M. R. Al-mulla, F. Sepulveda, and M. Colley, A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue, Sensors, vol.11, issue.12, pp.3545-3594, 2011.
DOI : 10.3390/s110403545

R. E. Anakwe, J. S. Huntley, and J. E. Mceachan, Grip strength and forearm circumference in a healthy population, The Journal of Hand Surgery: Journal of the British Society for Surgery of the Hand, vol.32, issue.2, pp.203-209, 2007.
DOI : 10.1016/j.jhsb.2006.11.003

A. Andews, E. Morin, and L. Mclean, Optimal electrode configurations for finger movement classification using EMG, Proceedings of the 31st Annual International Conference of the IEEE EMBS, pp.2987-2990, 2009.

S. P. Arjunan, Fractal features of surface electromyogram: A new measure for low level muscle activation, 2008.

S. P. Arjunan and D. K. Kumar, Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors, Journal of NeuroEngineering and Rehabilitation, vol.7, issue.1, 2010.
DOI : 10.1186/1743-0003-7-53

S. P. Arjunan and D. K. Kumar, FRACTAL PROPERTIES OF SURFACE ELECTROMYOGRAM FOR CLASSIFICATION OF LOW-LEVEL HAND MOVEMENTS FROM SINGLE-CHANNEL FOREARM MUSCLE ACTIVITY, Journal of Mechanics in Medicine and Biology, vol.11, issue.03, pp.581-590, 2011.
DOI : 10.1142/S0219519411003867

A. B. Barreto, S. D. Scargle, and M. Adjouadi, A practical EMG-based human-computer interface for users with motor disabilities, Journal of Rehabilitation Research and Development, vol.37, issue.1, pp.53-64, 2000.

H. Benko, T. S. Saponas, D. Morris, and D. Tan, Enhancing input on and above the interactive surface with muscle sensing, Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces, ITS '09, pp.93-100, 2009.
DOI : 10.1145/1731903.1731924

L. A. Bolgla and T. L. Uhl, Reliability of electromyographic normalization methods for evaluating the hip musculature, Journal of Electromyography and Kinesiology, vol.17, issue.1, pp.102-111, 2007.
DOI : 10.1016/j.jelekin.2005.11.007

R. Boostani and M. H. Moradi, Evaluation of the forearm EMG signal features for the control of a prosthetic hand, Physiological Measurement, vol.24, issue.2, pp.309-319, 2003.
DOI : 10.1088/0967-3334/24/2/307

A. Boschmann, P. Kaufmann, M. Platzner, and M. Winkler, Towards multi-movement hand prostheses: Combining adaptive classification with high precision sockets, Proceedings of 2nd European Conference Technically Assisted Rehabilitation, pp.1-4, 2009.

K. Brzostowski and M. Zieba, Analysis of Human Arm Motions Recognition Algorithms for System to Visualize Virtual Arm, 2011 21st International Conference on Systems Engineering, pp.422-426, 2011.
DOI : 10.1109/ICSEng.2011.83

J. A. Cannan and H. Hu, Automatic Circumference Measurement for Aiding in the Estimation of Maximum Voluntary Contraction (MVC) in EMG Systems, Proceedings of the 4th international conference on Intelligent Robotics and Applications, pp.202-211, 2011.
DOI : 10.1007/978-3-642-25486-4_21

M. Cifrek, V. Medved, S. Tonkovi?, and S. Ostoji?, Surface EMG based muscle fatigue evaluation in biomechanics, Clinical Biomechanics, vol.24, issue.4, pp.327-340, 2009.
DOI : 10.1016/j.clinbiomech.2009.01.010

F. H. Chan, Y. S. Yang, F. K. Lam, Y. T. Zhang, and P. A. Parker, Fuzzy EMG classification for prosthesis control, IEEE Transactions on Rehabilitation Engineering, vol.8, issue.3, pp.305-311, 2000.
DOI : 10.1109/86.867872

