A Review of Sensory Feedback in Upper-Limb Prostheses From the Perspective of Human Motor Control

This manuscript reviews historical and recent studies that focus on supplementary sensory feedback for use in upper limb prostheses. It shows that the inability of many studies to speak to the issue of meaningful performance improvements in real-life scenarios is caused by the complexity of the interactions of supplementary sensory feedback with other types of feedback along with other portions of the motor control process. To do this, the present manuscript frames the question of supplementary feedback from the perspective of computational motor control, providing a brief review of the main advances in that field over the last 20 years. It then separates the studies on the closed-loop prosthesis control into distinct categories, which are defined by relating the impact of feedback to the relevant components of the motor control framework, and reviews the work that has been done over the last 50+ years in each of those categories. It ends with a discussion of the studies, along with suggestions for experimental construction and connections with other areas of research, such as machine learning.

[1]  P. Fitts The information capacity of the human motor system in controlling the amplitude of movement. , 1954, Journal of experimental psychology.

[2]  A. Gale,et al.  MUSCLE TRAINING FOR BICEPS CINEPLASTY , 1957 .

[3]  A. H. Bottomley MYO-ELECTRIC CONTROL OF POWERED PROSTHESES. , 1965, The Journal of bone and joint surgery. British volume.

[4]  J. C. Bliss,et al.  Compensatory Tracking with Visual and Tactile Displays , 1966 .

[5]  N. A. Bernshteĭn The co-ordination and regulation of movements , 1967 .

[6]  D. B. Welbourn,et al.  Paper 8: A Self-Adaptive Gripping Device: Its Design and Performance , 1968 .

[7]  M. Rakić Paper 11: The ‘Belgrade Hand Prosthesis’ , 1968 .

[8]  D H Weir,et al.  Theory of manual vehicular control. , 1969, Ergonomics.

[9]  S. D. Reimers,et al.  Kinesthetic Sensing for the EMG Controlled "Boston Arm" , 1970 .

[10]  Thomas R. Schori,et al.  Tracking Performance as a Function of Precision of Electrocutaneous Feedback Information , 1970 .

[11]  J Kawamura,et al.  [Sensory feedback device for the artificial arm]. , 1971, Nihon Seikeigeka Gakkai zasshi.

[12]  F. Clippinger,et al.  A sensory feedback system for an upper-limb amputation prosthesis. , 1974, Bulletin of prosthetics research.

[13]  R E Prior,et al.  Supplemental sensory feedback for the VA/NU myoelectric hand. Background and preliminary designs. , 1976, Bulletin of prosthetics research.

[14]  Daniel E. Whitney,et al.  Force Feedback Control of Manipulator Fine Motions , 1977 .

[15]  H. Schmidt The importance of information feedback in prostheses for the upper limbs1 , 1977 .

[16]  D C Simpson,et al.  An externally powered controlled complete arm prosthesis. , 1977, Journal of medical engineering & technology.

[17]  George A. Bekey,et al.  Tactile Information Processing by Human Operators in Control Systems , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[18]  Carlo J. De Luca,et al.  Physiology and Mathematics of Myoelectric Signals , 1979 .

[19]  Frank A. Saunders,et al.  Electrocutaneous Stimulation for Sensory Communication in Rehabilitation Engineering , 1982, IEEE Transactions on Biomedical Engineering.

[20]  D. Childress,et al.  Design and evaluation of a prosthesis control system based on the concept of extended physiological proprioception. , 1984, Journal of rehabilitation research and development.

[21]  L. Ince,et al.  Experimental foundations of EMG biofeedback with the upper extremity: A review of the literature , 1984, Biofeedback and Self-Regulation.

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

[23]  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.

[24]  D. Livesey,et al.  Optimal Control Theory and Grants in Aid , 1986 .

[25]  S C Jacobsen,et al.  Extended physiologic taction: design and evaluation of a proportional force feedback system. , 1989, Journal of rehabilitation research and development.

[26]  R. Scott Feedback in myoelectric prostheses. , 1990, Clinical orthopaedics and related research.

[27]  W.J. Tompkins,et al.  Electrotactile and vibrotactile displays for sensory substitution systems , 1991, IEEE Transactions on Biomedical Engineering.

[28]  K. J. Cole,et al.  Sensory-motor coordination during grasping and manipulative actions , 1992, Current Opinion in Neurobiology.

