Hand Gesture Recognition Based on Deep Learning Method

The classical classification method usually consists preprocessing, windowing, feature extraction and classification. Despite the promising performance have been shown in recent researcher, the greatest disadvantage of those classical methods is that some useful information may be discarded when extracting feature. In this paper, a novel model based on deep learning is proposed to improve the accuracy of EMG-based hand gesture recognition. A parallel architecture with five convolution layers is adopted. The results show a slight improvement when using convolution neural network. Meanwhile, the effect of preprocessing on CNN-based method has also been evaluated.

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

[2]  Barbara Caputo,et al.  Exploiting accelerometers to improve movement classification for prosthetics , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

[3]  Beth Jelfs,et al.  Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network , 2017, Front. Neurosci..

[4]  Kevin B. Englehart,et al.  A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.

[5]  Marie-Françoise Lucas,et al.  Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization , 2008, Biomed. Signal Process. Control..

[6]  Jianguang Niu,et al.  The Influencing Factors, Regional Difference and Temporal Variation of Industrial Technology Innovation: Evidence with the FOA-GRNN Model , 2018 .

[7]  Manfredo Atzori,et al.  Electromyography data for non-invasive naturally-controlled robotic hand prostheses , 2014, Scientific Data.

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

[9]  Harksoo Kim,et al.  Low-Cost Implementation of a Named Entity Recognition System for Voice-Activated Human-Appliance Interfaces in a Smart Home , 2018, Sustainability.

[10]  G. Naik,et al.  Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Yinghong Peng,et al.  EMG‐Based Estimation of Limb Movement Using Deep Learning With Recurrent Convolutional Neural Networks , 2018, Artificial organs.

[12]  Panagiotis K. Artemiadis,et al.  EMG-Based Control of a Robot Arm Using Low-Dimensional Embeddings , 2010, IEEE Transactions on Robotics.

[13]  Seungmin Rho,et al.  Bridging the semantic gap in multimedia emotion/mood recognition for ubiquitous computing environment , 2010, The Journal of Supercomputing.

[14]  Jhing-Fa Wang,et al.  A Novel Video Summarization Based on Mining the Story-Structure and Semantic Relations Among Concept Entities , 2009, IEEE Transactions on Multimedia.

[15]  Panagiotis K. Artemiadis,et al.  Learning human reach-to-grasp strategies: Towards EMG-based control of robotic arm-hand systems , 2012, 2012 IEEE International Conference on Robotics and Automation.

[16]  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.

[17]  Manfredo Atzori,et al.  Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-Based Hand Movement Classification , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  Yu Hu,et al.  Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation , 2017, Sensors.

[19]  Hong Liu,et al.  Robust EMG pattern recognition in the presence of confounding factors: features, classifiers and adaptive learning , 2018, Expert Syst. Appl..

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

[21]  P. Geethanjali,et al.  Actuation of prosthetic drive using EMG signal , 2009, TENCON 2009 - 2009 IEEE Region 10 Conference.

[22]  Marimuthu Palaniswami,et al.  Subtle Hand Gesture Identification for HCI Using Temporal Decorrelation Source Separation BSS of Surface EMG , 2007, 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007).

[23]  Shyamanta M. Hazarika,et al.  EMG Feature Set Selection Through Linear Relationship for Grasp Recognition , 2016 .

[24]  Jing Yang,et al.  A Novel Method for Disease Prediction: Hybrid of Random Forest and Multivariate Adaptive Regression Splines , 2013, J. Comput..

[25]  Bruce C. Wheeler,et al.  EMG feature evaluation for movement control of upper extremity prostheses , 1995 .

[26]  Manfredo Atzori,et al.  Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands , 2016, Front. Neurorobot..