A Review of Electromyography Signal Analysis Techniques for Musculoskeletal Disorders

Social Security Organisation (SOCSO) Malaysia has reported that the incidence of work related to musculoskeletal disorders (MSDs) has been growing planetary in the manufacturing industry. MSDs are the result of repetitive, forceful or awkward movements on our body and or body parts of bones, joints, ligaments and other soft tissues. Workplace pains and strains can be serious and disabling for workers, causing pain and suffering ranging from discomfort to severe disability. To overcome this problem, Electromyography is proper to use in Health Screening Program (HSP) it to monitor darn diagnose the muscle’s performance for their patient and know the exact localization of muscle pain. The previous researchers has been explore of several in EMG analysis techniques and features proposed in time, frequency and time-frequency domain analysis. This review of common EMG signal processing techniques is proposed by assembling from simple to complex analysis techniques to give the overview information for the other researcher. This is because; the suitable selection of a method and its features settings will ensure readability of the time-frequency representations and reliability of results. The strongest correspond with time-frequency characteristic and resolution also reducing cross term for bilinear will consider it as the optimal method.

[1]  Sanjib K. Das,et al.  EFFECT OF ALTERED BODY COMPOSITION ON MUSCULOSKELETAL DISORDERS IN MEDICAL PRACTITIONERS , 2016 .

[2]  Toshio Fukuda,et al.  An exoskeletal robot for human elbow motion support-sensor fusion, adaptation, and control , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[3]  Tirthankar Ghosh,et al.  Assessment of Postural effect on Work Related Musculoskeletal Disorders and Back Muscle Fatigue among the Goldsmiths of India , 2017 .

[4]  Pu Liu,et al.  Identification of Constant-Posture EMG–Torque Relationship About the Elbow Using Nonlinear Dynamic Models , 2012, IEEE Transactions on Biomedical Engineering.

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

[6]  J. Kilby,et al.  Analysis of Surface Electromyography Signals Using Discrete Fourier Transform Sliding Window Technique , 2013 .

[7]  Zhizhong Wang,et al.  Classification of surface EMG signals using harmonic wavelet packet transform , 2006, Physiological measurement.

[8]  Jacek M. Zurada,et al.  Predicting the Risk of Low Back Disorders due to Manual Handling Tasks , 2012, 2012 45th Hawaii International Conference on System Sciences.

[9]  Abdul Rahim Abdullah,et al.  A New Two Points Method for Identify Dominant Harmonic Disturbance Using Frequency and Phase Spectrogram , 2014 .

[10]  Tole Sutikno,et al.  An Accurate Classification Method of Harmonic Signals in Power Distribution System by Utilising S-Transform , 2017 .

[11]  Richard G. Absher,et al.  A time-frequency approach to evaluate electromyographic recordings , 1992, [1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems.

[12]  C. D. De Luca Use of the surface EMG signal for performance evaluation of back muscles , 1993, Muscle & nerve.

[13]  Puspa Inayat Khalid,et al.  The use of surface electromyography in muscle fatigue assessments–a review , 2015 .

[14]  A. Aman,et al.  Leakage current analysis on polymeric surface condition using time-frequency distribution , 2012, 2012 IEEE International Power Engineering and Optimization Conference Melaka, Malaysia.

[15]  Djamel Chikouche,et al.  Effect of the window length on the EMG spectral estimation through the Blackman-Tukey method , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

[16]  Hang Tuah Jaya,et al.  A Study on Push-Pull Analysis Associated with Awkward Posture among Workers in Aerospace Industry , 2014 .

[17]  R. Merletti,et al.  Surface EMG signal processing during isometric contractions. , 1997, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[18]  Pradyut Kumar Biswal,et al.  Robust ECG artifact removal from EEG using continuous wavelet transformation and linear regression , 2016, 2016 International Conference on Signal Processing and Communications (SPCOM).

[19]  Paulo Rogério Scalassara,et al.  Fourier and Wavelet Spectral Analysis of EMG Signals in 1-km Cycling Time-Trial , 2014 .

[20]  Fabrice Plante,et al.  Improvement of speech spectrogram accuracy by the method of reassignment , 1998, IEEE Trans. Speech Audio Process..

[21]  Farzana Khanam,et al.  Frequency based EMG power spectrum analysis of Salat associated muscle contraction , 2015, 2015 International Conference on Electrical & Electronic Engineering (ICEEE).

