Distance and mutual information methods for EMG feature and channel subset selection for classification of hand movements
暂无分享,去创建一个
Christian Cipriani | Haitham M. Al-Angari | Gunter Kanitz | Sergio Tarantino | C. Cipriani | Gunter Kanitz | S. Tarantino
[1] S H Park,et al. EMG pattern recognition based on artificial intelligence techniques. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[2] Ilja Kuzborskij,et al. On the challenge of classifying 52 hand movements from surface electromyography , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[3] Erik Scheme,et al. Training Strategies for Mitigating the Effect of Proportional Control on Classification in Pattern Recognition–Based Myoelectric Control , 2013, Journal of prosthetics and orthotics : JPO.
[4] Jaime Valls Miró,et al. Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features , 2014, Neural Networks.
[5] K. Englehart,et al. Resolving the Limb Position Effect in Myoelectric Pattern Recognition , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[6] Bruce C. Wheeler,et al. EMG feature evaluation for movement control of upper extremity prostheses , 1995 .
[7] C. Cipriani,et al. The Effects of Weight and Inertia of the Prosthesis on the Sensitivity of Electromyographic Pattern Recognition in Relax State , 2012 .
[8] Christian Cipriani,et al. Feature and Channel Selection Using Correlation Based Method for Hand Posture Classification in Multiple Arm Positions , 2014 .
[9] P. Mahalanobis. On the generalized distance in statistics , 1936 .
[10] Keinosuke Fukunaga,et al. Introduction to Statistical Pattern Recognition , 1972 .
[11] Kevin B. Englehart,et al. A robust, real-time control scheme for multifunction myoelectric control , 2003, IEEE Transactions on Biomedical Engineering.
[12] Shuxiang Guo,et al. Comparison of sEMG-Based Feature Extraction and Motion Classification Methods for Upper-Limb Movement , 2015, Sensors.
[13] Henri Maître,et al. On the relevance of linear discriminative features , 2010, Inf. Sci..
[14] Fuhui Long,et al. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.
[16] 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.
[17] Yong Deng,et al. A novel feature selection method based on CFS in cancer recognition , 2012, 2012 IEEE 6th International Conference on Systems Biology (ISB).
[18] Frederick E. Croxton,et al. Applied General Statistics. , 1940 .
[19] 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.
[20] Guanglin Li,et al. EMG feature assessment for myoelectric pattern recognition and channel selection: a study with incomplete spinal cord injury. , 2014, Medical engineering & physics.
[21] F. Finley,et al. Myocoder studies of multiple myopotential response. , 1967, Archives of physical medicine and rehabilitation.
[22] Weidong Yang,et al. Erratum to “Biodistribution and SPECT Imaging Study of 99mTc Labeling NGR Peptide in Nude Mice Bearing Human HepG2 Hepatoma” , 2014, BioMed Research International.
[23] 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.
[24] J. Richman,et al. Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.
[25] F. K. Lam,et al. Fuzzy EMG classification for prosthesis control. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[26] Gang Wang,et al. The Analysis of Hand Movement Distinction Based on Relative Frequency Band Energy Method , 2014, BioMed research international.
[27] Kevin B. Englehart,et al. A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.
[28] Huosheng Hu,et al. Myoelectric control systems - A survey , 2007, Biomed. Signal Process. Control..
[29] Angkoon Phinyomark,et al. EMG feature evaluation for improving myoelectric pattern recognition robustness , 2013, Expert Syst. Appl..
[30] Zhaojie Ju,et al. Surface EMG Based Hand Manipulation Identification Via Nonlinear Feature Extraction and Classification , 2013, IEEE Sensors Journal.
[31] David G. Stork,et al. Pattern Classification , 1973 .
[32] 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..
[33] Dennis C. Tkach,et al. Study of stability of time-domain features for electromyographic pattern recognition , 2010, Journal of NeuroEngineering and Rehabilitation.
[34] Huosheng Hu,et al. Feature-channel subset selection for optimising myoelectric human-machine interface design , 2013 .
[35] D Graupe,et al. Multifunctional prosthesis and orthosis control via microcomputer identification of temporal pattern differences in single-site myoelectric signals. , 1982, Journal of biomedical engineering.
[36] J. Roitman,et al. ACSM's Resource Manual for Guidelines for Exercise Testing and Prescription , 1998 .
[37] 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.
[38] Christian Antfolk,et al. Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[39] Niki Pissinou,et al. Correlation-Based Feature Selection for Intrusion Detection Design , 2007, MILCOM 2007 - IEEE Military Communications Conference.
[40] R.N. Scott,et al. A new strategy for multifunction myoelectric control , 1993, IEEE Transactions on Biomedical Engineering.
[41] 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.
[42] Nathan E. Bunderson,et al. Quantification of Feature Space Changes With Experience During Electromyogram Pattern Recognition Control , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[43] A. Al-Jumaily,et al. Channel and Feature Selection in Multifunction Myoelectric Control , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[44] Lloyd A. Smith,et al. Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper , 1999, FLAIRS.
[45] Finley Fr,et al. Myocoder studies of multiple myopotential response. , 1967 .