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A study on the effect of age, gender and paralysis on sEMG signals

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In this presentation, i have tried to show the difference in the EMG signal output of different age group, gender and have also compared the signal pattern of a normal human being to that of a …

In this presentation, i have tried to show the difference in the EMG signal output of different age group, gender and have also compared the signal pattern of a normal human being to that of a paralytic person. Surface EMG sensors have employed for the testing on volunteers.
References:
[1] H. S. Milner-Brown and R. B. Stein, "The relation between the surface electromyogram and muscular force," Journal of Physiology, vol. 246, pp. 549- 569, 1975.
[2] C. Orizio, "Muscle sound: bases for the introduction of a mechanomyographic signal in muscle studies," Critical reviews in biomedical engineering, vol. 21, pp. 201-43, 1993 1993.
[3] D. B. Sanders, E. V. Stalberg, and S. D. Nandedkar, "Analysis of the electromyographic interference pattern," J Clin Neurophysiol., vol. 13, pp. 385-400, Sep 1996.
[4] W. Yao, A. J. Fuglevand, and R. M. Enoka, "Motor-unit synchronization increases EMG amplitude and decreases force steadiness of simulated contractions," Journal of Neurophysiology, vol. 83, pp. 441-452, 2000.
[5] Clancy, E. A., Bouchard, S., and Rancour, D. (2001): "Estimation and application of EMG amplitude during dynamic contractions", IEEE Eng. Med. BioI. Mag., 20, pp. 47-54.
[6] James C. Huhta, John G. Webster (1973): "60-Hz Interference in Electromyography", IEEE Transactions on Biomedical Engineering, Vol. BME-20, No. 2.
[7] Pinwei Zhu, "Design of Surface Electromyography Detection Circuit", 2010 International Conference on Future Information Technology and Management Engineering.
[8] Analog Devices, Inc. (2004): "Low Cost Low Power Instrumentation Amplifier", datasheet Rev. G, pp. 1-3.

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  • 1. A study on the effect of age, gender and paralysis on sEMG signals
  • 2. What is sEMG? • sEMG: Surface Electromyography Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles.
  • 3. What is electrical activity of muscles? • Muscles are stimulated by signals from nerve cells called motor neurons. This stimulation causes electrical activity in the muscle, which in turn causes contraction. These signals are called myoelectric signals.
  • 4. Myoelectric signal • Frequency Range: 10 Hz to 500 Hz • Amplitude: 10uV to 5000uV What is electrical activity of muscles?
  • 5. Myoelectric signal • Frequency Range: 10 Hz to 500 Hz • Amplitude: 10uV to 5000uV What is electrical activity of muscles? NOISE
  • 6. What is electrical activity of muscles? Power Transmission Lines(Hum) India: 50Hz USA: 60 Hz
  • 7. Difficulties for retrieving the signal. • Low amplitude. • Low SNR. • Highly immune to noise.
  • 8. • High gain amplifier. • High CMRR • Filter: 50hz band stop and 10 hz-500Hz bandpass. Requirement: • Low amplitude. • Low SNR. • Highly immune to noise.
  • 9. Hardware: Instrumentation amplifier(AD620) Gain Range: 1:10,000 CMRR= 120dB G = 49.4kΩ/ Ro+1
  • 10. sEMG sensor(Electromyograph)
  • 11. Electrodes: • Dry electrodes:
  • 12. Positioning of electrode: •Electrode 1 and electrode 2 across the target muscle i.e. Flexor carpi radialis. •Electrode 3 to the body ground. Electrodes:
  • 13. Positioning of electrode: •Electrode 1 and electrode 2 across the target muscle i.e. Flexor carpi radialis. •Electrode 3 to the body ground. Electrodes:
  • 14. Data acquisition:
  • 15. Data acquisition:
  • 16. Processing • Now we have 16-bit data in MATLAB. 16 bit data(Time Domain) DFT Data in frequency Domain Filter implementation
  • 17. Processing • Now we have 16-bit data in MATLAB. 16 bit data(Time Domain) DFT Data in frequency Domain Filter implementation
  • 18. Processing • Now we have 16-bit data in MATLAB. 16 bit data(Time Domain) DFT Data in frequency Domain Filter implementation Data Analysis
  • 19. Volunteers No. of Volunteers Age Group Gender Normal/Paralytic 10 16-25 M Normal 10 16-25 F Normal 5 35-45 M Normal 5 55-65 M Normal 5 55-65 F Normal 1 35-45 F Half-Paralytic • In total 36 volunteers were employed.
  • 20. • Protocol followed: • Sensors were attached to the volunteers’ right arm and they were asked to keep their arm in fully relaxed position. • Then they were asked to keep there arm in fully excited position by tightening their fist to get the maximum firing by the muscle neurons. • Similar task was done with the left arm. Volunteers
  • 21. Data Analysis and interpretation Left hand sEMG data for age group 16-25.
  • 22. Data Analysis and interpretation Right hand sEMG data for age group 16-25.
  • 23. Data Analysis and interpretation Right and Left hand sEMG data for male of age group 55-65. .
  • 24. Data Analysis and interpretation Right and Left hand sEMG data for female of age group 55-65. .
  • 25. Data Analysis and interpretation Comparison between the left hand sEMG data of different age groups at fully excited position.
  • 26. Data Analysis and interpretation Comparison between the sEMG data of the normal hand to the paralytic hand of a half paralytic individual of age group 35-45.
  • 27. Result • Right hand’s sEMG signal in all the age group is more in comparison to that of the left hand. • sEMG data of female is found to be less than in comparison to the male, in all age groups. • sEMG signal’s strength increases from age group (16-25) to age group (35- 45) and then decreases for age group (55-65). • The paralytic hand shows less amplitude sEMG signals in comparison to the normal hand of the same individual
  • 28. Future aspects • This study and data acquired during the experiment can be used as a reference to study the electric activity of Flexor carpi radialis for different movement of hand. • Database of electrical activity of major muscles of human body can be made, which can be used to create the prosthetic limbs for people with paralysis or amputees.
  • 29. • Abhishek Jha (abhishek.jha@ismu.ac.in) Thank you!! Authors: • Mrinal Sen