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.467.9371

L. Chen, Y. Geng, and G. Li, Effect of upper-limb positions on motion pattern recognition using electromyography, 2011 4th International Congress on Image and Signal Processing, pp.139-142, 2011.
DOI : 10.1109/CISP.2011.6100025

E. A. Clancy and N. Hogan, Probability density of the surface electromyogram and its relation to amplitude detectors, IEEE Transactions on Biomedical Engineering, vol.46, issue.6, pp.730-739, 1999.
DOI : 10.1109/10.764949

D. Luca and C. J. , Myoelectrical manifestations of localized muscular fatigue in humans, Critical Reviews in Biomedical Engineering, vol.11, issue.4, pp.254-279, 1984.

D. Luca and C. J. , Surface electromyography: Detection and recording. Retrieved from http, 2002.

D. Luca, C. J. Gilmore, L. D. Kuznetsov, M. Roy, and S. H. , Filtering the surface EMG signal: Movement artifact and baseline noise contamination, Journal of Biomechanics, issue.8, pp.43-1573, 2010.

D. Leva and P. , Adjustments to Zatsiorsky-Seluyanov's segment inertia parameters, Journal of Biomechanics, vol.29, issue.9, pp.1223-1230, 1996.
DOI : 10.1016/0021-9290(95)00178-6

Y. C. Du, C. H. Lin, L. Y. Shyu, and T. Chen, Portable hand motion classifier for multi-channel surface electromyography recognition using gray relational analysis, Expert Systems with Applications, issue.6, pp.37-4283, 2010.

Y. C. Du, L. Y. Shyu, and W. Hu, The effect of combining stationary wavelet transform and independent component analysis in the multichannel SEMGs hand motion identification system, Journal of Medical and Biological Engineering, vol.26, issue.1, pp.9-14, 2006.

R. Esteller, G. Vachtsevanos, J. Echauz, and B. Litt, A comparison of waveform fractal dimension algorithms, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, vol.48, issue.2, pp.177-1831, 2001.
DOI : 10.1109/81.904882

A. L. Fougner, Proportional myoelectric control of a multifunction upper-limb prosthesis, 2007.

A. Fougner, E. Scheme, A. D. Chan, K. Englehart, and Ø. Stavdahl, Resolving the Limb Position Effect in Myoelectric Pattern Recognition, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.19, issue.6, pp.644-651, 2011.
DOI : 10.1109/TNSRE.2011.2163529

J. A. Gitter and M. J. Czerniecki, Fractal analysis of the electromyographic interference pattern, Journal of Neuroscience Methods, vol.58, issue.1-2, pp.103-108, 1995.
DOI : 10.1016/0165-0270(94)00164-C

V. Gupta, S. Suryanarayanan, and N. P. Reddy, Fractal analysis of surface EMG signals from the biceps, International Journal of Medical Informatics, vol.45, issue.3, pp.185-192, 1997.
DOI : 10.1016/S1386-5056(97)00029-4

T. Higuchi, Approach to an irregular time series on the basis of the fractal theory, Physica D: Nonlinear Phenomena, vol.31, issue.2, pp.31-277, 1988.
DOI : 10.1016/0167-2789(88)90081-4

A. L. Hof, C. N. Pronk, and J. A. Van-best, Comparison between EMG to force processing and kinetic analysis for the calf muscle moment in walking and stepping, Journal of Biomechanics, vol.20, issue.2, pp.167-178, 1987.
DOI : 10.1016/0021-9290(87)90308-3

N. Hogan and R. W. Mann, Myoelectric Signal Processing: Optimal Estimation Applied to Electromyography - Part I: Derivation of the Optimal Myoprocessor, IEEE Transactions on Biomedical Engineering, vol.27, issue.7, pp.27-382, 1980.
DOI : 10.1109/TBME.1980.326652

