MECHANOMYOGRAPHIC SENSOR

A Triaxial Accelerometry Approach

Guilherme Nunes Nogueira-Neto

Departamento de Engenharia Biomédica, UNICAMP, Cidade Universitária Zeferino Vaz, P.O. 6040, Campinas, Brazil

Ronie Wesley Müller, Fábio Andrey Salles, Percy Nohama, Vera Lúcia da Silveira Nantes Button

CPGEI, Universidade Tecnológica Federal do Paraná, Av Sete de Setembro, 3165, Curitiba, Brazil

Departamento de Engenharia Biomédica, UNICAMP, Cidade Universitária Zeferino Vaz, P.O. 6040, Campinas, Brazil

Keywords: Mechanomyography, accelerometer, triaxial.

Abstract: Recently, accelerometers have been used to acquire mechanomyography signals. These signals are due to

muscle lateral oscillations during contraction. In this study, a sensor acquired such vibrations in three

directions. A triaxial accelerometer-based sensor was constructed and tested with a controlled mechanical

vibrator and subwoofer speaker (both from 10Hz up to 40Hz) during isokinetic muscle contraction (3

volunteers, 50 extensions at 300 degrees/s). With triaxial accelerometry it was possible to compute the

MMG modulus signal. For normalised and average values, MMG amplitude presented strong correlation

coefficients (R=0,89) with RMS and peak torque. Below 80% of normalised data, MMG amplitude and

torque values (RMS and peak) seem to converge.

1 INTRODUCTION

In the last decades, the acquisition of oscillatory

waves of contracting muscles has been performed

with diverse sensors like piezoelectric and condenser

microphones (Brozovich & Pollack, 1983; Stokes &

Cooper, 1992) and hydrophones (Orizio, 1993)

under different acronyms: acousticmyography

(AMG); sound-myography (SMG); vibromyography

(VMG); and phonomyography (PMG). Such

oscillations originate from lateral movement of

muscle fibres (Orizio, Perini, & Veicsteinas, 1989b).

Recently, these waves have been acquired by means

of accelerometers (Watakabe, Mita, Akataki, & Ito,

2003) and the technique named mechanomyography

(MMG). Laser displacement sensors have also been

used (Orizio, Gobbo, Diemont, Esposito, &

Veicsteinas, 2003).

The MMG literature presents studies performed

with isolated muscles and voluntary contraction

tests. Almost all of them have given emphasis in

monitoring the vibratory axis orthogonal to the

muscle belly. This could be assigned to the materials

used in the manufacture of those sensors. As

microelectromechanical systems (MEMS) advance,

new, smaller, more precise and sensible sensors are

developed. Today it is possible to find commercial

monoaxial accelerometers of 1,2V/g and triaxial

ones of 800mV/g.

The MMG signal can be useful for providing

muscle function information different from that

obtained by the electromyography (EMG) and

torque analysis (Orizio, Perini, & Veicsteinas,

1989a). MMG signal time and frequency domain

analyses can help in determining muscle fatigue

(Shinohara, Kouzaki, Yoshihisa, & Fukunaga,

1998).

With efforts aimed at detecting localized muscle

fatigue, defined as the failure to maintain muscle

power output (Fitts, 1994), a triaxial accelerometer

sensor and acquisition system were developed and

described in this paper.

2 METHODS

In this section we will present the hardware and the

methods employed for the sensor assessment.

176

Nunes Nogueira-Neto G., Wesley Müller R., Andrey Salles F., Nohama P. and Lúcia da Silveira Nantes Button V. (2008).

MECHANOMYOGRAPHIC SENSOR - A Triaxial Accelerometry Approach.