[29]  P E Patterson,et al.  Design and evaluation of a sensory feedback system that provides grasping pressure in a myoelectric hand. , 1992, Journal of rehabilitation research and development.

[30]  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.

[31]  P H Chappell,et al.  The Southampton Hand: an intelligent myoelectric prosthesis. , 1994, Journal of rehabilitation research and development.

[32]  William A. Gruver,et al.  Gripping force sensory feedback for a myoelectrically controlled forearm prosthesis , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[33]  T. Elbert,et al.  Phantom-limb pain as a perceptual correlate of cortical reorganization following arm amputation , 1995, Nature.

[34]  J R Flanagan,et al.  The Role of Internal Models in Motion Planning and Control: Evidence from Grip Force Adjustments during Movements of Hand-Held Loads , 1997, The Journal of Neuroscience.

[35]  B. Rosén,et al.  Artificial Sensibility Based on the Use of Piezoresistive Sensors , 1998, Journal of hand surgery.

[36]  Daniel M. Wolpert,et al.  Making smooth moves , 2022 .

[37]  T. Elbert,et al.  Plasticity of plasticity? Changes in the pattern of perceptual correlates of reorganization after amputation. , 1998, Brain : a journal of neurology.

[38]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[39]  A. J. Harris,et al.  Cortical origin of pathological pain , 1999, The Lancet.

[40]  N. Birbaumer,et al.  Does use of a myoelectric prosthesis prevent cortical reorganization and phantom limb pain? , 1999, Nature Neuroscience.

[41]  D. L. Weeks,et al.  Precision-grip force changes in the anatomical and prosthetic limb during predictable load increases , 2000, Experimental Brain Research.

[42]  D. Wolpert,et al.  Why can't you tickle yourself? , 2000, Neuroreport.

[43]  E A Clancy,et al.  Estimation and application of EMG amplitude during dynamic contractions. , 2001, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[44]  R. Johansson,et al.  Sensorimotor prediction and memory in object manipulation. , 2001, Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.

[45]  H. Flor,et al.  The relationship of perceptual phenomena and cortical reorganization in upper extremity amputees , 2001, Neuroscience.

[46]  H. Flor,et al.  Phantom movements and pain. An fMRI study in upper limb amputees. , 2001, Brain : a journal of neurology.

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

[48]  Kelvin E. Jones,et al.  Sources of signal-dependent noise during isometric force production. , 2002, Journal of neurophysiology.

[49]  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.

[50]  M. Ernst,et al.  Humans integrate visual and haptic information in a statistically optimal fashion , 2002, Nature.

[51]  D. Wolpert,et al.  Abnormalities in the awareness of action , 2002, Trends in Cognitive Sciences.

[52]  P. Haggard,et al.  Voluntary action and conscious awareness , 2002, Nature Neuroscience.

[53]  Andrew G. Barto,et al.  The emergence of movement units through learning with noisy efferent signals and delayed sensory feedback , 2002, Neurocomputing.

[54]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[55]  W.M. Grill,et al.  Evaluation of command algorithms for control of upper-extremity neural prostheses , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[56]  D. Wegner The Illusion of Conscious Will , 2018, The MIT Press.

[57]  C. Spence,et al.  Multisensory integration and the body schema: close to hand and within reach , 2003, Current Biology.

[58]  Rieko Osu,et al.  Different mechanisms involved in adaptation to stable and unstable dynamics. , 2003, Journal of neurophysiology.

[59]  D. Wegner The mind's best trick: how we experience conscious will , 2003, Trends in Cognitive Sciences.

[60]  M. Kawato,et al.  Formation and control of optimal trajectory in human multijoint arm movement , 1989, Biological Cybernetics.

[61]  R. Johansson,et al.  Coordinated isometric muscle commands adequately and erroneously programmed for the weight during lifting task with precision grip , 2004, Experimental Brain Research.

[62]  Yves Guiard,et al.  Fitts' law 50 years later: applications and contributions from human-computer interaction , 2004, Int. J. Hum. Comput. Stud..

[63]  E. Todorov Optimality principles in sensorimotor control , 2004, Nature Neuroscience.

[64]  S. Scott Optimal feedback control and the neural basis of volitional motor control , 2004, Nature Reviews Neuroscience.

[65]  R. Johansson,et al.  Visual size cues in the programming of manipulative forces during precision grip , 2004, Experimental Brain Research.

