Prediction of EMG Signal on Missing Channel from Signal Captured from Other Related Channels via Deep Neural Network

Capturing electromyography (EMG) is always time-consuming and tedious work as it has to located muscle group and attach the electrode with proper skin preparation. In order to reduce the capturing channel, we study modeling of coordinated muscles in this paper. Typical movement are repetitively conducted with seven major muscles on one leg with help of recruited participants. A deep neural network (DNN) is proposed and trained with historical data. The resulting EMG on vastus lateral (VL) are then predicted from other muscles including rectus femoris (RF), semitendinosus (ST) and biceps femoris (BF), tibialis anterior (TA), glutaeus maximus (GM) and soleus (SO). The predicted EMG signals on VL are compared with measuring result and shows the high accuracy. The predicted result has compared with other learning-based method to show its effectiveness. This result can be used for less channel EMG capturing with predicting signals from the missing channel which will save experimental time and money investing on the capturing hardware.

[1]  Yanfeng Hou,et al.  Prediction of EMG signals of trunk muscles in manual lifting using a neural network model , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[2]  Hyeon-Min Shim,et al.  Multi-channel electromyography pattern classification using deep belief networks for enhanced user experience , 2015, Journal of Central South University.

[3]  Wei Meng,et al.  Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation , 2015 .

[4]  Hasan Badem,et al.  Deep neural network classifier for hand movement prediction , 2017, 2017 25th Signal Processing and Communications Applications Conference (SIU).

[5]  George K. I. Mann,et al.  Developments in hardware systems of active upper-limb exoskeleton robots: A review , 2016, Robotics Auton. Syst..

[6]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[7]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[8]  Andrés Felipe Ruiz Olaya,et al.  Deep neural network for EMG signal classification of wrist position: Preliminary results , 2017, 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI).

[9]  Jianping Wu,et al.  Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method , 2017 .

[10]  Ta-Te Lin,et al.  A novel STFT-ranking feature of multi-channel EMG for motion pattern recognition , 2015, Expert Syst. Appl..

[11]  Ruifeng Li,et al.  SVM based simultaneous hand movements classification using sEMG signals , 2017, 2017 IEEE International Conference on Mechatronics and Automation (ICMA).

[12]  Feng Zhang,et al.  sEMG-based continuous estimation of joint angles of human legs by using BP neural network , 2012, Neurocomputing.

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

[14]  Xiaodong Zhang,et al.  Surface EMG based continuous estimation of human lower limb joint angles by using deep belief networks , 2018, Biomed. Signal Process. Control..

[15]  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.

[16]  Chee-Meng Chew,et al.  Muscle force estimation with surface EMG during dynamic muscle contractions: A wavelet and ANN based approach , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[17]  K. Nazarpour,et al.  Surface EMG Signal Classification Using a Selective Mix of Higher Order Statistics , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[18]  John J. Soraghan,et al.  Improved pattern recognition classification accuracy for surface myoelectric signals using spectral enhancement , 2015, Biomed. Signal Process. Control..

[19]  Michele Folgheraiter,et al.  Feature extraction and real-time recognition of hand motion intentions from EMGs via artificial neural networks , 2018, 2018 6th International Conference on Brain-Computer Interface (BCI).