[22]  W E Garrett,et al.  A comparison of knee joint motion patterns between men and women in selected athletic tasks. , 2001, Clinical biomechanics.

[23]  Abdul Rahim Bin Abdullah,et al.  Classification of power quality signals using smooth-windowed Wigner-Ville distribution , 2010, 2010 International Conference on Electrical Machines and Systems.

[24]  Gavriel Salvendy,et al.  Handbook of Human Factors and Ergonomics , 2005 .

[25]  S. Dawal,et al.  Muscles activities at two different work area boundaries during sedentary work , 2010, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[26]  Shalini Dhingra,et al.  An Explanatory Study of the Parameters to Be Measured From EMG Signal , 2013 .

[27]  AMANDA NEBEL,et al.  Signal Processing for Electromyography Parameter Estimation , 2013 .

[28]  Abdul Rahim Abdullah,et al.  Performance Evaluation of Real Power Quality Disturbances Analysis Using S-Transform , 2015 .

[29]  Peter Buckle,et al.  Ergonomics and musculoskeletal disorders: overview. , 2005, Occupational medicine.

[30]  Å. Kilbom,et al.  One-handed load carrying — Cardiovascular, muscular and subjective indices of endurance and fatigue , 2005, European Journal of Applied Physiology and Occupational Physiology.

[31]  Lele Yuan A TIME-FREQUENCY FEATURE FUSION ALGORITHM BASED ON NEURAL NETWORK FOR HRRP , 2017 .

[32]  Susan Armijo-Olivo,et al.  Predictive value of the DASH tool for predicting return to work of injured workers with musculoskeletal disorders of the upper extremity , 2016, Occupational and Environmental Medicine.

[33]  Vladimir G. Dimitrov,et al.  Interpretation of EMG integral or RMS and estimates of "neuromuscular efficiency" can be misleading in fatiguing contraction. , 2010, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[34]  Abdul Rahim Abdullah,et al.  Performance Verification of Power Quality Signals Classification System , 2015 .

[35]  Mehmet Rahmi Canal,et al.  Comparison of Wavelet and Short Time Fourier Transform Methods in the Analysis of EMG Signals , 2010, Journal of Medical Systems.

[36]  S Cerutti On time-frequency techniques in biomedical signal analysis. , 2013, Methods of information in medicine.

[37]  Daniel R. Rogers,et al.  EMG-based muscle fatigue assessment during dynamic contractions using principal component analysis. , 2011, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[38]  Rajiv Saxena,et al.  Fractional Fourier transform: A novel tool for signal processing , 2013 .

[39]  G. Carpinelli,et al.  Adaptive Prony Method for the Calculation of Power-Quality Indices in the Presence of Nonstationary Disturbance Waveforms , 2009, IEEE Transactions on Power Delivery.

[40]  Abdulhamit Subasi,et al.  Comparison of decision tree algorithms for EMG signal classification using DWT , 2015, Biomed. Signal Process. Control..

[41]  P. A. Karthick,et al.  Surface electromyography based muscle fatigue progression analysis using modified B distribution time-frequency features , 2016, Biomed. Signal Process. Control..

[42]  Nurhazimah Nazmi,et al.  A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions , 2016, Sensors.

[43]  Amal Feltane,et al.  Time-frequency based methods for nonstationary signal analysis with application to EEG signals , 2016 .

[44]  Abdul Rahim Abdullah,et al.  Power Quality Signals Classification System Using Time-Frequency Distribution , 2014 .

[45]  Dennis C. Tkach,et al.  Study of stability of time-domain features for electromyographic pattern recognition , 2010, Journal of NeuroEngineering and Rehabilitation.

[46]  Louise Farnworth,et al.  Reliability and Validation Properties of the Malaysian Language Version of the Occupational Self Assessment Version 2.2 for Injured Workers with Musculoskeletal Disorders , 2011 .

[47]  Ajat Shatru Arora,et al.  Comparison of the techniques used for sgmentation of EMG signals , 2009 .

[48]  Kaiyun Chen,et al.  Time–frequency analysis of nonstationary complex magneto-hydro-dynamics in fusion plasma signals using the Choi–Williams distribution , 2013 .