[66]  I. Scott MacKenzie,et al.  Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts' law research in HCI , 2004, Int. J. Hum. Comput. Stud..

[67]  B. Sparrow,et al.  Vicarious agency: experiencing control over the movements of others. , 2004, Journal of personality and social psychology.

[68]  Yves Guiard,et al.  Preface: Fitts' law 50 years later: Applications and contributions from human-computer interaction , 2004 .

[69]  C. Spence,et al.  Extending or projecting peripersonal space with tools? Multisensory interactions highlight only the distal and proximal ends of tools , 2004, Neuroscience Letters.

[70]  Konrad Paul Körding,et al.  The loss function of sensorimotor learning. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[71]  Konrad Paul Kording,et al.  Bayesian integration in sensorimotor learning , 2004, Nature.

[72]  G.S. Dhillon,et al.  Direct neural sensory feedback and control of a prosthetic arm , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[73]  P. Haggard Conscious intention and motor cognition , 2005, Trends in Cognitive Sciences.

[74]  Emanuel Todorov,et al.  Stochastic Optimal Control and Estimation Methods Adapted to the Noise Characteristics of the Sensorimotor System , 2005, Neural Computation.

[75]  J. Houk,et al.  Deciding when and how to correct a movement: discrete submovements as a decision making process , 2007, Experimental Brain Research.

[76]  A. Kargov,et al.  Design and Evaluation of a Low-Cost Force Feedback System for Myoelectric Prosthetic Hands , 2006 .

[77]  M. Zafar,et al.  Effectiveness of supplemental grasp-force feedback in the presence of vision , 2000, Medical and Biological Engineering and Computing.

[78]  John Lyman,et al.  Comparison of codes for sensory feedback using electrocutaneous tracking , 1977, Annals of Biomedical Engineering.

[79]  Dudley S. Childress,et al.  Closed-loop control in prosthetic systems: Historical perspective , 2006, Annals of Biomedical Engineering.

[80]  G. F. Shannon,et al.  A comparison of alternative means of providing sensory feedback on upper limb prostheses , 2006, Medical and biological engineering.

[81]  T. A. Rohland Sensory feedback for powered limb prostheses , 2006, Medical and biological engineering.

[82]  T. Beeker,et al.  Artificial touch in a hand-prosthesis , 2006, Medical and biological engineering.

[83]  R. N. Scott,et al.  Sensory-feedback system compatible with myoelectric control , 2006, Medical and Biological Engineering and Computing.

[84]  G. Shannon A myoelectrically-controlled prosthesis with sensory feedback , 2006, Medical and Biological Engineering and Computing.

[85]  Miles C. Bowman,et al.  Control strategies in object manipulation tasks , 2006, Current Opinion in Neurobiology.

[86]  A. Anani,et al.  Afferent electrical nerve stimulation: Human tracking performance relevant to prosthesis sensory feedback , 1979, Medical and Biological Engineering and Computing.

[87]  Robert A Jacobs,et al.  Near-Optimal Human Adaptive Control across Different Noise Environments , 2006, The Journal of Neuroscience.

[88]  Lloyd L. Salisbury,et al.  A mechanical hand with automatic proportional control of prehension , 1967, Medical and biological engineering.

[89]  Elaine Biddiss,et al.  Consumer design priorities for upper limb prosthetics , 2007, Disability and rehabilitation. Assistive technology.

[90]  Kenneth M Heilman,et al.  Mirror therapy for phantom limb pain. , 2007, The New England journal of medicine.

[91]  Emanuel Todorov,et al.  Iterative linearization methods for approximately optimal control and estimation of non-linear stochastic system , 2007, Int. J. Control.

[92]  Emanuel Todorov,et al.  Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control , 2007, The Journal of Neuroscience.

[93]  Konrad P. Kording,et al.  Decision Theory: What "Should" the Nervous System Do? , 2007 .

[94]  Peter Carruthers,et al.  The illusion of conscious will , 2007, Synthese.

[95]  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.

[96]  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.

[97]  E. Biddiss,et al.  Upper-Limb Prosthetics: Critical Factors in Device Abandonment , 2007, American journal of physical medicine & rehabilitation.

[98]  Nitish V. Thakor,et al.  Testing a Prosthetic Haptic Feedback Simulator With an Interactive Force Matching Task , 2008 .