In Proceedings of the First International Conference on Biomedical Electronics and Devices, pages 176-179

DOI: 10.5220/0001054601760179

 SciTePress

2.1 Hardware

Taking into account that muscle displacements

during isometric contraction are minimal, the sensor

circuitry was greatly reduced (2,2x2,9cm

, 4g). The

hardware was divided into two boards. The first one

(Figure 1) is a double-faced board. On one face is

the triaxial accelerometer circuit (Freescale

MMA7260Q, capacitive, high sensitivity

800mV/g@1,5g) and on the other face is the SMD

passive filter circuit: one high-pass (fc=3Hz) and

one low-pass (fc=1,5kHz) filter per axis. The static

acceleration was eliminated with high-pass filtering.

Therefore, as the inclination of body segments does

not vary so abruptly, its influence is ignored.

The second board lays at 10 cm from the first

one and consists of supply circuit (+/–10V) for the

inverter operational amplifier (G=37,5dB) and 3,3V

regulation circuit.

Figure 1: MMG sensor (both faces).

Sensor and cabling were completed shielded by

aluminium foil and bandage.

For acquisition and assessment purposes, the

signals were concentrated in a DT300 series Data

Translation™ acquisition board, 12-bits, 8

differential input channels and 1kHz sampling

frequency.

2.2 Hardware Assessment Tests

In order to assess the correct operation, the signals

generated were analysed in an FFT-based

LabVIEW™ program. The program used a 40Hz

low-pass Butterworth filter since the MMG signal

energy is primarily comprised below 50Hz (Zagar &

Krizaj, 2005). Figure 2 presents the assessment test

equipment, a PASCO™ digital function PI-9587C

connected to a mechanical wave driver SF-9324.

The MMG sensor was tightly fixed on a plastic

support screwed to the driver.

For the subwoofer test, the sensor was fixed with

double-faced adhesive tape on the woofer.

In the function generator, a sine wave of 0,5Vpp

was set with the following frequencies: 10, 11, 12,

13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, and 40Hz.

These frequencies were selected because of the 8-

50Hz MMG pass-band frequency range.

Figure 2: Assessment test equipment.

2.3 MMG Analysis Software

The MMG signal acquisition and analysis software

was created in LabVIEW™ and described elsewhere

(Salles, Müller, Nogueira-Neto, Button, & Nohama,

2006). Briefly, it processes MMG signals extracting

amplitude (integrated MMG, root mean square or

RMS) and frequency (mean power frequency)

variables of interest. Signals were filtered at 40Hz.

For this test, torque signal acquired from an

isokinetic dynamometer was added to the MMG

analysis software.

2.4 Isokinetic Test Protocol

Three volunteers (between 24 and 30 years)

performed an isokinetic muscle contraction test.

Firstly, they warmed up on a cycle ergometer.

Then, they were asked to perform 50 consecutive leg

extensions at the maximum voluntary contraction

(MVC) they could get while a physician provided

sound feedback. The angular velocity was fixed at

300º/s and the leg movement amplitude limited from

10º of flexion to complete extension (total 100º).

The sensor was placed over the muscle belly of

the rectus femoris muscle, as indicated in Figure 3,

fixed with double-face adhesive tape.

Figure 3: Sensor placement and triaxial orientation.

During the extensions, only the intermediate 270ms

of both MMG and torque signals were taken into

account for statistical analysis due to the

dynamometer initial/final acceleration/deceleration.

The modulus (MMG

MOD

) of the MMG signals

from all three axes was calculated and correlated

MECHANOMYOGRAPHIC SENSOR - A Triaxial Accelerometry Approach

177

with RMS and peak torque values (Torque

RMS

and

Torque

PEAK

, respectively).

3 RESULTS

Figure 4 shows the results from one of the controlled

frequencies on both vibrators. As one can see, the

subwoofer results presented less harmonic

components, and these occurred for all frequencies

from 10Hz to 20Hz. Moreover, the fundamental

frequency matched the desired one near two-decimal

digits of accuracy for all frequencies.

Figure 4: (a) Mechanical vibrator and (b) subwoofer

results during test at 15Hz.

Table 1 shows the correlation coefficients between

MMG

MOD

and torque data. Only V3 did not

presented strong coefficients. Figure 5 shows the

curves with the average normalized MMG

RMS

TORQUE

RMS

and TORQUE

PEAK

as a function of the

extensions.

