Mechanomyogram chandra sen vikram

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

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.

• Assessing muscular fatigue • Diagnosing muscle disease • Controlling upper-limb prostheses • Monitoring the dystrophic process

• Piezoelectric contact sensors • condenser microphones • Accelerometers • Laser distance sensor

Application :

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

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)

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)

MMG Signal Acquisition Reliability

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.

MMG Signal Acquisition Reliability

Coupled microphone-accelerometer sensor pair for dynamic noise reduction in MMG signal recording

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.

Silicon acted as a passive lowpass filter that helped to increase the SNR of the measurement

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

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.

MMG and force relation Mechanomyographic responses during voluntary ramp contractions

of the human first dorsal interosseous muscle

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.

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%

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.

Uncovering patterns of forearm muscle activity using multi-channel mechanomyography

Uncovering patterns of forearm muscle activity using multi-channel mechanomyography

Thank you