K. R. Holzbaur, W. M. Murray, G. E. Gold, and S. L. Delp, Upper limb muscle volumes in adult subjects, Journal of Biomechanics, vol.40, issue.4, pp.742-749, 2007.
DOI : 10.1016/j.jbiomech.2006.11.011

X. Hu, Z. Wang, and X. Ren, Classification of surface EMG signal with fractal dimension, Journal of Zhejiang University SCIENCE, vol.6, issue.8, pp.844-848, 2005.
DOI : 10.1631/jzus.2005.B0844

B. Hudgins, P. Parker, and R. N. Scott, A new strategy for multifunction myoelectric control, IEEE Transactions on Biomedical Engineering, vol.40, issue.1, pp.82-94, 1993.
DOI : 10.1109/10.204774

S. Jain, G. Singhal, R. J. Smith, R. Kaliki, and N. Thakor, Improving long term myoelectric decoding, using an adaptive classifier with label correction, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp.532-537, 2012.
DOI : 10.1109/BioRob.2012.6290901

N. Jiang, S. Muceli, B. Graimann, and D. Farina, Effect of arm position on the prediction of kinematics from EMG in amputees, Medical & Biological Engineering & Computing, vol.59, issue.4, pp.143-151, 2013.
DOI : 10.1007/s11517-012-0979-4

N. Jindapetch, S. Chewae, and P. Phukpattaranont, FPGA implementations of an ADALINE adaptive filter for power-line noise cancellation in surface electromyography signals, Measurement, vol.45, issue.3, pp.45-405, 2012.
DOI : 10.1016/j.measurement.2011.11.004

E. N. Kamavuako, D. Farina, K. Yoshida, and W. Jensen, Relationship between grasping force and features of single-channel intramuscular EMG signals, Journal of Neuroscience Methods, vol.185, issue.1, pp.143-150, 2009.
DOI : 10.1016/j.jneumeth.2009.09.006

E. N. Kamavuako, J. C. Rosenvang, M. F. Bøg, A. Smidstrup, E. Erkocevic et al., Influence of the feature space on the estimation of hand grasping force from intramuscular EMG, Biomedical Signal Processing and Control, vol.8, issue.1, pp.1-5, 2013.
DOI : 10.1016/j.bspc.2012.05.002

P. Kaufmann, L. Englehart, and M. Platzner, Fluctuating emg signals: Investigating long-term effects of pattern matching algorithms, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp.6357-6360, 2010.
DOI : 10.1109/IEMBS.2010.5627288

C. Kendall, E. D. Lemaire, Y. Losier, A. Chan, and B. Hudgins, A novel approach to surface electromyography: an exploratory study of electrode-pair selection based on signal characteristics, Journal of NeuroEngineering and Rehabilitation, vol.9, issue.1, 2012.
DOI : 10.1016/j.jelekin.2006.06.001

R. N. Khushaba and S. Kodagoda, Electromyogram (EMG) feature reduction using Mutual Components Analysis for multifunction prosthetic fingers control, 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV), pp.1534-1539, 2012.
DOI : 10.1109/ICARCV.2012.6485374

R. N. Khushaba, S. Kodagoda, D. Liu, and G. Dissanayake, Muscle computer interfaces for driver distraction reduction, Computer Methods and Programs in Biomedicine, vol.110, issue.2, pp.137-149, 2013.
DOI : 10.1016/j.cmpb.2012.11.002

K. S. Kim, H. H. Choi, C. S. Moon, and C. W. Mun, Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions, Current Applied Physics, vol.11, issue.3, pp.740-745, 2011.
DOI : 10.1016/j.cap.2010.11.051

S. J. Kim, E. C. Jeong, S. M. Lee, and Y. R. Song, Improvements of Multi-features Extraction for EMG for Estimating Wrist Movements, The Transactions of The Korean Institute of Electrical Engineers, vol.61, issue.5, pp.61-757, 2012.
DOI : 10.5370/KIEE.2012.61.5.757

A. S. Kundu, O. Mazumder, and S. Bhaumik, Design of wearable, low power, single supply surface EMG extractor unit for wireless monitoring, Proceedings of the 2nd International Conference on Nanotechnology and Biosensors, pp.69-74, 2011.

