Identification of Femoral-Acetabular Symptoms using sEMG Signals
during Dynamic Contraction
Zahra Karimi Tabar
1
, Chris Joslin
1
, Mario Lamontagne
2
and Giulia Mantovani
2
1
Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, Canada
2
School of Human Kinetics, University of Ottawa, 200 Lees Avenue, Ottawa, Canada
Keywords: Discrete Wavelet Transformation, DWT, EMG Signal Analysis, FAI, Hip Muscles, Non-Stationary Signal,
Signal Decomposition, Wavelet Decomposition.
Abstract: This paper focuses on development of an algorithm that automatically differentiates a Femoro-Acetabular
Impingement (FAI) patient from a healthy control person by comparing their surface electromyography
(sEMG) signal recorded from Gluteus Maximus (GMax), Tensor Fasciae Latae (TFL), and Rectus Femoris
(RF) muscles in the hip area. A discrete wavelet transform (DWT) method was used to analyse sEMG signals
by thirty-eight different wavelet functions (WFs) with 5 decomposition levels of dynamic contractions during
the three phases (descending, stationary, and ascending) of a squat task. The Bior3.9 WF was selected as it
provided higher amount of energy for most of the subjects and then the wavelet power spectrum was computed
for healthy control and FAI groups. The results show that the RF muscle is more active in the ascending phase
than the descending phase for FAI subjects, whereas it is more active in the descending phase for healthy
control. An independent sample t-test was used to check the activities of muscle in both groups. The results
demonstrate no significant difference for GMax (p=0.7477) and TFL (p=0.4997) muscles, while there is a
significant difference for RF muscle (p=0.0670).
1 INTRODUCTION
Femoro-Acetabular Impingement (FAI) is a
pathological condition in which the femoral head and
acetabular socket interact abnormally in the hip joint
(Myers, 1999). This abnormality reduces range of
motion and ability in patients (Keogh, 2008). In
young and active adults with FAI, the pain is usually
in the groin area (Samora, 2011). Hip bone
abnormalities can damage soft tissue structures and
limit the patients’ motion. Useful information can be
obtained from the muscles and such information has
clinical and engineering applications by measuring
Electromyography (EMG) signals. EMG is a
biomedical signal that provides a great source of
information to clinicians and researchers by
measuring the electrical currents generated in
muscles during contraction (Reaz, 2006). The
physiological and anatomical properties of muscles
can influence on the nervous system that controls the
EMG signal (Ahmed, 2009).
We propose an algorithm to discriminate a FAI
patient from a healthy person by comparing their
EMG signal recorded from hip muscles. The
proposed algorithm will produce a way to diagnose
FAI based on muscle activities, which can be a
complement to MRI and x-ray methods.
1.1 Background
An EMG signal recorded from muscles requires
advanced methods for detection, decomposition,
processing, and classification. To use the EMG signal
for diagnosis, a feature needs to be extracted before it
can be analysed or classified. This is due to the fact
that the raw EMG signal includes both useful
information and noise. EMG features can be
computed in various domains such as time,
frequency, time-frequency, and time-scale domains.
Analysis of EMG data requires rectification and
integration of signals or root mean square values to
extract information related to the amplitude of the
signal, which deals with the time domain
representation. However, the frequency content of
EMG is analysed using Fourier Transform (FT) that
is a unidimensional technique (Karlsson, 2001).
Traditional techniques for analysing surface EMG
signal are based on the FT method. The accuracy and
214
Karimi Tabar Z., Joslin C., Lamontagne M. and Mantovani G.
Identification of Femoral-Acetabular Symptoms using sEMG Signals during Dynamic Contraction.
DOI: 10.5220/0006173802140222
In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 214-222
ISBN: 978-989-758-212-7
Copyright
c
2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
reliability of this technique depends on the data
lengths’ and the signal requires to be stationary.
Moreover, a signal can be stationary or non-
stationary. If a signal is stationary, its properties are
statistically invariant over time; however the transient
events cannot be predicted in non-stationary signals.
The EMG signal is a non-stationary signal.
Furthermore, time-frequency methods that are
appropriate for non-stationary signal are used instead
of frequency methods to improve the EMG analysis
(Karlsson, 2001). Thus, for analysing EMG signals in
both time and frequency, the short-term Fourier
transforms (STFT) or wavelets can be used. The
former of the EMG signals usually has three steps:
recording the EMG signals, decomposition of signals
by signal processing techniques, and classification of
signals for diagnostic purpose.
