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Biofeedback System for Improved Athletic Training

Biofeedback System for Improved Athletic Training

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Biofeedback System for Improved Athletic Training

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  1. Biofeedback System for Improved Athletic Training ECE-498 Matt Statton Advisor: Professor Hanson

  2. Introduction • Goals of athletic training • Muscle hypertrophy through stimulation • Muscle fatigue during a specified repetition range • Maximum motor unit recruitment inducing muscle hypertrophy • Benefits of effective training • Increased results • Injury prevention • Many people do not know how to properly fatigue their muscles • Personal trainers

  3. Biofeedback • How it works • Raises awareness of unconscious physiological activities • Control • Adjustment • Uses • Anxiety and stress • Hypertension • ADHD

  4. Project Proposal • Electrical signals produced by muscles can be used as an indicator of muscle fatigue • Giving users the ability to recognize their level of muscle fatigue will lead to improved athletic training • Maximizing muscle fatigue • Decreasing injury

  5. Design Requirements • Measure electrical signals from muscles • Analyze signal to determine level of muscle fatigue • Determine threshold at which muscle fatigue occurs • Provide feedback response to user

  6. Design Requirements • Measure electrical signals from muscles • Electromyography • Intramuscular vs. surface electromyography • Cost effectiveness • Measurement accuracy

  7. Electromyography Figure 1: Electromyogram from http://www.dataq.com/images/article_images/emg1.jpg

  8. Design Requirements • Measure electrical signals from muscles • Electromyography • Analyze signal to determine level of muscle fatigue • Analog-to-digital conversion • Measure absolute and relative maximum amplitudes of signal • Determine threshold at which muscle fatigue occurs • Provide feedback response to user

  9. Electromyograph • Important components • MAX666CPA Voltage Regulator • LT1494 operational amplifier, A = 1 • Provides virtual ground at Vcc/2 • INA106 differential amplifier, A = 10 • High common-mode rejection ratio Figure 2: Electromyograph circuit based on circuit diagram from http://instruct1.cit.cornell.edu/courses/ee476/FinalProjects/s2005/bsm24_ajg47/website/website/index.htm

  10. Circuit Creation • LPKF ProtoMat C20S Circuit Board Plotter Figure 3: LPKF Circuit Board Plotter from http://www.lpkf.com/_images/757-lpkf-protomat-h100.jpg

  11. EagleCAD Figure 4: EagleCAD schematic of electromyograph circuit Figure 5: EagleCAD board file created from schematic

  12. CircuitCam / BoardMaster Figure 6: CircuitCam circuit board diagram Figure 7: BoardMaster circuit board diagram

  13. Printed Circuit Figure 8: Front of printed circuit board Figure 9: Back of printed circuit board

  14. Design Requirements • Analyze signal to determine level of muscle fatigue • Determine threshold at which muscle fatigue occurs • Provide feedback response to user

  15. Electromyograph Signal Analysis • Silicon Labs C8051F020 microcontroller • On-board analog-to-digital converter • ADC0 = 12-bit • ADC1 = 8-bit • Programmable in C

  16. Electromyograph Signal Analysis Figure 10: Flow chart of electromyograph signal analysis program

  17. Electromyography Figure 11: Contraction and relaxation of muscles of the upper arm from http://www.zoodu.com/uploads/images/2006-08-10/vlt9QAl2A5.jpg

  18. Results Figure 13: Electromyogram of fully contracted and relaxed biceps muscle (Range = 250 mV) Figure 12: Electromyogram of relaxed biceps muscle (Range = 30 mV)

  19. Results Figure 14: Electromyogram of slightly contracted and relaxed biceps muscle (Range = 130 mV) Figure 15: Electromyogram of fully contracted, slightly contracted, and relaxed biceps muscle

  20. Continuing Work • Signal processing • More sophisticated user interface • LCD screen • Buttons • Threshold calibration • Low battery indicator • Electrode leadwire connectors • FDA regulations

  21. Conclusions • The 8051 microcontroller is not yet accurately measuring absolute and relative maximum amplitudes of the signal • Electrical signals were successfully measured using surface electromyography • Continuing work will be done to successfully analyze the signal and provide users a feedback response based on muscle fatigue

  22. Acknowledgements • Professor Hanson • Professor Hedrick • Ben Bunes

  23. Bibliography Association for Applied Psychophysiology and Biofeedback. 4 June 2008 <http://www.aapb.org/i4a/pages/index.cfm?pageid=1>. Gariety, Arthur and Madoff, Benjamin. ECE 476 Final Project: Wireless Electromyograph. 13 November 2008 <http://instruct1.cit.cornell.edu/Courses/ee476/FinalProjects/s2005/bsm24_ajg47/website/website/index.html>. U.S. National Library of Medicine, National Institutes of Health. Electromyography. 4 June 2008 <http://www.nlm.nih.gov/medlineplus/ency/article/003929.htm>. http://www.dataq.com/images/article_images/emg1.jpg http://www.lpkf.com/_images/757-lpkf-protomat-h100.jpg http://www.winning.co.za/images/exImage6.jpg http://www.zoodu.com/uploads/images/2006-08-10/vlt9QAl2A5.jpg

  24. Questions?