J. H. Lawrence and C. J. De-luca, Myoelectric signal versus force relationship in different human muscles, Journal of Applied Physiology, vol.54, issue.6, pp.1653-1659, 1983.

K. Li, D. J. Hewson, J. Duchene, and J. Hogrel, Predicting maximal grip strength using hand circumference, Manual Therapy, vol.15, issue.6, pp.579-585, 2010.
DOI : 10.1016/j.math.2010.06.010

G. Li, Y. Li, L. Yu, and Y. Geng, Conditioning and Sampling Issues of EMG Signals in Motion Recognition of Multifunctional Myoelectric Prostheses, Annals of Biomedical Engineering, vol.98, issue.5, pp.1779-1787, 2011.
DOI : 10.1007/s10439-011-0265-x

W. S. Marras and K. G. Davis, A non-MVC EMG normalization technique for the trunk musculature: Part 1. Method development, Journal of Electromyography and Kinesiology, vol.11, issue.1, pp.1-9, 2001.
DOI : 10.1016/S1050-6411(00)00039-0

W. S. Marras and C. M. Sommerich, A three-dimensional motion model of loads on the lumbar spine: I. Model structure, Human Factors, vol.33, issue.2, pp.123-137, 1991.

R. Merletti and P. J. Parker, Electromyography: Physiology, engineering, and non-invasive applications, 2004.

J. P. Mogk and P. J. Keir, Crosstalk in surface electromyography of the proximal forearm during gripping tasks, Journal of Electromyography and Kinesiology, vol.13, issue.1, pp.63-71, 2003.
DOI : 10.1016/S1050-6411(02)00071-8

C. A. Oatis, Kinesiology: The mechanics and pathomechanics of human movement, 2008.

M. A. Oskoei and H. Hu, Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb, IEEE Transactions on Biomedical Engineering, vol.55, issue.8, pp.55-1956, 2008.
DOI : 10.1109/TBME.2008.919734

A. H. Oskouei, M. G. Paulin, and A. B. Carman, Intra-session and inter-day reliability of forearm surface EMG during varying hand grip forces, Journal of Electromyography and Kinesiology, vol.23, issue.1, pp.216-222, 2013.
DOI : 10.1016/j.jelekin.2012.08.011

S. H. Park and S. P. Lee, EMG pattern recognition based on artificial intelligence techniques, IEEE Transactions on Rehabilitation Engineering, vol.6, issue.4, pp.400-405, 1998.
DOI : 10.1109/86.736154

C. K. Peng, S. Havlin, H. E. Stanley, and A. L. Goldberger, Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol.5, issue.1, pp.82-87, 1995.
DOI : 10.1063/1.166141

L. Philipson and P. G. Larsson, The electromyographic signal as a measure of muscular force: A comparison of detection and quantification techniques, Electromyography and Clinical Neurophysiology, vol.28, issue.2-3, pp.141-150, 1988.

A. Phinyomark, C. Limsakul, and P. Phukpattaranont, EMG feature extraction for tolerance of white Gaussian noise, Proceedings of the International Workshop and Symposium on Science and Technology, pp.178-183, 2008.

A. Phinyomark, C. Limsakul, and P. Phukpattaranont, EMG feature extraction for tolerance of 50 Hz interference, Proceedings of the 4th International Conference on Engineering Technologies, pp.289-293, 2009.

A. Phinyomark, S. Hirunviriya, P. Phukpattaranont, and C. Limsakul, Evaluation of EMG Feature Extraction for Hand Movement Recognition Based on Euclidean Distance and Standard Deviation, Proceedings of the 7th International Conference on Electrical Engineering/Electronics, pp.856-860, 2010.

