosthetic Hand Control , 2022, IEEE Journal of Biomedical and Health Informatics.

[5]  R. Palkovits,et al.  Challenges and state of the art of glycerol conversion to acrylonitrile , 2022, Chemie Ingenieur Technik.

[6]  Claudio Castellini,et al.  Simultaneous and Proportional Real-Time Myocontrol of Up to Three Degrees of Freedom of the Wrist and Hand , 2022, IEEE Transactions on Biomedical Engineering.

[7]  Wilsaan M. Joiner,et al.  Sonomyography shows feasibility as a tool to quantify joint movement at the muscle level , 2022, 2022 International Conference on Rehabilitation Robotics (ICORR).

[8]  N. Sharma,et al.  Editorial: Next Generation User-Adaptive Wearable Robots , 2022, Frontiers in Robotics and AI.

[9]  S. Engdahl,et al.  First Demonstration of Functional Task Performance Using a Sonomyographic Prosthesis: A Case Study , 2022, Frontiers in Bioengineering and Biotechnology.

[10]  Xiaohong Chen,et al.  From sensing to control of lower limb exoskeleton: a systematic review , 2022, Annu. Rev. Control..

[11]  Yaqi Chu,et al.  Face-Computer Interface (FCI): Intent Recognition Based on Facial Electromyography (fEMG) and Online Human-Computer Interface With Audiovisual Feedback , 2021, Frontiers in Neurorobotics.

[12]  A. Pisarchik,et al.  Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states , 2021 .

[13]  Ravi Vaidyanathan,et al.  Wearable MMG-Plus-One Armband: Evaluation of Normal Force on Mechanomyography (MMG) to Enhance Human-Machine Interfacing , 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Mads Jochumsen,et al.  Investigating the feasibility of combining EEG and EMG for controlling a hybrid human computer interface in patients with spinal cord injury , 2020, 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE).

[15]  Tianyiyi He,et al.  Technologies toward next generation human machine interfaces: From machine learning enhanced tactile sensing to neuromorphic sensory systems , 2020, Applied Physics Reviews.

[16]  Dora Hermes,et al.  The current state of electrocorticography-based brain-computer interfaces. , 2020, Neurosurgical focus.

[17]  Siddhartha Sikdar,et al.  Sonomyography Combined with Vibrotactile Feedback Enables Precise Target Acquisition Without Visual Feedback , 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[18]  Connor Esterwood,et al.  A Usability Study of Low-Cost Wireless Brain-Computer Interface for Cursor Control Using Online Linear Model , 2020, IEEE Transactions on Human-Machine Systems.

[19]  Wendy Ju,et al.  Next Steps for Human-Computer Integration , 2020, CHI.

[20]  Chin-Teng Lin,et al.  EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[21]  Norma Candolfi Arballo,et al.  Myoelectric Interfaces and Related Applications: Current State of EMG Signal Processing–A Systematic Review , 2020, IEEE Access.

[22]  Alex H. B. Duffy,et al.  Systematic literature review of hand gestures used in human computer interaction interfaces , 2019, Int. J. Hum. Comput. Stud..

[23]  Siddhartha Sikdar,et al.  Evaluation of the Role of Proprioception During Proportional Position Control Using Sonomyography: Applications in Prosthetic Control , 2019, 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR).

[24]  Bo Tao,et al.  Intelligent Human-Computer Interaction Based on Surface EMG Gesture Recognition , 2019, IEEE Access.

[25]  Ping Zhou,et al.  Hands-Free Human-Computer Interface Based on Facial Myoelectric Pattern Recognition , 2019, Front. Neurol..

[26]  Gian Maria Gasparri,et al.  Proportional Joint-Moment Control for Instantaneously Adaptive Ankle Exoskeleton Assistance , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[27]  Lucas C. Parra,et al.  Adaptive Auto-Regressive Proportional Myoelectric Control , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  Ivan Vujaklija,et al.  Novel Control Strategies for Upper Limb Prosthetics , 2018, Converging Clinical and Engineering Research on Neurorehabilitation III.

