Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses
暂无分享,去创建一个
[1] S. Cobb,et al. ELECTROMYOGRAPHIC STUDIES OF MUSCULAR FATIGUE IN MAN , 1923 .
[2] Sher ry Folsom-Meek,et al. Human Performance , 1953, Nature.
[3] Donald Laming,et al. Information theory of choice-reaction times , 1968 .
[4] H. Hamburger. Donald R. J. Laming. Information, theory of choice‐reaction times. New York: Academic Press, 1968 , 1969 .
[5] H. Kwatny,et al. An application of signal processing techniques to the study of myoelectric signals. , 1970, IEEE transactions on bio-medical engineering.
[6] L. Lindstrom,et al. Muscular fatigue and action potential conduction velocity changes studied with frequency analysis of EMG signals. , 1970, Electromyography.
[7] C. J. Luca,et al. Derivation of some parameters of myoelectric signals recorded during sustained constant force isometric contractions , 1975 .
[8] C. D. De Luca,et al. Frequency Parameters of the Myoelectric Signal as a Measure of Muscle Conduction Velocity , 1981, IEEE Transactions on Biomedical Engineering.
[9] Tadashi Masuda,et al. The Measurement of Muscle Fiber Conduction Velocity Using a Gradient Threshold Zero-Crossing Method , 1982, IEEE Transactions on Biomedical Engineering.
[10] C. D. De Luca,et al. Myoelectrical manifestations of localized muscular fatigue in humans. , 1984, Critical reviews in biomedical engineering.
[11] L. Arendt-Nielsen,et al. The relationship between mean power frequency of the EMG spectrum and muscle fibre conduction velocity. , 1985, Electroencephalography and clinical neurophysiology.
[12] G. Inbar,et al. Autoregressive Modeling of Surface EMG and Its Spectrum with Application to Fatigue , 1987, IEEE Transactions on Biomedical Engineering.
[13] Knaflitz,et al. Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions. , 1990, Journal of applied physiology.
[14] R. B. Knapp,et al. Real-time computer control using pattern recognition of the electromyogram , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.
[15] S.G. Meek,et al. Fatigue compensation of the electromyographic signal for prosthetic control and force estimation , 1993, IEEE Transactions on Biomedical Engineering.
[16] R.N. Scott,et al. A new strategy for multifunction myoelectric control , 1993, IEEE Transactions on Biomedical Engineering.
[17] Carlo J. De Luca,et al. The Use of Surface Electromyography in Biomechanics , 1997 .
[18] Toshio Tsuji,et al. An EMG controlled robotic manipulator using neural networks , 1997, Proceedings 6th IEEE International Workshop on Robot and Human Communication. RO-MAN'97 SENDAI.
[19] C.I. Christodoulou,et al. Unsupervised pattern recognition for the classification of EMG signals , 1999, IEEE Transactions on Biomedical Engineering.
[20] Alwin Luttmann,et al. Electromyographical indication of muscular fatigue in occupational field studies , 2000 .
[21] Wenwei Yu,et al. On-line Learning Based Electromyogram to Forearm Motion Classifier with Motor Skill Evaluation , 2000 .
[22] Paolo Bonato,et al. Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions , 2001, IEEE Transactions on Biomedical Engineering.
[23] R. Scott,et al. The short-time Fourier transform and muscle fatigue assessment in dynamic contractions. , 2001, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.
[24] Wenwei Yu,et al. Mutual Adaptation in a Prosthetics Application , 2003, Embodied Artificial Intelligence.
[25] Toshio Tsuji,et al. A human-assisting manipulator teleoperated by EMG signals and arm motions , 2003, IEEE Trans. Robotics Autom..
[26] Dinesh Kant Kumar,et al. Wavelet analysis of surface electromyography , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[27] G. Hägg. Electromyographic fatigue analysis based on the number of zero crossings , 1981, Pflügers Archiv.
[28] Wookho Son,et al. A new means of HCI: EMG-MOUSE , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).
[29] R. Merletti,et al. Median frequency of the myoelectric signal , 1984, European Journal of Applied Physiology and Occupational Physiology.
[30] T. Masuda,et al. Relationships between muscle fibre conduction velocity and frequency parameters of surface EMG during sustained contraction , 1983, European Journal of Applied Physiology and Occupational Physiology.
[31] Adrian D. C. Chan,et al. Continuous myoelectric control for powered prostheses using hidden Markov models , 2005, IEEE Transactions on Biomedical Engineering.
[32] Ferdinando A Mussa-Ivaldi,et al. Remapping hand movements in a novel geometrical environment. , 2005, Journal of neurophysiology.