A. Phinyomark, S. Hirunviriya, A. Nuidod, P. Phukpattaranont, and C. Limsakul, Evaluation of EMG Feature Extraction for Movement Control of Upper Limb Prostheses Based on Class Separation Index, Proceedings of the 5th Kuala Lumpur International Conference on Biomedical Engineering, Kuala Lumpur, pp.750-754, 2011.
DOI : 10.1007/978-3-642-21729-6_183

A. Phinyomark, M. Phothisonothai, P. Phukpattaranont, and C. Limsakul, Critical exponent analysis applied to surface electromyography (EMG) signals for gesture recognition, Metrology and Measurement Systems, vol.18, issue.4, pp.645-658, 2011.

A. Phinyomark, P. Phukpattaranont, and C. Limsakul, Wavelet-based denoising algorithm for robust EMG pattern recognition. Fluctuation and Noise Letters, pp.157-167, 2011.

A. Phinyomark, P. Phukpattaranont, C. Limsakul, and M. Phothisonothai, Electromyography (EMG) signal classification based on detrended fluctuation analysis. Fluctuation and Noise Letters, pp.281-301, 2011.

A. Phinyomark, G. Chujit, P. Phukpattaranont, C. Limsakul, and H. Hu, A preliminary study assessing time-domain EMG features of classifying exercises in preventing falls in the elderly, 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp.1-4, 2012.
DOI : 10.1109/ECTICon.2012.6254117

A. Phinyomark, A. Nuidod, P. Phukpattaranont, and C. Limsakul, Feature Extraction and Reduction of Wavelet Transform Coefficients for EMG Pattern Classification, Electronics and Electrical Engineering, vol.122, issue.6, pp.27-32, 2012.
DOI : 10.5755/j01.eee.122.6.1816

A. Phinyomark, P. Phukpattaranont, and C. Limsakul, Feature reduction and selection for EMG signal classification, Expert Systems with Applications, vol.39, issue.8, pp.39-7420, 2012.
DOI : 10.1016/j.eswa.2012.01.102

A. Phinyomark, P. Phukpattaranont, and C. Limsakul, Fractal analysis features for weak and single-channel upper-limb EMG signals, Expert Systems with Applications, vol.39, issue.12, pp.39-11156, 2012.
DOI : 10.1016/j.eswa.2012.03.039

A. Phinyomark, P. Phukpattaranont, and C. Limsakul, Investigating long-term effects of feature extraction methods for continuous EMG pattern classification. Fluctuation and Noise Letters, p.1250028, 2012.

A. Phinyomark, P. Phukpattaranont, and C. Limsakul, The Usefulness of Wavelet Transform to Reduce Noise in the SEMG Signal, EMG methods for evaluating muscle and nerve function, pp.107-132, 2012.
DOI : 10.5772/25757

A. Phinyomark, S. Thongpanja, H. Hu, P. Phukpattaranont, and C. Limsakul, The usefulness of mean and median frequencies in electromyography analysis Computational intelligence in electromyography analysis -A p e r s p e c t i v e, pp.195-220, 2012.

A. Phinyomark, F. Quaine, S. Charbonnier, C. Serviere, F. Tarpin-bernard et al., A feasibility study on the use of anthropometric variables to make muscle???computer interface more practical, Engineering Applications of Artificial Intelligence, vol.26, issue.7, pp.1681-1688, 2013.
DOI : 10.1016/j.engappai.2013.01.004

URL : https://hal.archives-ouvertes.fr/hal-00831659

A. Phinyomark, F. Quaine, S. Charbonnier, C. Serviere, F. Tarpin-bernard et al., EMG feature evaluation for improving myoelectric pattern recognition robustness, Expert Systems with Applications, vol.40, issue.12, pp.40-4832, 2013.
DOI : 10.1016/j.eswa.2013.02.023