[29]  Erik Scheme,et al.  Real-time, simultaneous myoelectric control using a convolutional neural network , 2018, PloS one.

[30]  Siddhartha Sikdar,et al.  Proprioceptive Sonomyographic Control: A novel method for intuitive and proportional control of multiple degrees-of-freedom for individuals with upper extremity limb loss , 2018, Scientific Reports.

[31]  Alison Twycross,et al.  What is a case study? , 2017, Evidence Based Journals.

[32]  Thierry Keller,et al.  Short- and Long-Term Learning of Feedforward Control of a Myoelectric Prosthesis with Sensory Feedback by Amputees , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  Reva E. Johnson,et al.  Adaptation to random and systematic errors: Comparison of amputee and non-amputee control interfaces with varying levels of process noise , 2017, PloS one.

[34]  Jana Kosecka,et al.  Real-time, ultrasound-based control of a virtual hand by a trans-radial amputee , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[35]  Jana Kosecka,et al.  Real-Time Classification of Hand Motions Using Ultrasound Imaging of Forearm Muscles , 2016, IEEE Transactions on Biomedical Engineering.

[36]  Lauren H Smith,et al.  Use of probabilistic weights to enhance linear regression myoelectric control , 2015, Journal of neural engineering.

[37]  Bing Chen,et al.  Recent developments and challenges of lower extremity exoskeletons , 2015, Journal of orthopaedic translation.

[38]  Dario Farina,et al.  A Multi-Class Proportional Myocontrol Algorithm for Upper Limb Prosthesis Control: Validation in Real-Life Scenarios on Amputees , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[39]  Christian Cipriani,et al.  Ultrasound imaging for hand prosthesis control: a comparative study of features and classification methods , 2015, 2015 IEEE International Conference on Rehabilitation Robotics (ICORR).

[40]  David Hankin,et al.  First-in-man demonstration of a fully implanted myoelectric sensors system to control an advanced electromechanical prosthetic hand , 2015, Journal of Neuroscience Methods.

[41]  D. Farina,et al.  Spatial Correlation of High Density EMG Signals Provides Features Robust to Electrode Number and Shift in Pattern Recognition for Myocontrol , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[42]  Dario Farina,et al.  Extending mode switching to multiple degrees of freedom in hand prosthesis control is not efficient , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[43]  D. Farina,et al.  Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[44]  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.

[45]  Yoshiyuki Asai,et al.  Learning an Intermittent Control Strategy for Postural Balancing Using an EMG-Based Human-Computer Interface , 2013, PloS one.

[46]  Chang-Hwan Im,et al.  EEG-Based Brain-Computer Interfaces: A Thorough Literature Survey , 2013, Int. J. Hum. Comput. Interact..

[47]  Brendan Z. Allison,et al.  Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction , 2013 .

[48]  Kikuro Fukushima,et al.  Submovement Composition of Head Movement , 2012, PloS one.

[49]  C. Castellini,et al.  Using Ultrasound Images of the Forearm to Predict Finger Positions , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[50]  Lida Xu,et al.  EMG and EPP-Integrated Human–Machine Interface Between the Paralyzed and Rehabilitation Exoskeleton , 2012, IEEE Transactions on Information Technology in Biomedicine.

[51]  Patrick van der Smagt,et al.  Minimum jerk for human catching movements in 3D , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[52]  O. Stavdahl,et al.  Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[53]  G. Schalk,et al.  Brain-Computer Interfaces Using Electrocorticographic Signals , 2011, IEEE Reviews in Biomedical Engineering.

[54]  Amir Karniel,et al.  Open questions in computational motor control. , 2011, Journal of integrative neuroscience.

[55]  J. Clarke,et al.  What is a systematic review? , 2011, Evidence Based Nursing.

