A Literature review

1. Literature Review by :
Chandra Sen Vikram
MSc and DIC (Neurotechnology)
Imperial College London

2. Mechanomyogram (MMG)
Source: Pressure wave generated by contracting muscle owing to lateral
dimensional changes in active muscle fibers.
Detection : vibration transducer on the body surface overlying the muscle.
• Piezoelectric contact sensors
• condenser microphones
• Accelerometers
• Laser distance sensor
Application :
• Assessing muscular fatigue
• Diagnosing muscle disease
• Controlling upper-limb prostheses
• Monitoring the dystrophic process

3. EMG Vs MMG
EMG :
• 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 interference
MMG :
• 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. 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. MMG Signal Acquisition Reliability
Frequency response of a condenser microphone is declined with decreasing diameter
and decreasing length of the air chamber.
Microphones are less sensitive to motion artefact
So preferred for detecting MMG as the muscle
site is prone to movement.
Mechanical behaviour of a piezoelectric contact
sensor depends greatly on its attachment to the
body 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. MMG Signal Acquisition Reliability

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. Coupled microphone-accelerometer sensor
pair 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. Coupled microphone-accelerometer sensor
pair for dynamic noise reduction in MMG signal recording
During extension, the amplitude is directly
proportional to contraction strength
Discriminate between limb
movement and useful MMG signals
RMS value of the accelerometer
signal as a dynamic threshold for
the microphone signal.
High RMS value in the accelerometer
signal indicates the presence of motion
artefact and the microphone signal should not
be used directly for prosthesis control

10. MMG and force relation
Mechanomyographic responses during voluntary ramp contractions
of the human first dorsal interosseous muscle
Aim : Mechanomyogram (MMG) and force relationship of the first dorsal interosseous
(FDI) muscle as well as the biceps brachii (BB) muscle during voluntary isometric ramp
contractions.
Subjects were asked to exert ramp contractions of FDI and BB muscle from 5% to 70% of
the maximal voluntary contraction (MVC) at a constant rate of 10% MVC/s.

11. MMG and force relation
Mechanomyographic responses during voluntary ramp contractions
of the human first dorsal interosseous muscle

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. 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. Uncovering patterns of forearm muscle activity
using multi-channel mechanomyography
Onset times were determined by the first
indication of hand movement detected by the
tri-axis accelerometer on the participant’s
hand.

15. Uncovering patterns of forearm muscle activity
using multi-channel mechanomyography

16. Uncovering patterns of forearm muscle activity
using multi-channel mechanomyography

17. Thank you