In daily life activities or movements, subjects
perform more dynamic contractions instead of
isometric contractions. Moreover, in the field of
rehabilitation medicine, sports medicine, and etc.,
tasks similar to daily activities are performed. During
dynamic conditions, number of active motor units,
active muscle fibres, electrode geometry, muscle
fibre lengths, and innervation zone geometry
changes. These factors emphasize that the ME signals
are non-stationary. Therefore, time-frequency
methods have been introduced for the analysis of non-
stationary signals. These time-frequency
representation methods are: STFT, Wigner-Ville
distribution (WVD), Choi-Williams Distribution
(CWD), and wavelet transform (WT) that were
compared with recent studies for accuracy and
precision to analyse the ME signals (Karlsson, 2000
and 2001). A time-frequency analysis based on
wavelets (Meyer, 1993), which is introduced recently,
is an appropriate tool to overcome the limitations of
the traditional time-frequency methods. Karlsson
introduced the wavelet transform as a “mathematical
microscope that help observe various parts of the
signal by setting the focus (Karlsson, 2000 and 2001).
The WT has some advantages over the other time-
frequency methods. WT uses short window for high
frequencies and long window for low frequencies,
although the STFT uses a single analysis window for
all frequencies (Rioul, 1991). Furthermore, the WT
can be used to analyse both stationary and non-
stationary signals in both time and frequency domain.
The WT is classified into continuous wavelet
transforms (CWT) and discrete wavelet transforms
(DWT). The wavelet transform requires the selection
of a mother wavelet depending on the application.
Wavelets are defined by the scaling function (also
called father wavelet) and wavelet function (or the
mother wavelet). The scale function in WT
determines wavelet dilation and compression of the
various wavelets from a mother wavelet.
Furthermore, the optimization of the WT is related to
the scale function, which is used for a specific signal.
The ability of DWT to extract features from the
signal is dependent on choosing an appropriate
mother wavelet function. The common standard
families of wavelet basis functions are Haar,
Daubechies (db1 to db10), Coiflet (coif1 to coif5),
Symmlet (sym2 to sym8), Morlet, and Mexican Hat.
Although there is not a specific rule for selecting a
wavelet basis function, some features of wavelets
cause a specific mother wavelet to be more
appropriate for a particular application and signal
type. According to Santoso et al. (Santoso, 1994)
state, for slow transient disturbances db8 and db10
wavelets were the best choice, whereas for short and
fast transient disturbances db4 and db6 were more
proper. Also, Walker (Walker, 1999) presented
general guidelines for selecting a wavelet such that
db4 was more appropriate for feature extraction and
coiflet6 provided better data compression results. In
order to select a more accurate wavelet function, it is
significant that the characteristic of signal should be
matched with the properties of the wavelet function.
For applying WT to the EMG signal various
mother wavelets and scale functions can be used
although no agreement has been reached regarding
the selection of the mother wavelet and the scale
function. For example, Karlsson et al. (Karlsson,
1999) used a Morlet mother wavelet with a linear
scale function (Karlsson, 1999, 2000, 2003) and Neto
et al. (Neto, 2007) applied Morlet with exponential
function. Von Tscharnar (Von Tscharnar, 2000), used
Cauchy mother wavelets with polynomial function.
Hostens et al. (Hostens, 2004) and Beck et al. (Beck,
2005) applied Daubechies mother wavelets to the
EMG signal with different scale functions. Hostens et
al. (Hostens, 2004), used a linear scale function while
Beck et al. (Beck, 2005) used a dyadic function.
The WT of the EMG signal has been used in
several studies related to muscle fatigue, EMG signal
processing, and muscle strength. Moreover, Neto et
al. (Neto, 2007, 2008), and Von Tscharner et al. (Von
Tscharnar, 2000, 2002, 2003, 2006), used the WT for
the EMG signals that were recorded during the sports.
Flanders (Flanders, 2002) used DWT to identify
the time of occurrence of EMG bursts. They chose
wavelet db2 and focused on the coefficient at an
intermediate scale (D3) because by plotting the peak-
to-peak range of the db2 weighting coefficient values
of several muscles, the D3 component had the largest
peak weighting coefficients based on their research.
