Mechanomyogram chandra sen vikram

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A Literature review

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Mechanomyogram chandra sen vikram

  1. 1. Literature Review by :Chandra Sen VikramMSc and DIC (Neurotechnology)Imperial College London
  2. 2. Mechanomyogram (MMG)Source: Pressure wave generated by contracting muscle owing to lateraldimensional changes in active muscle fibers.Detection : vibration transducer on the body surface overlying the muscle.• Piezoelectric contact sensors• condenser microphones• Accelerometers• Laser distance sensorApplication :• Assessing muscular fatigue• Diagnosing muscle disease• Controlling upper-limb prostheses• Monitoring the dystrophic process
  3. 3. EMG Vs MMGEMG :• Detection, analysis and use of electrical signal that emanates from skeletal muscles• 0-6 mV• Frequency of 10-500 Hz (tonic - Type I &phasic - Type II), Face-500 Hz, Heart-100 Hz• Require a lot of filtering hardware and classification algorithm o motion detection filters in order to separate signal from artefacts. o 50Hz noise due to power line interferenceMMG :• Lateral oscillations at the resonant frequency of the muscle at the initiation of a contraction• Frequency vibrations of 5-100Hz• Higher signal-to-noise ratio than surface EMG• Does not have any 50Hz power-line interference• Less sensitive to motion artefact• Can monitor muscle activity from deeper muscles without the need of needle electrodes that are sometimes required in EMG signal acquisition
  4. 4. MMG Signal Acquisition• MMG signal acquisition by transducer• Amplified by the AC amplifier• Filtered with a bandwidth of 2-300 Hz by an 8th-order Butterworth filter• Power spectral density function by FFT • Root mean squared amplitude (RMS) • Mean power frequency (MPF) • Amplitude spectral density function (ASD)
  5. 5. MMG Signal Acquisition ReliabilityFrequency response of a condenser microphone is declined with decreasing diameterand decreasing length of the air chamber.Microphones are less sensitive to motion artefactSo preferred for detecting MMG as the musclesite is prone to movement.Mechanical behaviour of a piezoelectric contactsensor depends greatly on its attachment to thebody surface and contact pressure.Accelerometer is widely used because of• Light weight,• Small dimensions,• Easy attachment• High reliability.Output signal can be easily converted to physical units (metres per square second)
  6. 6. MMG Signal Acquisition Reliability
  7. 7. MMG Signal Acquisition Reliability• LDS , most accurate non contact MMG transducer without distortion• RMS amplitude and MPF increased as force levels increased and were in close agreement for the LDS signal and the double integral of the ACC signal• Paired t-test showed no significant difference• MMG signal detected with the accelerometer during voluntary muscle contractions accurately reflected acceleration of the vibration on the body surface.• MMG signal was gradually distorted when weight was added to the accelerometer.• The attenuation distortion began from low frequencies, and its attenuation slope became more remarkable with the increase of additional weight.
  8. 8. Coupled microphone-accelerometer sensorpair for dynamic noise reduction in MMG signal recording Silicon acted as a passive lowpass filter that helped to increase the SNR of the measurement Desirable mechanical impedance mismatch between both transducers for signals arriving from the microphone side, while both transducers were sensitive to signals originating from external forces. Accelerometer was capable of recording the direct effects of forces acting on the forearm as a whole.
  9. 9. Coupled microphone-accelerometer sensorpair for dynamic noise reduction in MMG signal recordingDuring extension, the amplitude is directlyproportional to contraction strengthDiscriminate between limbmovement and useful MMG signalsRMS value of the accelerometersignal as a dynamic threshold forthe microphone signal.High RMS value in the accelerometersignal indicates the presence of motionartefact and the microphone signal should notbe used directly for prosthesis control
  10. 10. MMG and force relation Mechanomyographic responses during voluntary ramp contractions of the human first dorsal interosseous muscleAim : Mechanomyogram (MMG) and force relationship of the first dorsal interosseous(FDI) muscle as well as the biceps brachii (BB) muscle during voluntary isometric rampcontractions.Subjects were asked to exert ramp contractions of FDI and BB muscle from 5% to 70% ofthe maximal voluntary contraction (MVC) at a constant rate of 10% MVC/s.
  11. 11. MMG and force relationMechanomyographic responses during voluntary ramp contractions of the human first dorsal interosseous muscle
  12. 12. MMG and force relation Mechanomyographic responses during voluntary ramp contractions of the human first dorsal interosseous muscle• Beyond 70% MVC the force output deviated markedly from the criterion of the ramp contraction trials. • Beginning of muscle fatigue due to the progressive and cumulative force production • some of the FDI MUs with high recruitment threshold displayed sharp bursts of activity with rapid increase in firing rate as force levels approached 80% MVC• Amplitude of the MMG increases with the number of recruited MUs• Decreases with higher firing rate due to fusion of the MU mechanical activity• MPF of the MMG, as well as the median frequency, reflects the averaged firing rate of the active MUs• Results demonstrated a progressive increase in the RMS amplitude followed by a decline at greater force levels in both FDI and BB muscles.• In large limb muscles, force production is controlled by recruitment of the MUs up to higher force levels, while recruitment in small hand muscles is completed early.
  13. 13. Uncovering patterns of forearm muscle activity using multi-channel mechanomyography• Determine if multisite MMG signals exhibit distinctive patterns of forearm muscle activity• 14 features were classified by a linear discriminant analysis classifier• MMG patterns are specific and consistent enough to identify 7 ± 1 hand movements with an accuracy of 90 ± 4%
  14. 14. Uncovering patterns of forearm muscle activity using multi-channel mechanomyographyOnset times were determined by the firstindication of hand movement detected by thetri-axis accelerometer on the participant’shand.
  15. 15. Uncovering patterns of forearm muscle activity using multi-channel mechanomyography
  16. 16. Uncovering patterns of forearm muscle activity using multi-channel mechanomyography
  17. 17. Thank you

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