URL : https://hal.archives-ouvertes.fr/hal-00831643

M. Phothisonothai and M. Nakagawa, Fractal-Based EEG Data Analysis of Body Parts Movement Imagery Tasks, The Journal of Physiological Sciences, vol.57, issue.4, pp.217-226, 2007.
DOI : 10.2170/physiolsci.RP006307

M. B. Reaz, M. S. Hussain, and F. Mohd-yasin, Techniques of EMG signal analysis: detection, processing, classification and applications, Biological Procedures Online, vol.13, issue.2, pp.11-35, 2006.
DOI : 10.1251/bpo115

K. W. Rhee, K. J. You, and H. C. Shin, Recognition of finger motion with sEMG and gyrosensor signals, Journal of Measurement Science and Instrumentation, vol.2, issue.2, pp.136-139, 2011.

J. S. Richman and J. R. Moorman, Physiological time series analysis using approximate entropy and sample entropy, American Journal of Physiology Heart and Circulatory Physiology, vol.278, issue.6, pp.2039-2049, 2000.

T. S. Saponas, D. S. Tan, D. Morris, and R. Balakrishnan, Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces, Proceeding of the twenty-sixth annual CHI conference on Human factors in computing systems , CHI '08, pp.515-524, 2008.
DOI : 10.1145/1357054.1357138

T. S. Saponas, D. S. Tan, D. Morris, R. Balakrishnan, J. Turner et al., Enabling always-available input with muscle-computer interfaces, Proceedings of the 22nd annual ACM symposium on User interface software and technology, UIST '09, pp.167-176, 2009.
DOI : 10.1145/1622176.1622208

T. S. Saponas, D. S. Tan, D. Morris, J. Turner, and J. A. Landay, Making muscle-computer interfaces more practical, Proceedings of the 28th international conference on Human factors in computing systems, CHI '10, pp.851-854, 2010.
DOI : 10.1145/1753326.1753451

J. W. Sensinger, B. A. Lock, and T. A. Kuiken, Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.17, issue.3, pp.270-278, 2009.
DOI : 10.1109/TNSRE.2009.2023282

P. Shenoy, K. J. Miller, B. Crawford, and R. P. Rao, Online Electromyographic Control of a Robotic Prosthesis, IEEE Transactions on Biomedical Engineering, vol.55, issue.3, pp.1128-1135, 2008.
DOI : 10.1109/TBME.2007.909536

L. Y. Shyu, J. Y. Chen, R. W. Tatn, and W. Hu, A new electrode system for hand action discrimination, Journal of Medical and Biological Engineering, vol.22, issue.4, pp.211-217, 2002.

R. J. Smith, D. Huberdeau, F. Tenore, and N. V. Thakor, Real-time myoelectric decoding of individual finger movements for a virtual target task, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.2376-2379, 2009.
DOI : 10.1109/IEMBS.2009.5334981

R. J. Smith, F. Tenore, D. Huberdeau, R. Etienne-cummings, and N. V. Thakor, Continuous decoding of finger position from surface EMG signals for the control of powered prostheses, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.197-200, 2008.
DOI : 10.1109/IEMBS.2008.4649124

A. Subasi, Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines, Computers in Biology and Medicine, vol.42, issue.8, pp.42-806, 2012.
DOI : 10.1016/j.compbiomed.2012.06.004

A. Subasi, Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders, Computers in Biology and Medicine, vol.43, issue.5, pp.576-586, 2013.
DOI : 10.1016/j.compbiomed.2013.01.020

X. Tang, Y. Liu, C. Lv, and D. Sun, Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor, Sensors, vol.12, issue.12, pp.1130-1147, 2012.
DOI : 10.3390/s120201130

R. Taylor, Interpretation of the Correlation Coefficient: A Basic Review, Journal of Diagnostic Medical Sonography, vol.73, issue.1, pp.35-39, 1990.
DOI : 10.1177/875647939000600106

F. V. Tenore, A. Ramos, A. Fahmy, S. Acharya, R. Etienne-cummings et al., Towards the Control of Individual Fingers of a Prosthetic Hand Using Surface EMG Signals, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.6145-6148, 2007.
DOI : 10.1109/IEMBS.2007.4353752

F. V. Tenore, A. Ramos, A. Fahmy, S. Acharya, R. Etienne-cummings et al., Decoding of individual finger movements using surface electromyography, IEEE Transactions on Biomedical Engineering, issue.5, pp.56-1427, 2009.

