MECHANOMYOGRAPHIC ANALYSIS WITH 0.2 S AND 1.0 S

TIME DELAY AFTER ONSET OF CONTRACTION

Eddy Krueger, Eduardo M. Scheeren, Gean Francesco D. Chu, Percy Nohama

CPGEI, Federal Technological University of Paraná, Ave Sete de Setembro 3165, Rebouças, Curitiba, PR, Brazil

Guilherme N. Nogueira-Neto, Vera Lúcia S. Nantes Button

School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil

Keywords: Accelerometer, Prosthesis control, Upper limb, Time delay, Onset of contraction, Motion artifact.

Abstract: Muscle contractions generate lateral oscillations and motion artifacts that can be detected by MMG sensors

placed in the inner and outer sides of the forearm. These artifacts can significantly affect signal processing

and eventually it is necessary to eliminate their influence in order to detect movements reliably. One

approach is to respect a time delay after the onset of contraction. This study aimed to evaluate the

correlation of 0.2 s and 1.0 s time delays after the onset of contraction during wrist movements. This work

respected two different time delays before initiating the signal analysis. Two analysis window lengths were

evaluated (0.25 s and 0.50 s). The results showed that there are strong correlations between the acquired

signals with both time delays, mainly the devised RZ feature (0.81–0.95). This study was a first approach to

determine whether triaxial MMG features can be used for motor prosthesis control. The axial moduli

presented strong correlations for all movements and can be productive in future applications.

1 INTRODUCTION

Mechanomyography (MMG) sensors can be built

with triaxial accelerometers (Nogueira-Neto et al.,

2008). These transducers measured the displacement

accelerations during muscle contractions in three

axes of movement and also their modulus. One of

the differences between MMG and EMG temporal

characteristics are the initial and final contraction

movement artifacts (Silva and Chau, 2003) existing

in MMG, that was denominated onset of contraction

(Nolan and dePaor, 2004). These artifacts jeopardize

processing because they contaminate signal temporal

and spectral behaviors (Silva and Chau, 2003). Some

studies use analysis windows with a time delay

beginning on the onset of contraction to characterize

signals, e. g. for prosthesis control (Prociow et al.,

2008, Alves and Chau, 2008). In preliminary tests, a

time delay of 1.0 s after the onset of contraction

(1.0AOC) was considered too long for practical

purposes because it was impossible to characterize

the four different movements using this delay. In

order to control a myoelectrical prosthesis, short

delay and analysis window length (AWL) are

necessary because human perception needs 300 ms

or less to consider an event as having occurred in

real time (Englehart and Hudgins, 2003).

The purpose of this study is to evaluate the

behavior of MMG features obtained with 0.2 s after

onset of contraction (0.2AOC) and 1.0AOC during

four different wrist movements.

2 METHODS

2.1 Volunteers

Twelve male volunteers (24±5.5 years old) without

neuromuscular or elbow and wrist joint problems

performed the tests. The study was approved by the

institute’s ethics committee. All participants were

instructed in detail about the test protocol and they

agreed to participate in the study. Then, they were

submitted to skin preparation (trichotomy and

cleaning) and sensor placement.

296

Krueger E., M. Scheeren E., Francesco D. Chu G., Nohama P., N. Nogueira-Neto G. and Lúcia S. Nantes Button V. (2010).

MECHANOMYOGRAPHIC ANALYSIS WITH 0.2 S AND 1.0 S TIME DELAY AFTER ONSET OF CONTRACTION .

In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing, pages 296-299

DOI: 10.5220/0002746902960299

 SciTePress

2.2 Sensors

The developed MMG sensors used Freescale

MMA7260Q MEMS triaxial accelerometers with

high sensitivity 800 mV/V at 1.5 G (G, gravitational

acceleration). Electronic circuits allowed 10x

amplification and 4-40 Hz Butterworth filtering,

focusing MMG passband (Silva and Chau, 2003).

Individual axes and their modulus were acquired.

A string was stretched from the epicondyle until

the centre of carpal region to help in determining the

right sensor placement, approximately 7 cm from the

epicondyles over the muscle belly of the forearm

(Wojtczak et al., 2009), with the Y axis parallel to

the muscle fibers as shown in Figure 1.

Figure 1: Volunteer and MMG sensor placement.