[33] R. Jennane,et al. An EMG fractal indicator having different sensitivities to changes in force and muscle fatigue during voluntary static muscle contractions. , 2005, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.
[34] B. Hudgins,et al. REAL-TIME MYOELECTRIC CONTROL IN A VIRTUAL ENVIRONMENT TO RELATE USABILITY VS. ACCURACY , 2005 .
[35] Hiroshi Yokoi,et al. Adaptable EMG Prosthetic Hand using On-line Learning Method -Investigation of Mutual Adaptation between Human and Adaptable Machine , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.
[36] B. Hudgins,et al. The effect of electrode displacements on pattern recognition based myoelectric control , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.
[37] Z. Zenn Bien,et al. Robust EMG Pattern Recognition to Muscular Fatigue Effect for Human-Machine Interaction , 2006, MICAI.
[38] Hiroshi Yokoi,et al. Real-time Learning Method for Adaptable Motion-Discrimination using Surface EMG Signal , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[39] G. Inbar,et al. Monitoring surface EMG spectral changes by the zero crossing rate , 2006, Medical and Biological Engineering and Computing.
[40] Patrick van der Smagt,et al. Learning EMG control of a robotic hand: towards active prostheses , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..
[41] B. Hudgins,et al. A Real-Time Pattern Recognition Based Myoelectric Control Usability Study Implemented in a Virtual Environment , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[42] Ping Zhou,et al. Decoding a new neural machine interface for control of artificial limbs. , 2007, Journal of neurophysiology.
[43] Hua Caohua,et al. Analysis of Muscular Fatigue during Cyclic Dynamic Movement , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[44] E. Biddiss,et al. Upper-Limb Prosthetics: Critical Factors in Device Abandonment , 2007, American journal of physical medicine & rehabilitation.
[45] Levi J. Hargrove,et al. A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control , 2008, Biomed. Signal Process. Control..
[46] A. Timmermans,et al. Technology-assisted training of arm-hand skills in stroke: concepts on reacquisition of motor control and therapist guidelines for rehabilitation technology design , 2009, Journal of NeuroEngineering and Rehabilitation.
[47] Marie-Françoise Lucas,et al. Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization , 2008, Biomed. Signal Process. Control..
[48] Andrew Jackson,et al. Learning a Novel Myoelectric-Controlled Interface Task , 2008, Journal of neurophysiology.
[49] R. Enoka,et al. Muscle fatigue: what, why and how it influences muscle function , 2008, The Journal of physiology.
[50] Kongqiao Wang,et al. An Adaptive Feature Extractor for Gesture SEMG Recognition , 2008, ICMB.
[51] Angelo Davalli,et al. DESIGN OF A NEW EMG SENSOR FOR UPPER LIMB PROSTHETIC CONTROL AND REAL TIME FREQUENCY ANALYSIS , 2008 .
[52] Patrick van der Smagt,et al. Surface EMG in advanced hand prosthetics , 2008, Biological Cybernetics.
[53] Giulio Sandini,et al. Multi-subject/daily-life activity EMG-based control of mechanical hands , 2009, Journal of NeuroEngineering and Rehabilitation.
[54] Panagiotis K. Artemiadis,et al. Assessment of muscle fatigue using a probabilistic framework for an EMG-based robot control scenario , 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.
[55] M. R. Al-Mulla,et al. Classification of localized muscle fatigue with genetic programming on sEMG during isometric contraction , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[56] Mario Cifrek,et al. Surface EMG based muscle fatigue evaluation in biomechanics. , 2009, Clinical biomechanics.
[57] Max E Valentinuzzi,et al. Robust EMG sensing system based on data fusion for myoelectric control of a robotic arm , 2009, Biomedical engineering online.
[58] Abdulhamit Subasi,et al. Muscle Fatigue Detection in EMG Using Time–Frequency Methods, ICA and Neural Networks , 2009, Journal of Medical Systems.
[59] Robert D. Lipschutz,et al. Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. , 2009, JAMA.
[60] Todd A. Kuiken,et al. The Effect of ECG Interference on Pattern-Recognition-Based Myoelectric Control for Targeted Muscle Reinnervated Patients , 2009, IEEE Transactions on Biomedical Engineering.
[61] J.W. Sensinger,et al. Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[62] Dennis C. Tkach,et al. Study of stability of time-domain features for electromyographic pattern recognition , 2010, Journal of NeuroEngineering and Rehabilitation.
[63] Giulio Sandini,et al. Model adaptation with least-squares SVM for adaptive hand prosthetics , 2009, 2009 IEEE International Conference on Robotics and Automation.