[99]  D. Nowak,et al.  Preserved and Impaired Aspects of Feed-Forward Grip Force Control After Chronic Somatosensory Deafferentation , 2008, Neurorehabilitation and neural repair.

[100]  J. Bradshaw,et al.  Mechanisms underlying embodiment, disembodiment and loss of embodiment , 2008, Neuroscience & Biobehavioral Reviews.

[101]  Marjolein P. M. Kammers,et al.  What is embodiment? A psychometric approach , 2008, Cognition.

[102]  A. Newen,et al.  Beyond the comparator model: A multifactorial two-step account of agency , 2008, Consciousness and Cognition.

[103]  C. Murray Embodiment and Prosthetics , 2008 .

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

[105]  Silvestro Micera,et al.  On the Shared Control of an EMG-Controlled Prosthetic Hand: Analysis of User–Prosthesis Interaction , 2008, IEEE Transactions on Robotics.

[106]  T. Poggio,et al.  BOOK REVIEW David Marr’s Vision: floreat computational neuroscience VISION: A COMPUTATIONAL INVESTIGATION INTO THE HUMAN REPRESENTATION AND PROCESSING OF VISUAL INFORMATION , 2009 .

[107]  Emanuel Todorov,et al.  Efficient computation of optimal actions , 2009, Proceedings of the National Academy of Sciences.

[108]  G.E. Loeb,et al.  Grip Control Using Biomimetic Tactile Sensing Systems , 2009, IEEE/ASME Transactions on Mechatronics.

[109]  T. Kuiken,et al.  Examination of Force Discrimination in Human Upper Limb Amputees With Reinnervated Limb Sensation Following Peripheral Nerve Transfer , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[110]  A. Panarese,et al.  Humans Can Integrate Force Feedback to Toes in Their Sensorimotor Control of a Robotic Hand , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[111]  D. Wegner,et al.  Modulating the sense of agency with external cues , 2009, Consciousness and Cognition.

[112]  T. Kuiken,et al.  Sensory capacity of reinnervated skin after redirection of amputated upper limb nerves to the chest , 2009, Brain : a journal of neurology.

[113]  Daniel A. Braun,et al.  Motor Task Variation Induces Structural Learning , 2009, Current Biology.

[114]  Etienne Burdet,et al.  Dissociating Variability and Effort as Determinants of Coordination , 2009, PLoS Comput. Biol..

[115]  T. Kuiken,et al.  Vibrotactile detection thresholds for chest skin of amputees following targeted reinnervation surgery , 2009, Brain Research.

[116]  P. Rossini,et al.  Double nerve intraneural interface implant on a human amputee for robotic hand control , 2010, Clinical Neurophysiology.

[117]  S. Vijayakumar,et al.  A Computational Model of Limb Impedance Control Based on Principles of Internal Model Uncertainty , 2010, PloS one.

[118]  S Micera,et al.  Control of Hand Prostheses Using Peripheral Information , 2010, IEEE Reviews in Biomedical Engineering.

[119]  Marcia K. O'Malley,et al.  Toward improved sensorimotor integration and learning using upper-limb prosthetic devices , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[120]  Cara E. Stepp,et al.  Relative to direct haptic feedback, remote vibrotactile feedback improves but slows object manipulation , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[121]  Daniel A. Braun,et al.  Risk-Sensitive Optimal Feedback Control Accounts for Sensorimotor Behavior under Uncertainty , 2010, PLoS Comput. Biol..

[122]  José González,et al.  Multichannel audio biofeedback for dynamical coupling between prosthetic hands and their users , 2010, Ind. Robot.

[123]  Daniel A. Braun,et al.  Structure learning in action , 2010, Behavioural Brain Research.

[124]  J. Wheeler,et al.  Investigation of Rotational Skin Stretch for Proprioceptive Feedback With Application to Myoelectric Systems , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[125]  Robert F Kirsch,et al.  Virtual Reality Environment for Simulating Tasks With a Myoelectric Prosthesis: An Assessment and Training Tool , 2011, Journal of prosthetics and orthotics : JPO.

[126]  Keehoon Kim,et al.  Robotic touch shifts perception of embodiment to a prosthesis in targeted reinnervation amputees. , 2011, Brain : a journal of neurology.

[127]  Christine L. MacKenzie,et al.  The Grasping Hand , 2011, The Grasping Hand.

[128]  S. Vijayakumar,et al.  The role of feed-forward and feedback processes for closed-loop prosthesis control , 2011, Journal of NeuroEngineering and Rehabilitation.