2.3 Protocol

The volunteers were seated on a chair where they

performed five concentric contractions for each

wrist movement of the dominant limb: flexion,

extension, ulnar and radial deviations. The sequence

of movements was randomly chosen. The limb

stayed loose, closed fist, in neutral anatomical

position without touching any body. Researchers

indicated the start and end of contractions in order to

determine the contraction timing (approx. 2.5 s).

2.4 Data Acquisition and Analysis

A LabVIEW™ program was coded to acquire MMG

signals. All signals and volunteer data were saved

into European Data Format (EDF) files. The data

acquisition board was a Data Translation™ DT300

series with 1 kHz sample rate. Figure 2 shows the

MMG Z axis signal for a wrist flexion detailing the

onset of contraction artifact, 0.2AOC and 1.0AOC

time delays, 0.25 s and 0.5 s AWLs.

Figure 2: MMG Z axis. Details: onset of contraction,

0.2AOC, 1.0AOC and AWL intervals (darker shades 0.25

s AWLs and darker plus lighter shades 0.5 s AWLs).

From the five repeated contractions, the central

one was chosen for analysis. Temporal features were

calculated for all signals and each AWL. Root mean

square (RMS) is the quadratic mean indicating the

range of muscle displacement represented by its

acceleration. Zero-crossing is the number of times

that the signal crossed the baseline. Peak counting is

the number of peaks (sub-window of 30 ms) in the

AWL. Zero-crossing and peak counting are temporal

features, however both have direct connection with

frequency spectrum. Zero-crossing has close relation

with the signal fundamental frequency and peak

counting is related to spectral higher frequencies. In

addition to the raw features, multiplications were

performed in order to devise hybrid values. MMG

captures muscle oscillations during contraction and

stretching (unlike EMG for electrical activity).

Eventually, RMS is bigger during stretching than

contraction. The multiplication of RMS (energy

related) and zero-crossing (frequency related) can

enhance the discriminatory procedure, thus the RZ

feature was created. After t-test analysis, Pearson’s

correlation coefficients (R) between 0.2AOC and

1.0AOC were calculated. In order to determine R

between individual values of each sensor, data were

splitted by: (a) movement, (b) side of the forearm

(inner and outer), (c) axes/modulus, (d) AWL and

(e) analysis.

3 RESULTS

The t-test revealed that the values of all features,

signals and AWLs were different when comparing

0.2AOC and 1.0AOC. Table 1 shows only the R

values that presented strong correlation between

0.2AOC and 1.0AOC, i. e. values greater than 0.75.

MECHANOMYOGRAPHIC ANALYSIS WITH 0.2 S AND 1.0 S TIME DELAY AFTER ONSET OF CONTRACTION

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Table 1: Features that presented strong correlation

coefficients (R) between 0.2AOC and 1.0AOC.

Side Mov AWL Axis/Mod Feature R

Inner

Ext

.25s

Z RZ 0.79

Mod

RZ 0.81

Zc 0.88

.5s

RMS 0.79

RZ 0.82

Y RZ 0.88

Mod

RZ 0.93

Zc 0.84

Rad

.25s

RMS 0.80

RZ 0.91

Mod Zc 0.82

.5s

RMS 0.87

Zc 0.84

RZ 0.95

Y RZ 0.84

Mod

Pc 0.78

RMS 0.78

RZ 0.93

Zc 0.88

Flex .5s X RMS 0.84

Outer Flex .5s Mod Zc

0.90

Wrist movement (Mov), extension (Ext), radial deviatio

(Rad), flexion (Flex), modulus (Mod), zero-crossing (Zc),

peak counting (Pc), RMS*zero-crossing (RZ)

4 DISCUSSION

The purpose of this study was to evaluate the

difference between 0.2AOC and 1.0AOC in forearm

muscles by MMG analysis. Since this work was a

first approach, we decided to take the extremities: a

short (0.2 s) and a long time delay (1.0 s). We

hypothesized that if 0.2 s did not have difference

with 1.0 s delay then it would be unproductive to

investigate intermediate delays.

The t-test showed that all feature values between

0.2AOC and 1.0AOC were different. This can have

occurred because the onset of contraction (see

Figure 1) had a high amplitude and since it is within

any 0.2AOC AWL it was expected that the artifact

influenced the analysis.