[56]  A Melendez-Calderon,et al.  Force Field Adaptation Can Be Learned Using Vision in the Absence of Proprioceptive Error , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[57]  Michael J. Black,et al.  Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[58]  Yong-Ku Kong,et al.  Crosstalk effect on surface electromyogram of the forearm flexors during a static grip task. , 2010, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[59]  Klas Ihme,et al.  A Dry EEG-System for Scientific Research and Brain–Computer Interfaces , 2010, Front. Neurosci..

[60]  Hiske van Duinen,et al.  Voluntary activation of the different compartments of the flexor digitorum profundus. , 2010, Journal of neurophysiology.

[61]  K.B. Englehart,et al.  Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[62]  Lynn Rochester,et al.  Motor learning in Parkinson's disease: limitations and potential for rehabilitation. , 2009, Parkinsonism & related disorders.

[63]  R G Radwin,et al.  Evaluation of a modified Fitts law brain–computer interface target acquisition task in able and motor disabled individuals , 2009, Journal of neural engineering.

[64]  Daniel P Ferris,et al.  The exoskeletons are here , 2009, Journal of NeuroEngineering and Rehabilitation.

[65]  J. Randall Flanagan,et al.  Coding and use of tactile signals from the fingertips in object manipulation tasks , 2009, Nature Reviews Neuroscience.

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

[67]  Patrick van der Smagt,et al.  Surface EMG in advanced hand prosthetics , 2008, Biological Cybernetics.

[68]  J. Wolpaw,et al.  Brain–computer interfaces in neurological rehabilitation , 2008, The Lancet Neurology.

[69]  Desney S. Tan,et al.  Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces , 2008, CHI.

[70]  J. Krakauer,et al.  A computational neuroanatomy for motor control , 2008, Experimental Brain Research.

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

[72]  Basia Belza,et al.  Ambulatory Physical Activity Performance in Youth With Cerebral Palsy and Youth Who Are Developing Typically , 2007, Physical Therapy.

[73]  N. Birbaumer Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. , 2006, Psychophysiology.

[74]  Junuk Chu,et al.  A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand , 2006, IEEE Transactions on Biomedical Engineering.

[75]  L.J. Trejo,et al.  Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[76]  Philip N. Sabes,et al.  Flexible strategies for sensory integration during motor planning , 2005, Nature Neuroscience.

[77]  Reza Shadmehr,et al.  The Computational Neurobiology of Reaching and Pointing: A Foundation for Motor Learning , 2004 .

[78]  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).

[79]  R. Sainburg,et al.  Handedness: dominant arm advantages in control of limb dynamics. , 2002, Journal of neurophysiology.

[80]  E L Morin,et al.  Sampling, noise-reduction and amplitude estimation issues in surface electromyography. , 2002, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[81]  W. Prinz,et al.  Perceptual basis of bimanual coordination , 2001, Nature.

[82]  J. Flanagan,et al.  Independence of perceptual and sensorimotor predictions in the size–weight illusion , 2000, Nature Neuroscience.

[83]  K. Takada,et al.  Smoothness of Human Jaw Movement during Chewing , 1999, Journal of dental research.

[84]  M. Graziano Where is my arm? The relative role of vision and proprioception in the neuronal representation of limb position. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[85]  J. Kalaska,et al.  Comparison of variability of initial kinematics and endpoints of reaching movements , 1999, Experimental Brain Research.

[86]  Michael I. Jordan,et al.  Smoothness maximization along a predefined path accurately predicts the speed profiles of complex arm movements. , 1998, Journal of neurophysiology.

[87]  T. Flash,et al.  Minimum-jerk, two-thirds power law, and isochrony: converging approaches to movement planning. , 1995, Journal of experimental psychology. Human perception and performance.

[88]  J. Lackner,et al.  Rapid adaptation to Coriolis force perturbations of arm trajectory. , 1994, Journal of neurophysiology.