[129]  Wenwei Yu,et al.  Psycho-physiological assessment of a prosthetic hand sensory feedback system based on an auditory display: a preliminary study , 2012, Journal of NeuroEngineering and Rehabilitation.

[130]  K. Horch,et al.  Object Discrimination With an Artificial Hand Using Electrical Stimulation of Peripheral Tactile and Proprioceptive Pathways With Intrafascicular Electrodes , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[131]  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.

[132]  Peter H. Veltink,et al.  Vibro- and Electrotactile User Feedback on Hand Opening for Myoelectric Forearm Prostheses , 2012, IEEE Transactions on Biomedical Engineering.

[133]  David J. Reinkensmeyer,et al.  A computational model of use-dependent motor recovery following a stroke: Optimizing corticospinal activations via reinforcement learning can explain residual capacity and other strength recovery dynamics , 2012, Neural Networks.

[134]  Y. Matsuoka,et al.  Vibrotactile Sensory Substitution for Object Manipulation: Amplitude Versus Pulse Train Frequency Modulation , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[135]  P. Fletcher,et al.  Sense of agency in health and disease: A review of cue integration approaches☆ , 2012, Consciousness and Cognition.

[136]  Keehoon Kim,et al.  Haptic Feedback Enhances Grip Force Control of sEMG-Controlled Prosthetic Hands in Targeted Reinnervation Amputees , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[137]  Thomas R. Reppert,et al.  Evidence for Hyperbolic Temporal Discounting of Reward in Control of Movements , 2012, The Journal of Neuroscience.

[138]  O. Witte,et al.  Sensory feedback prosthesis reduces phantom limb pain: Proof of a principle , 2012, Neuroscience Letters.

[139]  G. Lundborg,et al.  Sensory feedback from a prosthetic hand based on air-mediated pressure from the hand to the forearm skin. , 2012, Journal of rehabilitation medicine.

[140]  Lionel Rigoux,et al.  A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control , 2012, PLoS Comput. Biol..

[141]  Y. Matsuoka,et al.  Comparison of remote pressure and vibrotactile feedback for prosthetic hand control , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[142]  R. Riener,et al.  Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review , 2012, Psychonomic Bulletin & Review.

[143]  Heidi Johansen-Berg,et al.  Phantom pain is associated with preserved structure and function in the former hand area , 2013, Nature Communications.

[144]  Christian Antfolk,et al.  Sensory feedback in upper limb prosthetics , 2013, Expert review of medical devices.

[145]  C. Antfolk,et al.  Artificial Redirection of Sensation From Prosthetic Fingers to the Phantom Hand Map on Transradial Amputees: Vibrotactile Versus Mechanotactile Sensory Feedback , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[146]  Christian Balkenius,et al.  Transfer of tactile input from an artificial hand to the forearm: experiments in amputees and able-bodied volunteers , 2013, Disability and rehabilitation. Assistive technology.

[147]  P. Rossini,et al.  Stanisa Raspopovic Prostheses Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand , 2014 .

[148]  H. Flor,et al.  Mirror therapy for phantom limb pain: Brain changes and the role of body representation , 2014, European journal of pain.

[149]  Jonathon W. Sensinger,et al.  Does EMG control lead to distinct motor adaptation? , 2014, Front. Neurosci..

[150]  M. Keith,et al.  A neural interface provides long-term stable natural touch perception , 2014, Science Translational Medicine.

[151]  Christian Cipriani,et al.  Humans can integrate feedback of discrete events in their sensorimotor control of a robotic hand , 2014, Experimental Brain Research.

[152]  Dario Farina,et al.  Time-division multiplexing for myoelectric closed-loop control using electrotactile feedback , 2014, Journal of NeuroEngineering and Rehabilitation.

[153]  Dario Farina,et al.  Stereovision and augmented reality for closed-loop control of grasping in hand prostheses , 2014, Journal of neural engineering.

[154]  Dario Farina,et al.  Closed-Loop Control of Grasping With a Myoelectric Hand Prosthesis: Which Are the Relevant Feedback Variables for Force Control? , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[155]  Luca Citi,et al.  Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prostheses , 2014, Science Translational Medicine.

[156]  Yei Hwan Jung,et al.  Stretchable silicon nanoribbon electronics for skin prosthesis , 2014, Nature Communications.