Table 1: Correlation between MMG

MOD

and torque.

Volunteers V1 V2

TORQUE

RMS

0,79 0,78 0,48

TORQUE

PEAK

0,75 0,76 0,46

Figure 5: MMG

MOD

(star), TORQUE

RMS

(circle), and

TORQUE

PEAK

(square) vs. extension.

The MMG

MOD

average signal presented strong

correlation coefficient with both TORQUE

RMS

and

TORQUE

PEAK

average signals (R=0,89 and R=0,89,

respectively).

4 DISCUSSION

The results of the hardware assessment test showed

that the mechanical vibrator introduced more

harmonic components than the subwoofer. This can

be partly due to the difference between the

amplitudes of vibration, and partly assigned to the

fixation method. The sensor was tightly fixed on the

plastic support of the driver. However, it was loosely

fixed on the subwoofer membrane. The damping

effect can be responsible for the harmonic

suppression. Also, the amplitude of vibration was

maximal for the driver, but partial for the subwoofer

because the sensor was not placed over the axis of

movement. When analysing spectral indicators, it is

important to have this in mind.

The triaxial sensor-based MMG analysis

becomes acceptable when someone considers a

physiological approach. Inside the thigh, the rectus

femoris muscle is surrounded by subcutaneous fat

layer under the skin (Hudash, Albright, McAuley,

Martin, & Fulton, 1985). It is difficult to determine

the exact direction of muscle vibrations. Fibres are

constantly changing length. Moreover, in the

quadriceps group there are other muscle oscillatory

sources (e.g. vastus medialis) that can indirectly

reduce and distort the MMG signal acquired by the

sensor placed over the rectus femoris muscle belly.

MMG presents greater correlation with torque

when it is measured at the muscle belly (Cescon,

Farina, Gobbo, Merletti, & Orizio, 2004). A

theoretical advantage of triaxial accelerometry is

that MMG

MOD

is less sensitive to variations in sensor

positioning and orientation than individual axes.

On the other hand, triaxial accelerometers tend to

have larger dimensions and can negatively affect

MMG signal analysis due to distortions (Watakabe

et al., 2003). However, it does not seem to be the

case of the sensor described in this paper.

Regarding the correlation coefficients, the high

values (R=0,89) obtained for the average MMG and

torque data are similar to those previously obtained

for peak torque during isokinetic contraction at

300º/s (Evetovich et al., 1997).

It was assumed that volunteers used the

maximum voluntary contraction (MVC) at the

beginning of the tests and, along with the exercise,

torque loss occurred which would lead to localized

BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices

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muscle fatigue. The average normalized MMG

RMS

TORQUE

RMS

, and TORQUE

PEAK

curves seem to

converge for values below approximately 80% of

maximum normalized amplitude and diverge above

it. Similar results were found by researchers

studying isometric contractions (Orizio et al.,

1989b). However, it is not possible to affirm that

volunteers used MVC, because data have been

normalized.

5 CONCLUSIONS

When computing spectral values based on MMG

monitoring of muscle contraction, it is important to

consider the effect of the sensor adhesion technique

because it can influence the calculus of e.g. mean

power frequency. The moduli of the signals acquired

by the triaxial accelerometer sensor present good

correlation with RMS and peak torque. MMG

MOD

can be a good indicator of torque loss during

isokinetic contractions. The MMG and torque

amplitudes (RMS and peak) seem to converge for

values below 80% of normalised data (presumably

80%MVC). The results obtained in the preliminary

tests, with three volunteers, showed that the sensor is

viable. These tests consist in the initial efforts for

assessing the sensor and it will be complemented

with a wider volunteer population.

ACKNOWLEDGEMENTS

Guilherme Nogueira would like to thank CNPq –

Conselho Nacional de Desenvolvimento Científico –

Fundação Araucária and FINEP for the financial

support.

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