[64] L. R. Altimari,et al. Fourier and wavelet spectral analysis of EMG signals in supramaximal constant load dynamic exercise , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.
[65] Francisco Sepulveda,et al. Novel Feature Modelling the Prediction and Detection of sEMG Muscle Fatigue towards an Automated Wearable System , 2010, Sensors.
[66] A.D.C. Chan,et al. Examining the adverse effects of limb position on pattern recognition based myoelectric control , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.
[67] Fan Zhang,et al. Design of a robust EMG sensing interface for pattern classification , 2010, Journal of neural engineering.
[68] Marco Platzner,et al. Fluctuating emg signals: Investigating long-term effects of pattern matching algorithms , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.
[69] M. Swiontkowski. Targeted Muscle Reinnervation for Real-time Myoelectric Control of Multifunction Artificial Arms , 2010 .
[70] Panagiotis K. Artemiadis,et al. An EMG-Based Robot Control Scheme Robust to Time-Varying EMG Signal Features , 2010, IEEE Transactions on Information Technology in Biomedicine.
[71] Othman O. Khalifa,et al. Hand motion detection from EMG signals by using ANN based classifier for human computer interaction , 2011, 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization.
[72] Erik J. Scheme,et al. Selective Classification for Improved Robustness of Myoelectric Control Under Nonideal Conditions , 2011, IEEE Transactions on Biomedical Engineering.
[73] Guanglin Li,et al. Effect of upper-limb positions on motion pattern recognition using electromyography , 2011, 2011 4th International Congress on Image and Signal Processing.
[74] Øyvind Stavdahl,et al. A multi-modal approach for hand motion classification using surface EMG and accelerometers , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[75] Jun Morimoto,et al. Learning and adaptation of a Stylistic Myoelectric Interface: EMG-based robotic control with individual user differences , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.
[76] Ann M. Simon,et al. Prosthesis-Guided Training For Practical Use Of Pattern Recognition Control Of Prostheses , 2011 .
[77] K. Englehart,et al. Resolving the Limb Position Effect in Myoelectric Pattern Recognition , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[78] Sethuraman Panchanathan,et al. Topology Preserving Domain Adaptation for Addressing Subject Based Variability in SEMG Signal , 2011, AAAI Spring Symposium: Computational Physiology.
[79] Ashley N. Johnson,et al. Dual-task motor performance with a tongue-operated assistive technology compared with hand operations , 2012, Journal of NeuroEngineering and Rehabilitation.
[80] Guanglin Li,et al. Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees , 2012, Journal of NeuroEngineering and Rehabilitation.
[81] 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.
[82] Panagiotis K. Artemiadis,et al. A Switching Regime Model for the EMG-Based Control of a Robot Arm , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[83] Dario Farina,et al. Effect of arm position on the prediction of kinematics from EMG in amputees , 2012, Medical & Biological Engineering & Computing.
[84] Ahmet Alkan,et al. Identification of EMG signals using discriminant analysis and SVM classifier , 2012, Expert Syst. Appl..
[85] Marco Platzner,et al. Reducing classification accuracy degradation of pattern recognition based myoelectric control caused by electrode shift using a high density electrode array , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[86] Kevin Englehart,et al. High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control. , 2012, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.
[87] Lei Shi,et al. Time-dependent spectral features for limb position invariant myoelectric pattern recognition , 2012, 2012 International Symposium on Communications and Information Technologies (ISCIT).
[88] Pornchai Phukpattaranont,et al. INVESTIGATING LONG-TERM EFFECTS OF FEATURE EXTRACTION METHODS FOR CONTINUOUS EMG PATTERN CLASSIFICATION , 2012 .
[89] S. Jain,et al. Improving long term myoelectric decoding, using an adaptive classifier with label correction , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).
[90] Dingguo Zhang,et al. Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control , 2013, Journal of NeuroEngineering and Rehabilitation.
[91] A. M. Simon,et al. Patient Training for Functional Use of Pattern Recognition–Controlled Prostheses , 2012, Journal of prosthetics and orthotics : JPO.
[92] Jianda Han,et al. PCA and LDA for EMG-based control of bionic mechanical hand , 2012, 2012 IEEE International Conference on Information and Automation.
[93] Max Ortiz-Catalan,et al. BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms , 2013, Source Code for Biology and Medicine.
[94] Dingguo Zhang,et al. Effect of dynamic change of arm position on myoelectric pattern recognition , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).