According to the results indicated in Table 1, the

major number of strong correlations occurred on the

inner side of the forearm. Anatomically, the inner

side is responsible for wrist flexion and ulnar

deviation movements. However, even though the

extention and radial deviation belong to the outer

side, they appeared several times in the inner side

sensor. This was considered a correlation between

0.2AOC and 1.0AOC during antagonist movements.

It was assumed that 0.2AOC incorporated the initial

contraction interference that follows immediately the

onset of contraction. In this perspective, the strong

correlations between 0.2AOC and 1.0AOC, recorded

by an MMG sensor positioned over antagonist

muscles, mean that this muscle group do not

introduce significant interference from the onset of

contraction. From the control strategy point of view,

this is an important finding because spurious

contractions registered on this side could be rejected.

The feature of flexion movement was singular

because it was the only agonistic movement that

appeared in the inner side and the only one with R >

0.75 in the outer side (in spite of being antagonist).

The greater the R (the closest to 1.0) the more

similar the features behaved comparing 0.2AOC and

1.0AOC. Therefore, despite using a short delay will

probably involve a part of the onset of contraction,

choosing 0.2AOC and 0.25 s delay is acceptable

because the total time is almost completely within

human perception range (Englehart and Hudgins,

2003).

Strong correlations were not observed for ulnar

deviation in both sides. Apparently, the inner side

was affected by the flexion and ulnar deviation

movement artifacts, because of the inexistence of

strong correlations. The observation of eleven

correlations to radial deviation obtained for the inner

side is attributed to the contractions having lower

amplitude movement artifacts. Radial deviation

range of movement (21°±4.0°) (Cipriano, 2003) is

the smallest among the other wrist movements and,

thus, can support the idea of onset of contraction

amplitude interference.

(Petitjean et al., 1998) used electrical stimulation

to obtain a single twitch. Their results showed that

the increase in electrical stimulation amplitude lead

to an increase in MMG peak-to-peak amplitude, but

the duration of movement artifact was very similar,

approximately 20 ms.

Some studies used a time delay after the onset of

contraction to minimize the movement artifact. Such

time delays range from 0.67 s up to 1.0 s (Beck et

al., 2004, Alves and Chau, 2008, Nolan and dePaor,

2004, Smith et al., 1998).

Another strategy is to dismiss the initial 30% of

signal, thus eliminating the movement artifact

generated at the onset of contraction, and analysing

the remaining signal (Prociow et al., 2008).

However, this approach can lead to latency problems

due to human perception and further works in

prosthetic control can face practical problems in

patient-prosthesis interaction.

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One interesting observation has to do with the

moduli features. The moduli presented many strong

correlations, five against one, two and four

occurrences for Z, Y, and X axes, respectively. They

had strong correlation between 0.2AOC and

1.0AOC for both AWLs. The moduli values can be

calculated with bi- and triaxial accelerometers and

their use can be helpful because of results

repeatability, specially the number of zero-crossings.

The RZ feature brought good perspectives to

wrist movement analyses. Table 1 shows nine strong

correlations whereas the RMS value and zero-

crossing, from where it is derived, presented six and

five correlations respectively. The use of peak

counting to determine muscle contraction was not

effective when performed concomitantly with

movement artifact, since only one correlation was

observed between 0.2AOC and 1.0AOC. Further

studies with new indicators can improve MMG

movement analysis, making unnecessary to

eliminate unwanted artifact interferences in the onset

of contraction, therefore, enhancing myoelectrical

prosthesis control.

5 CONCLUSIONS

This paper investigated mechanomyographic

analyses with 0.2 s and 1.0 s time delay after onset

of contraction during four wrist movements. The

main outcome was the great amount of correlation

between antagonist sides. In such case, the strong

correlations between 0.2AOC and 1.0AOC in

antagonist sides mean that the onset of contraction

do not interfere with the time delay. Radial deviation

has a smaller range of movement and for this reason

varying the time delays before the analyses did not

affect their correlation. Modulus was the most

frequent feature with strong correlation with varying

time delay what showed its repeatability. The

correlations were strong for antagonist movements

mainly in the inner side of the forearm.

Of all analysed features for 0.2AOC and

1.0AOC, it was demonstrated that in antagonist

movements RZ feature, zero-crossing and RMS are

very similar and can be used, if necessary, to reduce

the time delay for myoelectrical prosthesis

activation.

ACKNOWLEDGEMENTS

We would like to thank CNPq, CAPES and FINEP

for important funding and financial support and

Sidnei Schuindt for material support.

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