Online myoelectric pattern recognition based on hybrid spatial features
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[1] Luigi Fortuna,et al. Adaptive Myoelectric Pattern Recognition Based on Hybrid Spatial Features of HD-sEMG Signals , 2020, Iranian Journal of Science and Technology, Transactions of Electrical Engineering.
[2] Luigi Fortuna,et al. Interactive Real-Time Control System for The Artificial Hand , 2020, Iraqi Journal for Electrical and Electronic Engineering.
[3] Luigi Fortuna,et al. Using the Robust High Density-surface Electromyography Features for Real-Time Hand Gestures Classification , 2020, IOP Conference Series: Materials Science and Engineering.
[4] E. Scheme,et al. Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity , 2020, Sensors.
[5] Paolo Bifulco,et al. Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation , 2019, Sensors.
[6] Clément Gosselin,et al. Pattern recognition based on HD-sEMG spatial features extraction for an efficient proportional control of a robotic arm , 2019, Biomed. Signal Process. Control..
[7] Tej Singh,et al. A visual cognizance based multi-resolution descriptor for human action recognition using key pose , 2019, AEU - International Journal of Electronics and Communications.
[8] Luigi Fortuna,et al. Robust hand gesture identification using envelope of HD-sEMG signal , 2019, ICICT '19.
[9] Mofeed Turky Rashid,et al. HD-sEMG Gestures Recognition by SVM Classifier for Controlling Prosthesis , 2019 .
[10] Roberto Díaz-Amador,et al. Using Image Processing Techniques and HD-EMG for Upper Limb Prosthesis Gesture Recognition , 2018, CIARP.
[11] Erik Scheme,et al. EMG Pattern Recognition in the Era of Big Data and Deep Learning , 2018, Big Data Cogn. Comput..
[12] Guido Bugmann,et al. Adaptive Windowing Framework for Surface Electromyogram-Based Pattern Recognition System for Transradial Amputees , 2018, Sensors.
[13] Hong Liu,et al. Robust EMG pattern recognition in the presence of confounding factors: features, classifiers and adaptive learning , 2018, Expert Syst. Appl..
[14] Fan Zhang,et al. An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition , 2017, Sensors.
[15] Kiyoshi Kotani,et al. A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition , 2017, Sensors.
[16] Yu Hu,et al. Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation , 2017, Sensors.
[17] Dario Farina,et al. Translating Research on Myoelectric Control into Clinics—Are the Performance Assessment Methods Adequate? , 2017, Front. Neurorobot..
[18] Weidong Geng,et al. Gesture recognition by instantaneous surface EMG images , 2016, Scientific Reports.
[19] Craig Sherstan,et al. Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching , 2016, Prosthetics and orthotics international.
[20] Heung-Il Suk,et al. Position-Independent Decoding of Movement Intention for Proportional Myoelectric Interfaces , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[21] Ju Yong Chang. Nonparametric Feature Matching Based Conditional Random Fields for Gesture Recognition from Multi-Modal Video , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[22] Guido Bugmann,et al. Improving the Performance Against Force Variation of EMG Controlled Multifunctional Upper-Limb Prostheses for Transradial Amputees , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[23] Miguel Angel Mañanas,et al. Prediction of isometric motor tasks and effort levels based on high-density EMG in patients with incomplete spinal cord injury , 2016, Journal of neural engineering.
[24] Miguel Angel Mañanas,et al. Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury , 2016, Journal of NeuroEngineering and Rehabilitation.
[25] Xinjun Sheng,et al. Reduced Daily Recalibration of Myoelectric Prosthesis Classifiers Based on Domain Adaptation , 2016, IEEE Journal of Biomedical and Health Informatics.
[26] 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).
[27] 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).
[28] 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.
[29] 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.
[30] Erik J. Scheme,et al. On the robustness of EMG features for pattern recognition based myoelectric control; A multi-dataset comparison , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[31] Elsa Andrea Kirchner,et al. Exoskeleton Technology in Rehabilitation: Towards an EMG-Based Orthosis System for Upper Limb Neuromotor Rehabilitation , 2013, J. Robotics.
[32] Levi J. Hargrove,et al. Classification of Simultaneous Movements Using Surface EMG Pattern Recognition , 2013, IEEE Transactions on Biomedical Engineering.
[33] Patrick M. Pilarski,et al. Adaptive artificial limbs: a real-time approach to prediction and anticipation , 2013, IEEE Robotics & Automation Magazine.
[34] Rajiv Kapoor,et al. Simple and intelligent system to recognize the expression of speech-disabled person , 2012, 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI).
[35] Ping Zhou,et al. High-Density Myoelectric Pattern Recognition Toward Improved Stroke Rehabilitation , 2012, IEEE Transactions on Biomedical Engineering.
[36] Dean J Krusienski,et al. Brain-computer interfaces in medicine. , 2012, Mayo Clinic proceedings.
[37] Monica Rojas-Martínez,et al. High-density surface EMG maps from upper-arm and forearm muscles , 2012, Journal of NeuroEngineering and Rehabilitation.
[38] 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.
[39] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[40] Silvestro Micera,et al. Bidirectional interfaces with the peripheral nervous system. , 2009, International review of neurobiology.
[41] D. Reinkensmeyer,et al. Review of control strategies for robotic movement training after neurologic injury , 2009, Journal of NeuroEngineering and Rehabilitation.
[42] 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..
[43] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[44] Adrian D. C. Chan,et al. Continuous myoelectric control for powered prostheses using hidden Markov models , 2005, IEEE Transactions on Biomedical Engineering.