SVM based simultaneous hand movements classification using sEMG signals

Prediction of motion volitions is a practical issue in control of artificial limbs. Four classifiers are investigated in this paper to discriminate simultaneous hand movements based on pattern recognition of surface electromyographic (sEMG) signals. A sEMG signal processing tube composed of feature extraction, feature reduction and movements classification is proposed for offline myoelectric pattern recognition. Previous research was mainly devoted to individual hand movements classification. In this paper, several common tools are used for definition of movements. The results show that Support Vector Machine (SVM) outperforms the other three classifiers on both accuracy and model-training time. The user-depend classification accuracy reaches as high as 92.25% while the accuracy of user-independent is about 80%. The proposed classification method is a promising candidate to be used in prosthetic control for a rehabilitation robot in the future.

[1]  Mikhail Kuznetsov,et al.  Filtering the surface EMG signal: Movement artifact and baseline noise contamination. , 2010, Journal of biomechanics.

[2]  Fei Wang,et al.  A comparative study on sign recognition using sEMG and inertial sensors , 2016, 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER).

[3]  Maren Bennewitz,et al.  Real-time imitation of human whole-body motions by humanoids , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Dana Kulic,et al.  Hand gesture recognition based on surface electromyography , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Ling Liu,et al.  Development of an EMG-ACC-Based Upper Limb Rehabilitation Training System , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  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.

[7]  Asha,et al.  A Hand Gesture Recognition Framework and Wearable Gesture Based Interaction Prototype for Mobile Devices , 2015 .

[8]  Luca Benini,et al.  Hyperdimensional biosignal processing: A case study for EMG-based hand gesture recognition , 2016, 2016 IEEE International Conference on Rebooting Computing (ICRC).

[9]  Roozbeh Jafari,et al.  Real-time American Sign Language Recognition using wrist-worn motion and surface EMG sensors , 2015, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[10]  Bo Norrving,et al.  The global burden of stroke and need for a continuum of care , 2013, Neurology.

[11]  Jin-Hee Lee,et al.  A Survey of Approaches for Recognizing Hand Gestures Using EMG Signal , 2016, 2016 IEEE 22nd International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA).

[12]  Weidong Geng,et al.  Gesture recognition by instantaneous surface EMG images , 2016, Scientific Reports.

[13]  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.

[14]  Dario Farina,et al.  The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  Mamun Bin Ibne Reaz,et al.  Surface Electromyography Signal Processing and Classification Techniques , 2013, Sensors.

[16]  Adrian D. C. Chan,et al.  Myoelectric Control Development Toolbox , 2007 .

[17]  Pornchai Phukpattaranont,et al.  Feature reduction and selection for EMG signal classification , 2012, Expert Syst. Appl..