[157]  P. Lum,et al.  Internal models of upper limb prosthesis users when grasping and lifting a fragile object with their prosthetic limb , 2014, Experimental Brain Research.

[158]  Luigi Acerbi,et al.  On the Origins of Suboptimality in Human Probabilistic Inference , 2014, PLoS Comput. Biol..

[159]  Dario Farina,et al.  Virtual Grasping: Closed-Loop Force Control Using Electrotactile Feedback , 2014, Comput. Math. Methods Medicine.

[160]  Jacqueline S. Hebert,et al.  Applications of sensory feedback in motorized upper extremity prosthesis: a review , 2014, Expert review of medical devices.

[161]  Max Ortiz-Catalan,et al.  An osseointegrated human-machine gateway for long-term sensory feedback and motor control of artificial limbs , 2014, Science Translational Medicine.

[162]  F. Sup,et al.  A Haptic Feedback Scheme to Accurately Position a Virtual Wrist Prosthesis Using a Three-Node Tactor Array , 2015, PloS one.

[163]  Dario Farina,et al.  Sensor fusion and computer vision for context-aware control of a multi degree-of-freedom prosthesis , 2015, Journal of neural engineering.

[164]  A. V. Smirnov,et al.  Visualization of arc and plasma flow patterns for advanced material processing , 2015, J. Vis..

[165]  R. Brent Gillespie,et al.  An exploration of grip force regulation with a low-impedance myoelectric prosthesis featuring referred haptic feedback , 2015, Journal of NeuroEngineering and Rehabilitation.

[166]  Dario Farina,et al.  The impact of the stimulation frequency on closed-loop control with electrotactile feedback , 2015, Journal of NeuroEngineering and Rehabilitation.

[167]  Peter H Veltink,et al.  Vibrotactile grasping force and hand aperture feedback for myoelectric forearm prosthesis users , 2015, Prosthetics and orthotics international.

[168]  Kevin Englehart,et al.  Do Cost Functions for Tracking Error Generalize across Tasks with Different Noise Levels? , 2015, PloS one.

[169]  Dario Farina,et al.  EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis , 2015, Journal of NeuroEngineering and Rehabilitation.

[170]  Dario Farina,et al.  Building an internal model of a myoelectric prosthesis via closed-loop control for consistent and routine grasping , 2015, Experimental Brain Research.

[171]  Kianoush Nazarpour,et al.  Artificial Proprioceptive Feedback for Myoelectric Control , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[172]  Dustin J. Tyler Restoring the human touch: Prosthetics imbued with haptics give their wearers fine motor control and a sense of connection , 2016, IEEE Spectrum.

[173]  Thierry Keller,et al.  Sensor fusion and computer vision for context-aware control of a multi degree-of-freedom prosthesis , 2016 .

[174]  Daniel Tan,et al.  Sensory feedback by peripheral nerve stimulation improves task performance in individuals with upper limb loss using a myoelectric prosthesis , 2016, Journal of neural engineering.

[175]  Christian Cipriani,et al.  Non-Invasive, Temporally Discrete Feedback of Object Contact and Release Improves Grasp Control of Closed-Loop Myoelectric Transradial Prostheses , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[176]  Dario Farina,et al.  Electrotactile EMG feedback improves the control of prosthesis grasping force , 2016, Journal of neural engineering.

[177]  Dario Farina,et al.  High-Density Electromyography and Motor Skill Learning for Robust Long-Term Control of a 7-DoF Robot Arm , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[178]  Dario Farina,et al.  Multichannel electrotactile feedback for simultaneous and proportional myoelectric control , 2016, Journal of neural engineering.

[179]  Benoit P. Delhaye,et al.  The neural basis of perceived intensity in natural and artificial touch , 2016, Science Translational Medicine.

[180]  Dario Farina,et al.  Tactile feedback is an effective instrument for the training of grasping with a prosthesis at low- and medium-force levels , 2017, Experimental Brain Research.

[181]  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.

[182]  Dario Farina,et al.  User adaptation in Myoelectric Man-Machine Interfaces , 2017, Scientific Reports.

[183]  Peter F. Stadler,et al.  Similarity-Based Segmentation of Multi-Dimensional Signals , 2017, Scientific Reports.

[184]  Christian Antfolk,et al.  A review of invasive and non-invasive sensory feedback in upper limb prostheses , 2017, Expert review of medical devices.

