2. Directly control video games with muscles
Uses Arduino UNO microcontroller
Acts as HID keyboard interface
Muscle sensor design for signal processing
Allows two muscles to act independently and
in combination to control three buttons
(left bicep , LEFT) (Right bicep , RIGHT)
(combination , ROTATE)
Illustrated using Tetrix game
3.
4. To facilitate game-playing in people with
special needs
Pilot project for a diagnostic tool for
identifying neuromuscular diseases and
disorders of motor control
Could be extended for use in control of
moving objects such as mobile robots and an
electric wheelchair
5. Handicapped persons
People with neuromuscular diseases
Obsolete gaming models
Electronics Engineering theory into practice
Biomedical Hobbyist
6.
7. Electrical potential produced by electrically or
neurologically activated muscles
Electrical source is the muscle membrane
potential of about -90 mV
Measured EMG potentials range from less
than 50 µV and up to 20-30 mV
Typical repetition rate of muscle motor unit
firing is about 7-20 Hz
Surface EMG and intramuscular EMG
Muscle tissue at rest is normally electrically
inactive
8. Superimposed motor unit action potentials (
MUAPs )
Can be decomposed into their constituent
MUAPs for analysis
MUAPs from different motor units tend to
have different characteristic shapes
9. 1 x Arduino Uno R2 ( needs the atmega8u2
USB chip which is only available on newer
Arduino MCUs )
1 x Arduino Project Enclosure
1 x USB cable for the Arduino
2 x Muscle Sensors
1 x 12V Power Supply
2 sets of EMG Cables and Electrodes
10. 3 x TL072 IC Chip
1 x INA106 IC Chip
2 x 9V Battery
2 x 9V battery clips
2 x 1.0 µF Tant capacitor
1 x 0.01 µF Ceramic Disc capacitor
1 x 1.0 µF Ceramic Disc capacitor
3 x 150 kOhm 1% resistor
2 x 1 Mohm 1% ( Refer to Complete list )
11. Signal acquisition
INA106 difference amplifier ( G = 110 )
Signal conditioning – Amplification
TL072 inverting amplifier ( G = -15 )
AC coupling
High pass filter
Signal conditioning – Rectification
Active full-wave rectifier
Signal conditioning – Smoothing +
Amplification
12.
13.
14. The two sets of electrodes limit allowable
button count
Could use DSP
Wireless technology
16. Covered aspects completed successfully
Project to be completed by mid-April
17. 1. ^ Kamen, Gary. Electromyographic Kinesiology. In Robertson, DGE et al. Research Methods in Biomechanics. Champaign, IL: Human
Kinetics Publ., 2004.
2. ^ Electromyography at the US National Library of Medicine Medical Subject Headings (MeSH)
3. ^ Nigg B.M., & Herzog W., 1999. Biomechanics of the Musculo-Skeletal system. Wiley. Page:349.
4. ^ Cram, JR.; Steger, JC. (Jun 1983). "EMG scanning in the diagnosis of chronic pain.". Biofeedback Self Regul 8 (2): 229–
41. PMID 6227339.
5. ^ Arthur C. Rothman, MD, v. Selective Insurance Company of America, Supreme Court of New Jersey, Jan. 19
6. ^ Texas Court of Appeals, Third District, at Austin, Cause No. 03-10-673-CV. April 5, 2012
7. ^ Section 333.17018 Michigan Compiled Laws http://legislature.mi.gov/doc.aspx?mcl-333-17018
8. ^ Harvey AM, Masland RL: Actions of durarizing preparations in the human. Journal of Pharmacology And Experimental Therapeutics,
Vol. 73, Issue 3, 304-311, 1941
9. ^ Botelho SY: Comparison of simultaneously recorded electrical and mechanical activity in myasthenia gravis patients and in partially
curarized normal humans. Am J Med. 1955 Nov;19(5):693-6.PMID 13268466
10. ^ Christie TH, Churchill-Davidson HC: The St. Thomas's Hospital nerve stimulator in the diagnosis of prolonged apnoea. Lancet. 1958
Apr 12;1(7024):776. PMID 13526270
11. ^ Engbaek J, Ostergaard D, Viby-Mogensen J: Double burst stimulation (DBS): a new pattern of nerve stimulation to identify residual
neuromuscular block. Br J Anaesth. 1989 Mar;62(3):274-8. PMID 2522790
12. ^ Andreasen, DS.; Gabbert DG,: EMG Switch Navigation of Power Wheelchairs, RESNA 2006. [1]
13. ^ Park, DG.; Kim, HC. Muscleman: Wireless input device for a fighting action game based on the EMG signal and acceleration of the
human forearm. [2]
14. ^ Hsu, Jeremy (2009-10-29). "The Future of Video Game Input: Muscle Sensors". Live
Science. http://www.livescience.com/technology/091029-ttr-muscle-sensing.html. Retrieved 2010-01-16.
15. ^ "Recognizing Gestures from Forearm EMG Signals". United States Patent and Trademark Office. 2008-06-
26. http://appft.uspto.gov/netacgi/nph-
Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%222009032
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