S. Thongpanja, A. Phinyomark, P. Phukpattaranont, and C. Limsakul, Mean and Median Frequency of EMG Signal to Determine Muscle Force based on Time-Dependent Power Spectrum, Electronics and Electrical Engineering, vol.19, issue.3, pp.51-56, 2013.
DOI : 10.5755/j01.eee.19.3.3697

D. Tkach, H. Huang, and T. A. Kuiken, Study of stability of time-domain features for electromyographic pattern recognition, Journal of NeuroEngineering and Rehabilitation, vol.7, issue.1, 2010.
DOI : 10.1186/1743-0003-7-21

F. J. Vera-garcia, J. M. Moreside, and S. M. Mcgill, MVC techniques to normalize trunk muscle EMG in healthy women, Journal of Electromyography and Kinesiology, vol.20, issue.1, pp.10-16, 2010.
DOI : 10.1016/j.jelekin.2009.03.010

B. Vigreux, J. C. Cnockaert, and E. Pertuzon, Factors influencing quantified surface EMGs, European Journal of Applied Physiology and Occupational Physiology, vol.51, issue.Suppl. 280, pp.119-129, 1979.
DOI : 10.1007/BF00421659

D. A. Winter, Biomechanics and Motor Control of Human Movement, 1990.
DOI : 10.1002/9780470549148

L. Wei, H. Hu, and Y. Zhang, FUSING EMG AND VISUAL DATA FOR HANDS-FREE CONTROL OF AN INTELLIGENT WHEELCHAIR, International Journal of Humanoid Robotics, vol.08, issue.04, pp.707-724, 2011.
DOI : 10.1142/S0219843611002629

J. J. Woods and B. Bigland-ritchie, Linear and non-linear surface EMG/force relationships in human muscles An anatomical/functional argument for the existence of both, American Journal of Physical Medicine, issue.6, pp.62-287, 1983.

S. W. Yang, C. S. Lin, S. K. Lin, and C. H. Lee, Design of virtual keyboard using blink control method for the severely disabled, Computer Methods and Programs in Biomedicine, vol.111, issue.2
DOI : 10.1016/j.cmpb.2013.04.012

K. J. You, K. W. Rhee, and H. C. Shin, Finger flexion motion inference from sEMG signals, Journal of Measurement Science and Instrumentation, vol.2, issue.2, pp.140-143, 2011.

K. J. You, K. W. Rhee, and H. C. Shin, Finger Motion Decoding Using EMG Signals Corresponding Various Arm Postures, Experimental Neurobiology, vol.19, issue.1, pp.54-61, 2010.
DOI : 10.5607/en.2010.19.1.54

URL : http://doi.org/10.5607/en.2010.19.1.54

A. J. Young, L. J. Hargrove, and T. A. Kuiken, The Effects of Electrode Size and Orientation on the Sensitivity of Myoelectric Pattern Recognition Systems to Electrode Shift, IEEE Transactions on Biomedical Engineering, vol.58, issue.9, pp.58-2537, 2011.
DOI : 10.1109/TBME.2011.2159216

A. J. Young, L. J. Hargrove, and T. A. Kuiken, Improving Myoelectric Pattern Recognition Robustness to Electrode Shift by Changing Interelectrode Distance and Electrode Configuration, IEEE Transactions on Biomedical Engineering, vol.59, issue.3, pp.59-645, 2012.
DOI : 10.1109/TBME.2011.2177662