[185]  Dario Farina,et al.  GLIMPSE: Google Glass interface for sensory feedback in myoelectric hand prostheses , 2017, Journal of neural engineering.

[186]  Graham Morgan,et al.  Deep learning-based artificial vision for grasp classification in myoelectric hands , 2017, Journal of neural engineering.

[187]  Maurizio Valle,et al.  A System for Electrotactile Feedback Using Electronic Skin and Flexible Matrix Electrodes: Experimental Evaluation , 2017, IEEE Transactions on Haptics.

[188]  Dario Farina,et al.  Humans Can Integrate Augmented Reality Feedback in Their Sensorimotor Control of a Robotic Hand , 2017, IEEE Transactions on Human-Machine Systems.

[189]  Jonathon W. Sensinger,et al.  EMG Versus Torque Control of Human–Machine Systems: Equalizing Control Signal Variability Does not Equalize Error or Uncertainty , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[190]  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.

[191]  Thierry Keller,et al.  Multichannel Electrotactile Feedback With Spatial and Mixed Coding for Closed-Loop Control of Grasping Force in Hand Prostheses , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[192]  Adam Wilson,et al.  Assessing the quality of supplementary sensory feedback using the crossmodal congruency task , 2017, Scientific Reports.

[193]  Jason Friedman,et al.  Visuomotor behaviors and performance in a dual-task paradigm with and without vibrotactile feedback when using a myoelectric controlled hand , 2018, Assistive technology : the official journal of RESNA.

[194]  Christian Cipriani,et al.  Improving internal model strength and performance of prosthetic hands using augmented feedback , 2018, Journal of NeuroEngineering and Rehabilitation.

[195]  Kevin Englehart,et al.  Conventional analysis of trial-by-trial adaptation is biased: Empirical and theoretical support using a Bayesian estimator , 2018, PLoS Comput. Biol..

[196]  Dario Farina,et al.  Myocontrol is closed-loop control: incidental feedback is sufficient for scaling the prosthesis force in routine grasping , 2018, Journal of NeuroEngineering and Rehabilitation.

[197]  Dustin J Tyler,et al.  Artificial tactile and proprioceptive feedback improves performance and confidence on object identification tasks , 2018, PloS one.

[198]  W. Miltner,et al.  Leg Prosthesis With Somatosensory Feedback Reduces Phantom Limb Pain and Increases Functionality , 2018, Front. Neurol..

[199]  Nitish V. Thakor,et al.  Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain , 2018, Science Robotics.

[200]  Linda Resnik,et al.  Home Use of a Neural-connected Sensory Prosthesis Provides the Functional and Psychosocial Experience of Having a Hand Again , 2018, Scientific Reports.

[201]  Silvestro Micera,et al.  Biomimetic Intraneural Sensory Feedback Enhances Sensation Naturalness, Tactile Sensitivity, and Manual Dexterity in a Bidirectional Prosthesis , 2018, Neuron.

[202]  Ahmed W. Shehata,et al.  Evaluating Internal Model Strength and Performance of Myoelectric Prosthesis Control Strategies , 2017, bioRxiv.

[203]  Silvestro Micera,et al.  A closed-loop hand prosthesis with simultaneous intraneural tactile and position feedback , 2018 .

[204]  Dario Farina,et al.  The clinical relevance of advanced artificial feedback in the control of a multi-functional myoelectric prosthesis , 2018, Journal of NeuroEngineering and Rehabilitation.

[205]  A Mazzoni,et al.  Comparison of linear frequency and amplitude modulation for intraneural sensory feedback in bidirectional hand prostheses , 2018, Scientific Reports.

[206]  Kianoush Nazarpour,et al.  Myoelectric control with abstract decoders , 2018, Journal of neural engineering.

[207]  Rafael Granja-Vazquez,et al.  Illusory movement perception improves motor control for prosthetic hands , 2018, Science Translational Medicine.

[208]  Ahmed W Shehata,et al.  Audible Feedback Improves Internal Model Strength and Performance of Myoelectric Prosthesis Control , 2018, Scientific Reports.

[209]  A. Drohomirecka,et al.  Low-level light therapy reduces platelet destruction during extracorporeal circulation , 2018, Scientific Reports.

[210]  Albert H Vette,et al.  Using synchronized eye and motion tracking to determine high-precision eye-movement patterns during object-interaction tasks. , 2018, Journal of vision.

