Identification of low level sEMG signals for individual finger prosthesis

This research reports the identification of motor tasks in a human hand from weak myoelectric signals, aimed to control a prosthesis with individual finger flexion and wrist and grasps movements. The gestures were evaluated in two groups, independently. Four channel sEMG signals were captured on the forearm from able-body and amputees volunteers, taking into account low level contraction. Linear and non-linear parameters were extracted based on time and frequency domain and Detrended Fluctuation Analysis (DFA), to represent EMG patterns. The average classification accuracies were computed using Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) to evaluate the results. Confusion matrix from some experiments show the success rate identifying the gestures.

[1]  Dapeng Yang,et al.  An anthropomorphic robot hand developed based on underactuated mechanism and controlled by EMG signals , 2009 .

[2]  Xiao Hu,et al.  Classification of surface EMG signal with fractal dimension. , 2005, Journal of Zhejiang University. Science. B.

[3]  Bert U Kleine,et al.  Using two-dimensional spatial information in decomposition of surface EMG signals. , 2007, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[4]  Teodiano Freire Bastos Filho,et al.  Pattern recognition of hand movements with low density sEMG for prosthesis control purposes , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

[5]  R. Merletti,et al.  Modeling of surface myoelectric signals--Part I: Model implementation. , 1999, IEEE transactions on bio-medical engineering.

[6]  R.N. Scott,et al.  A new strategy for multifunction myoelectric control , 1993, IEEE Transactions on Biomedical Engineering.

[7]  N.V. Thakor,et al.  Towards the Control of Individual Fingers of a Prosthetic Hand Using Surface EMG Signals , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Stefano Stramigioli,et al.  Myoelectric forearm prostheses: state of the art from a user-centered perspective. , 2011, Journal of rehabilitation research and development.

[9]  Marie-Françoise Lucas,et al.  Signal-dependent wavelets for electromyogram classification , 2006, Medical and Biological Engineering and Computing.

[10]  Damjan Zazula,et al.  Decomposition of surface EMG signals using non-linear LMS optimisation of higher-order cumulants , 2002, Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002).

[11]  Thomas Laurell,et al.  Real-time control of a virtual hand , 2005 .

[12]  C M Light,et al.  Intelligent multifunction myoelectric control of hand prostheses , 2002, Journal of medical engineering & technology.

[13]  Stuart D. Harshbarger,et al.  Revolutionizing Prosthetics : Systems Engineering Challenges and Opportunities , 2011 .

[14]  N. Shoylev,et al.  Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals , 2006, 2006 8th Seminar on Neural Network Applications in Electrical Engineering.

[15]  Jun Yu,et al.  Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study , 2000, IEEE Transactions on Biomedical Engineering.

[16]  D.K. Kumar,et al.  Fractal Based Modelling and Analysis of Electromyography (EMG) To Identify Subtle Actions , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Huosheng Hu,et al.  Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb , 2008, IEEE Transactions on Biomedical Engineering.

[18]  N KhushabaRami,et al.  Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals , 2012 .

[19]  Guido Bugmann,et al.  Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography , 2013, IEEE Journal of Biomedical and Health Informatics.

[20]  Ping Zhou,et al.  A study of surface motor unit action potentials in first dorsal interosseus (FDI) muscle , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  G.F. Inbar,et al.  Classification of finger activation for use in a robotic prosthesis arm , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  M Controzzi,et al.  Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  G. Lundborg,et al.  Refined myoelectric control in below-elbow amputees using artificial neural networks and a data glove. , 2005, The Journal of hand surgery.

[24]  Christos D. Katsis,et al.  A two-stage method for MUAP classification based on EMG decomposition , 2007, Comput. Biol. Medicine.

[25]  Adam Wilson,et al.  An Overview Of The UNB Hand System , 2011 .

[26]  Toshio Tsuji,et al.  Limb-function discrimination using EMG signals by neural network and application to prosthetic forearm control , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[27]  G. Magenes,et al.  Bio-inspired controller for a dexterous prosthetic hand based on principal components analysis , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[28]  Dingguo Zhang,et al.  EMG controlled multifunctional prosthetic hand: Preliminary clinical study and experimental demonstration , 2011, 2011 IEEE International Conference on Robotics and Automation.

[29]  Pornchai Phukpattaranont,et al.  Fractal analysis features for weak and single-channel upper-limb EMG signals , 2012, Expert Syst. Appl..

[30]  S. Krishnan,et al.  Real-Time Classification of Forearm Electromyographic Signals Corresponding to User-Selected Intentional Movements for Multifunction Prosthesis Control , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[31]  Roberto Merletti,et al.  Technology and instrumentation for detection and conditioning of the surface electromyographic signal: state of the art. , 2009, Clinical biomechanics.

[32]  Sridhar P. Arjunan,et al.  Pattern classification of Myo-Electrical signal during different Maximum Voluntary Contractions: A study using BSS techniques , 2010 .

[33]  Blair A. Lock,et al.  Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[34]  Nitish V. Thakor,et al.  Decoding of Individuated Finger Movements Using Surface Electromyography , 2009, IEEE Transactions on Biomedical Engineering.

[35]  Junuk Chu,et al.  A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand , 2006, IEEE Transactions on Biomedical Engineering.

[36]  H. Stanley,et al.  Effect of trends on detrended fluctuation analysis. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[37]  S Poosapadi Arjunan,et al.  Fractal features of surface electromyogram: a new measure for low level muscle activation , 2008 .

[38]  Bin Chen,et al.  Determining EMG embedding and fractal dimensions and its application , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[39]  Vijay P. Singh,et al.  Towards identification of finger flexions using single channel surface electromyography – able bodied and amputee subjects , 2013, Journal of NeuroEngineering and Rehabilitation.

[40]  Gamini Dissanayake,et al.  Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals , 2012, Expert Syst. Appl..

[42]  D. Stegeman,et al.  Volume conduction models for surface EMG; confrontation with measurements. , 1997, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[43]  A. Phinyomark,et al.  ELECTROMYOGRAPHY (EMG) SIGNAL CLASSIFICATION BASED ON DETRENDED FLUCTUATION ANALYSIS , 2011 .

[44]  Kevin B. Englehart,et al.  A robust, real-time control scheme for multifunction myoelectric control , 2003, IEEE Transactions on Biomedical Engineering.

[45]  Serge H. Roy,et al.  Modeling of surface myoelectric signals. II. Model-based signal interpretation , 1999, IEEE Transactions on Biomedical Engineering.

[46]  A. van Oosterom,et al.  Three-Layer Volume Conductor Model and Software Package for Applications in Surface Electromyography , 2002, Annals of Biomedical Engineering.

[47]  Barbara Caputo,et al.  Improving Control of Dexterous Hand Prostheses Using Adaptive Learning , 2013, IEEE Transactions on Robotics.

[48]  José Luis Pons Rovira,et al.  Virtual reality training and EMG control of the MANUS hand prosthesis , 2005, Robotica.

[49]  Dinesh Kant Kumar,et al.  Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors , 2010, Journal of NeuroEngineering and Rehabilitation.