Gesture Recognition Based on Multiscale Singular Value Entropy and Deep Belief Network

As an important research direction of human–computer interaction technology, gesture recognition is the key to realizing sign language translation. To improve the accuracy of gesture recognition, a new gesture recognition method based on four channel surface electromyography (sEMG) signals is proposed. First, the S-transform is applied to four channel sEMG signals to enhance the time-frequency detail characteristics of the signals. Then, multiscale singular value decomposition is applied to the multiple time-frequency matrix output of S-transform to obtain the time-frequency joint features with better robustness. The corresponding singular value permutation entropy is calculated as the eigenvalue to effectively reduce the dimension of multiple eigenvectors. The gesture features are used as input into the deep belief network for classification, and nine kinds of gestures are recognized with an average accuracy of 93.33%. Experimental results show that the multiscale singular value permutation entropy feature is especially suitable for the pattern classification of the deep belief network.

[1]  Zhiwei Wang,et al.  Analysis of Weak Fault in Hydraulic System Based on Multi-scale Permutation Entropy of Fault-Sensitive Intrinsic Mode Function and Deep Belief Network , 2019, Entropy.

[2]  Shu Wang,et al.  Deep Belief Networks Based Toponym Recognition for Chinese Text , 2018, ISPRS Int. J. Geo Inf..

[3]  Mahmoud Tavakoli,et al.  Single channel surface EMG control of advanced prosthetic hands: A simple, low cost and efficient approach , 2017, Expert Syst. Appl..

[4]  Lin Chen,et al.  Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals , 2020, Sensors.

[5]  Xinjun Sheng,et al.  Towards Chinese sign language recognition using surface electromyography and accelerometers , 2017, 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP).

[6]  Wahyu Caesarendra,et al.  Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements , 2020, Symmetry.

[7]  이상헌,et al.  Deep Belief Networks , 2010, Encyclopedia of Machine Learning.

[8]  Anke Xue,et al.  Estimation and Correlation Analysis of Lower Limb Joint Angles Based on Surface Electromyography , 2020, Electronics.

[9]  Li Wen-guo.  Removal of Pulse Artifacts from Surface EMG Signals , 2008 .

[10]  Tieniu Tan,et al.  Do singular values contain adequate information for face recognition? , 2003, Pattern Recognit..

[11]  Guowei Cai,et al.  Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm , 2016 .

[12]  L M Hively,et al.  Detecting dynamical changes in time series using the permutation entropy. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Fouad R. Zaro,et al.  Power Quality Detection and Classification Using S-Transform and Rule-Based Decision Tree , 2019, International Journal of Electrical and Electronic Engineering & Telecommunications.

[14]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[15]  Syed Omer Gilani,et al.  Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG , 2020, Sensors.

[16]  Wo Songlin.  Recognition of Prosthesis EMG Signals Based on EMG Frequency Characteristics , 2011 .

[17]  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.

[18]  Xiang Cao,et al.  Chinese Sign Language Recognition Based on an Optimized Tree-Structure Framework , 2017, IEEE Journal of Biomedical and Health Informatics.

[19]  Kongqiao Wang,et al.  A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[20]  Li Ming-a.  An Adaptive Multi-Domain Fusion Feature Extraction with Method HHT and CSSD , 2013 .

[21]  Mingyuan Yang,et al.  Learning Document Semantic Representation with Hybrid Deep Belief Network , 2015, Comput. Intell. Neurosci..

[22]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.