[211]  M. Ortiz-Catalán The Stochastic Entanglement and Phantom Motor Execution Hypotheses: A Theoretical Framework for the Origin and Treatment of Phantom Limb Pain , 2018, Front. Neurol..

[212]  Z. C. Thumser,et al.  Fitts’ Law in the Control of Isometric Grip Force With Naturalistic Targets , 2018, Front. Psychol..

[213]  Christian Cipriani,et al.  Discrete Vibro-Tactile Feedback Prevents Object Slippage in Hand Prostheses More Intuitively Than Other Modalities , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[214]  Thomas Stieglitz,et al.  Paradigms for restoration of somatosensory feedback via stimulation of the peripheral nervous system , 2017, Clinical Neurophysiology.

[215]  Scott M Tintle,et al.  Targeted Muscle Reinnervation Treats Neuroma and Phantom Pain in Major Limb Amputees: A Randomized Clinical Trial. , 2019, Annals of surgery.

[216]  Gunnar Blohm,et al.  Modeling in Neuroscience as a Decision Process , 2019 .

[217]  Silvestro Micera,et al.  Intraneural sensory feedback restores grip force control and motor coordination while using a prosthetic hand , 2019, Journal of neural engineering.

[218]  Loredana Zollo,et al.  Restoring tactile sensations via neural interfaces for real-time force-and-slippage closed-loop control of bionic hands , 2019, Science Robotics.

[219]  A. Arnold,et al.  XX sex chromosome complement promotes atherosclerosis in mice , 2019, Nature Communications.

[220]  Elizaveta V Okorokova,et al.  Biomimetic sensory feedback through peripheral nerve stimulation improves dexterous use of a bionic hand , 2019, Science Robotics.

[221]  Ranu Jung,et al.  Effects of vibrotactile feedback and grasp interface compliance on perception and control of a sensorized myoelectric hand , 2019, PloS one.

[222]  G Baud-Bovy,et al.  Optimal integration of intraneural somatosensory feedback with visual information: a single-case study , 2019, Scientific Reports.

[223]  Matteo Bianchi,et al.  Skin Stretch Haptic Feedback to Convey Closure Information in Anthropomorphic, Under-Actuated Upper Limb Soft Prostheses , 2019, IEEE Transactions on Haptics.

[224]  Strahinja Dosen,et al.  Psychometric characterization of incidental feedback sources during grasping with a hand prosthesis , 2019, Journal of NeuroEngineering and Rehabilitation.

[225]  Jon W. Sensinger,et al.  Optimized control mapping through user-tuned cost of effort, time, and reliability* , 2019, 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR).

[226]  Ahmed W. Shehata,et al.  When Less Is More – Discrete Tactile Feedback Dominates Continuous Audio Biofeedback in the Integrated Percept While Controlling a Myoelectric Prosthetic Hand , 2019, Front. Neurosci..

[227]  Z. C. Thumser,et al.  Using sensory discrimination in a foraging-style task to evaluate human upper-limb sensorimotor performance , 2019, Scientific Reports.

[228]  Prostheses—Assistive Technology—Upper , 2019, Encyclopedia of Biomedical Engineering.

[229]  Taro Toyoizumi,et al.  A Bayesian psychophysics model of sense of agency , 2019, Nature Communications.

[230]  Dustin J. Tyler,et al.  Learning of Artificial Sensation Through Long-Term Home Use of a Sensory-Enabled Prosthesis , 2019, Front. Neurosci..

[231]  Silvestro Micera,et al.  A closed-loop hand prosthesis with simultaneous intraneural tactile and position feedback , 2018, Science Robotics.

[232]  Maurizio Valle,et al.  Dual-Parameter Modulation Improves Stimulus Localization in Multichannel Electrotactile Stimulation , 2020, IEEE Transactions on Haptics.

[233]  Internal Models , 2020, Encyclopedia of Creativity, Invention, Innovation and Entrepreneurship.

[234]  Z. C. Thumser,et al.  Long-Term Home-Use of Sensory-Motor-Integrated Bidirectional Bionic Prosthetic Arms Promotes Functional, Perceptual, and Cognitive Changes , 2020, Frontiers in Neuroscience.

[235]  Strahinja Dosen,et al.  The Interaction Between Feedback Type and Learning in Routine Grasping With Myoelectric Prostheses , 2019, IEEE Transactions on Haptics.