S. Yu, E. Jeong, K. Hong, and S. Lee, Classification of nine directions using the maximum likelihood estimation based on electromyogram of both forearms, Biomedical Engineering Letters, vol.36, issue.4, pp.129-137, 2012.
DOI : 10.1007/s13534-012-0063-x

M. Zardoshti-kermani, B. C. Wheeler, K. Badie, and R. M. Hashemi, EMG feature evaluation for movement control of upper extremity prostheses, IEEE Transactions on Rehabilitation Engineering, vol.3, issue.4, pp.324-333, 1995.
DOI : 10.1109/86.481972

X. Zhang, X. Chen, Z. Y. Zhao, Q. Li, J. H. Yang et al., An Adaptive Feature Extractor for Gesture SEMG Recognition, Proceedings of the 1st International Conference on Medical Biometrics, pp.83-90, 2008.
DOI : 10.1109/83.817604

X. Zhang and P. Zhou, Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes, Journal of Electromyography and Kinesiology, vol.22, issue.6, pp.901-907, 2012.
DOI : 10.1016/j.jelekin.2012.06.005

J. Zhao, L. Jiang, H. Cai, H. Liu, and G. Hirzinger, A Novel EMG Motion Pattern Classifier Based on Wavelet Transform and Nonlinearity Analysis Method, 2006 IEEE International Conference on Robotics and Biomimetics, pp.1494-1499, 2006.
DOI : 10.1109/ROBIO.2006.340150

J. Zhao, Z. Xie, L. Jiang, H. Cai, H. Liu et al., EMG Control for a Five-fingered Underactuated Prosthetic Hand Based on Wavelet Transform and Sample Entropy, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.3215-3220, 2006.
DOI : 10.1109/IROS.2006.282425

M. R. Ahsan, M. I. Ibrahimy, and O. O. Khalifa, EMG signal classification for human computer interaction: A review, European Journal of Scientific Research, vol.33, issue.3, pp.480-501, 2009.

E. Franti, L. Milea, V. Butu, S. Cismas, M. Lungu et al., Methods of acquisition and signal processing for myoelectric control of artificial arms, Romanian Journal of Information Science and Technology, vol.15, issue.2, pp.91-105, 2012.

S. Herle and S. Man, Processing Surface Electromyographical Signals for Myoelectric Control, Rehabilitation engineering, pp.223-244, 2009.
DOI : 10.5772/7383

G. Li, Electromyography pattern-recognition-based control of powered multifunctional upperlimb prostheses, Advances in applied electromyography, pp.99-116, 2011.

S. Micera, J. Carpaneto, and S. Raspopovic, Control of Hand Prostheses Using Peripheral Information, IEEE Reviews in Biomedical Engineering, vol.3, pp.48-68, 2010.
DOI : 10.1109/RBME.2010.2085429

M. A. Oskoei and H. Hu, Myoelectric control systems???A survey, Biomedical Signal Processing and Control, vol.2, issue.4, pp.275-294, 2007.
DOI : 10.1016/j.bspc.2007.07.009

B. Peerdeman, D. Boere, H. Witteveen, R. H. Veld, H. Hermens et al., Myoelectric forearm prostheses: State of the art from a user-centered perspective, The Journal of Rehabilitation Research and Development, vol.48, issue.6, pp.48-719, 2011.
DOI : 10.1682/JRRD.2010.08.0161

A. Phinyomark, P. Phukpattaranont, and C. Limsakul, A review of control methods for electric power wheelchairs based on electromyography (EMG) signals with special emphasis on pattern recognition, IETE Technical Review, vol.25, issue.4, pp.316-326, 2011.

M. Zecca, S. Micera, M. C. Carrozza, and P. Dario, Control of multifunctional prosthetic hands by processing the electromyograpic signal, Critical Reviews in Biomedical Engineering, vol.30, pp.4-6, 2002.