[49]  Abdul Rahman Omar,et al.  Assessment of Muscle Fatigue Associated with Prolonged Standing in the Workplace , 2012, Safety and health at work.

[50]  Abdul Rahim Abdullah,et al.  Localization of Multiple Harmonic Sources for Inverter Loads Utilizing Periodogram , 2016 .

[51]  Abdul Rahim Abdullah,et al.  An Evaluation of Linear Time Frequency Distribution Analysis for VSI Switch Faults Identification , 2017 .

[52]  Per Aagaard,et al.  Training-induced changes in muscle CSA, muscle strength, EMG, and rate of force development in elderly subjects after long-term unilateral disuse. , 2004, Journal of applied physiology.

[53]  Huosheng Hu,et al.  A preliminary study assessing time-domain EMG features of classifying exercises in preventing falls in the elderly , 2012, 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[54]  Mohamed Roushdy,et al.  Extended Case-Based Behavior Control for Multi-Humanoid Robots , 2016, Int. J. Humanoid Robotics.

[55]  Javier Garcia-Casado,et al.  Time-frequency representations of the sternocleidomastoid muscle electromyographic signal recorded with concentric ring electrodes , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[56]  F. Mohd-Yasin,et al.  Techniques of EMG signal analysis: detection, processing, classification and applications , 2006, Biological Procedures Online.

[57]  Siti Zawiah Md Dawal,et al.  Upper limb and lower back muscle activity during prolonged sitting , 2009, 2009 IEEE Toronto International Conference Science and Technology for Humanity (TIC-STH).

[58]  Ganapati Panda,et al.  An Improved S-Transform for Time-Frequency Analysis , 2009, 2009 IEEE International Advance Computing Conference.

[59]  Ahmad Zuri Sha'ameri,et al.  Real-time power quality disturbances detection and classification system , 2014 .

[60]  Abdul Rahim Abdullah,et al.  A Utilisation of Improved Gabor Transform for Harmonic Signals Detection and Classification Analysis , 2017 .

[61]  Abdul Rahim Abdullah,et al.  Lead Acid Battery Analysis using S-Transform , 2017 .

[62]  Arash Shahravan,et al.  Prevalence of Upper Extremity Musculoskeletal Disorders in Dentists: Symptoms and Risk Factors , 2015, Journal of environmental and public health.

[63]  N. Sathiakumar,et al.  Occupational Disease Among Non-Governmental Employees in Malaysia: 2002-2006 , 2008, International journal of occupational and environmental health.

[64]  Muhsin Tunay Gençoglu,et al.  An expert system based on S-transform and neural network for automatic classification of power quality disturbances , 2009, Expert Syst. Appl..

[65]  H. Ghaem,et al.  The effect of musculoskeletal problems on fatigue and productivity of office personnel: a cross-sectional study , 2017, Journal of preventive medicine and hygiene.

[66]  N. H. Shamsudin,et al.  sEMG signals analysis using time-frequency distribution for symmetric and asymmetric lifting , 2015, 2015 International Symposium on Technology Management and Emerging Technologies (ISTMET).

[67]  Pradipta Kishore Dash,et al.  S-transform-based intelligent system for classification of power quality disturbance signals , 2003, IEEE Trans. Ind. Electron..

[68]  Sirinee Thongpanja,et al.  Mean and Median Frequency of EMG Signal to Determine Muscle Force based on Time- dependent Power Spectrum , 2013 .

[69]  E F Shair,et al.  EMG Processing Based Measures of Fatigue Assessment during Manual Lifting , 2017, BioMed research international.

[70]  A. Jidin,et al.  Short-circuit switches fault analysis of voltage source inverter using spectrogram , 2013, 2013 International Conference on Electrical Machines and Systems (ICEMS).

[71]  Norali Ahmad Nasrul,et al.  Surface Electromyography signal processing and application: a review , 2009 .

[72]  A. R. Abdullah,et al.  Power quality signals detection using S-transform , 2013, 2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO).

[73]  N. H. Shamsudin,et al.  Real-time power quality signals monitoring system , 2013, 2013 IEEE Student Conference on Research and Developement.

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

[75]  Diana Starovoytova,et al.  Hazards and Risks at Rotary Screen Printing (Part 2/6): Analysis of Machine-operators’ Posture via Rapid-Upper-Limb-Assessment (RULA) , 2017 .