[89]  F A Mussa-Ivaldi,et al.  Adaptive representation of dynamics during learning of a motor task , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[90]  D J McFarland,et al.  An EEG-based brain-computer interface for cursor control. , 1991, Electroencephalography and clinical neurophysiology.

[91]  Charles H. Pritham,et al.  Evolution and Development of the Silicone Suction Socket (3S) for Below-Knee Prostheses , 1989 .

[92]  C. Atkeson,et al.  Kinematic features of unrestrained vertical arm movements , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[93]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[94]  N. Hogan An organizing principle for a class of voluntary movements , 1984, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[95]  Pietro Morasso,et al.  How a discontinuous mechanism can produce continuous patterns in trajectory formation and handwriting , 1983 .

[96]  P. Viviani,et al.  The law relating the kinematic and figural aspects of drawing movements. , 1983, Acta psychologica.

[97]  P. Morasso,et al.  Trajectory formation and handwriting: A computational model , 1982, Biological Cybernetics.

[98]  E. Bizzi,et al.  Human arm trajectory formation. , 1982, Brain : a journal of neurology.

[99]  J. F. Soechting,et al.  Coordination of arm and wrist motion during a reaching task , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[100]  J. T. Massey,et al.  Spatial trajectories and reaction times of aimed movements: effects of practice, uncertainty, and change in target location. , 1981, Journal of neurophysiology.

[101]  M. Jeannerod,et al.  Optimal response of eye and hand motor systems in pointing at a visual target , 1979, Biological Cybernetics.

[102]  R Kerr,et al.  Diving, adaptation, and Fitts law. , 1978, Journal of motor behavior.

[103]  C J Scheirer,et al.  The analysis of ranked data derived from completely randomized factorial designs. , 1976, Biometrics.

[104]  L. Stark,et al.  Time optimal behavior of human saccadic eye movement , 1975 .

[105]  N. Berger,et al.  The use of electrical and mechanical muscular forces for the control of an electrical prosthesis. , 1952, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[106]  Thorsten O. Zander,et al.  Towards BCI-Based Implicit Control in Human–Computer Interaction , 2014 .

[107]  Huzefa Rangwala,et al.  Novel Method for Predicting Dexterous Individual Finger Movements by Imaging Muscle Activity Using a Wearable Ultrasonic System , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[108]  J. Krakauer,et al.  Motor learning principles for neurorehabilitation. , 2013, Handbook of clinical neurology.

[109]  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.

[110]  Karl F. Stock,et al.  A COMPUTATIONAL MODEL , 2011 .

[111]  Brendan Z. Allison,et al.  Brain-Computer Interfaces , 2010 .

[112]  Alexander W Dromerick,et al.  Feedforward control strategies of subjects with transradial amputation in planar reaching. , 2010, Journal of rehabilitation research and development.

[113]  P. Morasso,et al.  Analysis of human movements: spatial localisation with multiple perspective views , 2006, Medical and Biological Engineering and Computing.

[114]  J. Patton,et al.  Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors , 2005, Experimental Brain Research.

[115]  P. Morasso Spatial control of arm movements , 2004, Experimental Brain Research.

[116]  Mitsuo Kawato,et al.  A computational model of four regions of the cerebellum based on feedback-error learning , 2004, Biological Cybernetics.

[117]  R. Johansson,et al.  Factors influencing the force control during precision grip , 2004, Experimental Brain Research.

[118]  J. F. Soechting,et al.  Effect of target size on spatial and temporal characteristics of a pointing movement in man , 2004, Experimental Brain Research.

[119]  R. S. Johansson,et al.  Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects , 2004, Experimental Brain Research.

[120]  Niels da Vitoria Lobo,et al.  Features and Classification Methods , 2001 .

[121]  P. Viviani,et al.  32 Space-Time Invariance in Learned Motor Skills , 1980 .