[95] Klaus-Robert Müller,et al. Spatial Filtering for Robust Myoelectric Control , 2012, IEEE Transactions on Biomedical Engineering.
[96] Huosheng Hu,et al. The Usefulness of Mean and Median Frequencies in Electromyography Analysis , 2012 .
[97] Todd A. Kuiken,et al. Improving Myoelectric Pattern Recognition Robustness to Electrode Shift by Changing Interelectrode Distance and Electrode Configuration , 2012, IEEE Transactions on Biomedical Engineering.
[98] Pornchai Phukpattaranont,et al. Feature reduction and selection for EMG signal classification , 2012, Expert Syst. Appl..
[99] Dario Farina,et al. Long term stability of surface EMG pattern classification for prosthetic control , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[100] Sirinee Thongpanja,et al. Mean and Median Frequency of EMG Signal to Determine Muscle Force based on Time- dependent Power Spectrum , 2013 .
[101] Christian Cipriani,et al. Abstract and Proportional Myoelectric Control for Multi-Fingered Hand Prostheses , 2013, Annals of Biomedical Engineering.
[102] Xinjun Sheng,et al. Effects of Long-Term Myoelectric Signals on Pattern Recognition , 2013, ICIRA.
[103] 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.
[104] Angkoon Phinyomark,et al. EMG feature evaluation for improving myoelectric pattern recognition robustness , 2013, Expert Syst. Appl..
[105] Angkoon Phinyomark,et al. A feasibility study on the use of anthropometric variables to make muscle-computer interface more practical , 2013, Eng. Appl. Artif. Intell..
[106] Levi J. Hargrove,et al. Classification of Simultaneous Movements Using Surface EMG Pattern Recognition , 2013, IEEE Transactions on Biomedical Engineering.
[107] Panagiotis Artemiadis,et al. Beyond User-Specificity for EMG Decoding Using Multiresolution Muscle Synergy Analysis , 2013 .
[108] Panagiotis Artemiadis,et al. User-Independent Hand Motion Classification With Electromyography , 2013 .
[109] Jun Morimoto,et al. Bilinear Modeling of EMG Signals to Extract User-Independent Features for Multiuser Myoelectric Interface , 2013, IEEE Transactions on Biomedical Engineering.
[110] Barbara Caputo,et al. Improving Control of Dexterous Hand Prostheses Using Adaptive Learning , 2013, IEEE Transactions on Robotics.
[111] Manfredo Atzori,et al. Electromyography data for non-invasive naturally-controlled robotic hand prostheses , 2014, Scientific Data.
[112] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[113] Xinjun Sheng,et al. Quantification and solutions of arm movements effect on sEMG pattern recognition , 2014, Biomed. Signal Process. Control..
[114] S. Ramakrishnan,et al. Multiscale feature based analysis of surface EMG signals under fatigue and non-fatigue conditions , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[115] Barbara Caputo,et al. Multi-source Adaptive Learning for Fast Control of Prosthetics Hand , 2014, 2014 22nd International Conference on Pattern Recognition.
[116] Dario Farina,et al. Is Accurate Mapping of EMG Signals on Kinematics Needed for Precise Online Myoelectric Control? , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[117] 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.
[118] Panagiotis K. Artemiadis,et al. Learning efficient control of robots using myoelectric interfaces , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).
[119] Nitish V. Thakor,et al. User Training for Pattern Recognition-Based Myoelectric Prostheses: Improving Phantom Limb Movement Consistency and Distinguishability , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[120] Panagiotis Artemiadis,et al. Embedded Human Control of Robots Using Myoelectric Interfaces , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[121] Marco Platzner,et al. Towards robust HD EMG pattern recognition: Reducing electrode displacement effect using structural similarity , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[122] Jaime Valls Miró,et al. Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features , 2014, Neural Networks.
[123] Rami N. Khushaba,et al. Correlation Analysis of Electromyogram Signals for Multiuser Myoelectric Interfaces , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[124] Honghai Liu,et al. Robust sEMG electrodes configuration for pattern recognition based prosthesis control , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[125] Manfredo Atzori,et al. Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-Based Hand Movement Classification , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[126] Paweł Bartuzi,et al. Assessment of muscle load and fatigue with the usage of frequency and time-frequency analysis of the EMG signal. , 2014, Acta of bioengineering and biomechanics.
[127] Marco Paleari,et al. Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography , 2014, PloS one.
[128] Reza Langari,et al. A Performance Comparison of EMG Classification Methods for Hand and Finger Motion , 2014 .
[129] K. Englehart,et al. On the Suitability of Integrating Accelerometry Data with Electromyography Signals for Resolving the Effect of Changes in Limb Position during Dynamic Limb Movement , 2014 .
