Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.

[1]  Sridhar P. Arjunan,et al.  Computation and Evaluation of Features of Surface Electromyogram to Identify the Force of Muscle Contraction and Muscle Fatigue , 2014, BioMed research international.

[2]  Stephen D. Laycock,et al.  Performance of a 2D-3D Image Registration System using (Lossy) Compressed X-ray CT , 2008 .

[3]  Effects of endurance training on neuromuscular fatigue in healthy active men. Part I: Strength loss and muscle fatigue , 2018, European Journal of Applied Physiology.

[4]  C. J. Luca,et al.  SURFACE ELECTROMYOGRAPHY : DETECTION AND RECORDING , 2022 .

[5]  Mark Fisher,et al.  Performance of 2D/3D medical image registration using compressed volumetric data , 2008 .

[6]  Rubita Sudirman,et al.  ELECTROMYOGRAPHY SIGNAL ON BICEPS MUSCLE IN TIME DOMAIN ANALYSIS , 2014 .

[7]  Alaa F. Sheta,et al.  Development of Temperature-based Weather Forecasting Models Using Neural Networks and Fuzzy Logic , 2014, MUE 2014.

[8]  Daniel Vélez Día,et al.  Biomechanics and Motor Control of Human Movement , 2013 .

[9]  Lin Wang,et al.  Prediction of joint moments using a neural network model of muscle activations from EMG signals , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Khalil Ullah,et al.  Electromyographic (EMG) signal to joint torque processing and effect of various factors on EMG to torque model , 2011 .

[11]  Jawdat Alshaer.  Mobile Object-Tracking Approach using A Combination of Fuzzy Logic and Neural Networks , 2016 .

[12]  Steven W. Smith,et al.  The Scientist and Engineer's Guide to Digital Signal Processing , 1997 .

[13]  Z. H. Bohari,et al.  Predicting EMG Based Elbow Joint Torque Model Using Multiple Input ANN Neurons for Arm Rehabilitation , 2014, 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation.

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

[15]  Ammar Almomani,et al.  Enhancing the Security of Exchanging and Storing DICOM Medical Images on the Cloud , 2018, Int. J. Cloud Appl. Comput..

[16]  Adi Indrayanto,et al.  Bicep brachii's force estimation using MAV method on assistive technology application , 2011, 2011 2nd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering.

[17]  M. Jubeau,et al.  Changes in central and peripheral neuromuscular fatigue indices after concentric versus eccentric contractions of the knee extensors , 2018, European Journal of Applied Physiology.

[18]  Stephen D. Laycock,et al.  Fast reconstructed radiographs from octree-compressed volumetric data , 2013, International Journal of Computer Assisted Radiology and Surgery.

[19]  Jawdat Alshaer,et al.  Security Model for Communication and Exchanging Data in Mobile Cloud Computing , 2015 .

[20]  Stephen D. Laycock,et al.  GPU Accelerated Generation of Digitally Reconstructed Radiographs for 2-D/3-D Image Registration , 2012, IEEE Transactions on Biomedical Engineering.

[21]  S. P. Arjunan,et al.  Measuring Increase in Synchronization to Identify Muscle Endurance Limit , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  Dinesh K Kumar,et al.  Testing of motor unit synchronization model for localized muscle fatigue , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  P. Manimegalai.  HAND GESTURE RECOGNITION BASED ON EMG SIGNALS USING ANN , 2013 .