[130] Barbara Caputo,et al. Stable myoelectric control of a hand prosthesis using non-linear incremental learning , 2014, Front. Neurorobot..
[131] Xinjun Sheng,et al. User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control , 2015, Journal of neural engineering.
[132] Panagiotis K. Artemiadis,et al. Proportional Myoelectric Control of Robots: Muscle Synergy Development Drives Performance Enhancement, Retainment, and Generalization , 2015, IEEE Transactions on Robotics.
[133] 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.
[134] Max Ortiz-Catalan,et al. Offline accuracy: A potentially misleading metric in myoelectric pattern recognition for prosthetic control , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[135] Xinjun Sheng,et al. Towards zero training for myoelectric control based on a wearable wireless sEMG armband , 2015, 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).
[136] Jie Liu,et al. Adaptive myoelectric pattern recognition toward improved multifunctional prosthesis control. , 2015, Medical engineering & physics.
[137] Xiaorong Zhang,et al. A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition , 2015, Journal of NeuroEngineering and Rehabilitation.
[138] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[139] Ganesh R. Naik,et al. Nonnegative Matrix Factorization for the Identification of EMG Finger Movements: Evaluation Using Matrix Analysis , 2015, IEEE Journal of Biomedical and Health Informatics.
[140] Xinjun Sheng,et al. Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns , 2015, Journal of NeuroEngineering and Rehabilitation.
[141] Joseph L. Betthauser,et al. Limb-position robust classification of myoelectric signals for prosthesis control using sparse representations , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[142] Jesse Jur,et al. Fabric-Based Wearable Dry Electrodes for Body Surface Biopotential Recording , 2016, IEEE Transactions on Biomedical Engineering.
[143] Heung-Il Suk,et al. Position-Independent Decoding of Movement Intention for Proportional Myoelectric Interfaces , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[144] Enrico Pagello,et al. Online subject-independent modeling of sEMG signals for the motion of a single robot joint , 2016, 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob).
[145] Ganesh R. Naik,et al. Principal Component Analysis Applied to Surface Electromyography: A Comprehensive Review , 2016, IEEE Access.
[146] Xinjun Sheng,et al. Reduced Daily Recalibration of Myoelectric Prosthesis Classifiers Based on Domain Adaptation , 2016, IEEE Journal of Biomedical and Health Informatics.
[147] Manfredo Atzori,et al. Deep learning with convolutional neural networks: a resource for the control of robotic prosthetic hands via electromyography , 2016 .
[148] Dario Farina,et al. A Novel Percutaneous Electrode Implant for Improving Robustness in Advanced Myoelectric Control , 2016, Front. Neurosci..
[149] Manfredo Atzori,et al. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands , 2016, Front. Neurorobot..
[150] Dario Farina,et al. Improving the Robustness of Myoelectric Pattern Recognition for Upper Limb Prostheses by Covariate Shift Adaptation , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[151] Todd A. Kuiken,et al. Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control , 2016, Front. Neurorobot..
[152] 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.
[153] Kianoush Nazarpour,et al. Combined influence of forearm orientation and muscular contraction on EMG pattern recognition , 2016, Expert Syst. Appl..
[154] Sethu Vijayakumar,et al. Real-time classification of multi-modal sensory data for prosthetic hand control , 2016, 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob).
[155] Xinjun Sheng,et al. Towards Zero Retraining for Myoelectric Control Based on Common Model Component Analysis , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[156] Dario Farina,et al. Translating Research on Myoelectric Control into Clinics—Are the Performance Assessment Methods Adequate? , 2017, Front. Neurorobot..
[157] Hong Liu,et al. Classification of Multiple Finger Motions During Dynamic Upper Limb Movements , 2017, IEEE Journal of Biomedical and Health Informatics.
[158] Caihua Xiong,et al. Simultaneous and Continuous Estimation of Shoulder and Elbow Kinematics from Surface EMG Signals , 2017, Frontiers in neuroscience.
[159] Beth Jelfs,et al. Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network , 2017, Front. Neurosci..
[160] Yu Hu,et al. Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation , 2017, Sensors.
[161] Sethu Vijayakumar,et al. Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements , 2017, Journal of NeuroEngineering and Rehabilitation.
[162] Clément Gosselin,et al. Deep Learning for Electromyographic Hand Gesture Signal Classification by Leveraging Transfer Learning , 2018, ArXiv.
[163] Mohan S. Kankanhalli,et al. A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface , 2017, Pattern Recognit. Lett..