Identification of Femoral-Acetabular Symptoms using sEMG Signals during Dynamic Contraction
215
Thus, Flanders demonstrated that the simplest
wavelet such as db2 was suitable for recognizing
multiunit EMG bursts. Moreover, Kilby and Gholam
Hosseini (Kilby, 2004) also used mother wavelet that
had a different set of families to extract detailed
features of the sEMG signals. They used mother
wavelets that were available in LabVIE to
decompose the sEMG signals and reconstruct the
signal back. The reconstructed signals were
subtracted from the original signals in order to
calculate the errors to achieve statistical analysis.
Based on different errors of the various families of the
mother wavelets, they concluded the Daubechies
(db5) was the most appropriate mother wavelet for
analysing sEMG signals. However, they did not
consider several mother wavelets such as Morlet,
Meyer, and Mexican Hat because these mother
wavelets were not available by the software. Ahmed
et al. (Ahmed, 2009) illustrated a comparative study
of decomposing sEMG signals by using different
types of wavelets. Their goal was to choose a wavelet
based on the best possible energy localization in the
time-scale plane. In their algorithm, they decomposed
a signal using DWT for various wavelets and the
energy localization in time-scale plane was
calculated. From their results, db4, db5, and db6 were
shown to have the best energy localization compare
to other wavelets for normal and healthy muscle
EMG signal.
2 METHODOLOGY
The EMG data of Gluteus Maximus (GMax), Tensor
Fasciae Latae (TFL), and Rectus Femoris (RF)
muscles were recorded from 30 subjects during
dynamic contraction of a squat task (Fig. 1).
Figure 1: Descending, Stationary, and Ascending Phase of
Squat (Lamontagne, 2009).
During the squat, subjects were required to stand
with feet shoulder-width apart, parallel to one
another. Both arms were anteriorly extended, and
heels were in contact with the floor during the entire
squat. EMG signals were recorded during squat
cycles, thus each recorded signal was divided into
three regions based on squat phases (Descending,
Stationary, and Ascending phase). Five repetitions of
the same movement were executed (Lamontagne,
2009) The recorded sEMG signals formed two
groups: control (15 males and 1 female) and FAI (11
males and 3 females). Participant characteristics are
indicated in Table 1.
Table 1: Participant characteristics by group.
Group
Gender
Weight (Kg)
Height (cm)
Control (CON)
1
M
104.591
191.2
2
M
132.009
179
5
M
113.527
176
6
F
97.245
152
7
M
120.189
180.5
10
M
93.078
175.5
11
M
83.666
180.5
12
M
87.466
168
15
M
117.896
177.5
17
M
83.551
178
19
M
83.311
181
20
M
106.529
183
22
M
82.646
177
23
M
147.725
176
24
M
61.129
160.5
25
M
79.526
175.5
FAI (OR)
3
M
129.012
183.75
4
M
83.035
168
8
M
139.491
186
9
M
133.394
176.5
13
M
126.997
174.2
14
M
101.154
175
16
M
108.719
175
18
M
149.428
176
21
F
97.508
163
26
M
64.312
167
27
M
136.712
167
28
M
120.632
175
29
F
132.507
168.5
30
F
59.766
167
The sampling frequency of the signal was
1000Hz. The DC offset was removed from the raw
EMG signals. In order to be able to compare the EMG
activity in the same muscle on different subjects, the
signals have to be normalized. Normalization of
EMG signals were performed by dividing EMG
signals during a squat task to a reference EMG value
achieved from the same muscle of the same subject.
We utilized the Maximum Voluntary Isometric
Contraction (MVIC) method for normalization. To
measure the MVIC the participants were asked to lie
down on a testing bench, which was provided with
support beams and adjustable straps to limit the
movement of the limbs and hold it in place during
isometric contraction. The MVIC data were collected
BIOSIGNALS 2017 - 10th International Conference on Bio-inspired Systems and Signal Processing
216
for duration of 5 seconds for each muscle. MVICs for
GMax and RF muscles were measured when the
participants’ leg was straight, and they were asked to
push upwards against the Hand-Held Dynamometer
(HHD). Moreover, MVIC for TFL muscle was
collected when the participants were asked to push
diagonally against the HHD. After normalization,
EMG signals were full-rectified meaning that the
absolute value of the signal was used. The rectified
signals were passed through a 5
th
-order low-pass
Butterworth filter with cut off frequency of 10Hz.
This process provided the linear envelope of signals.
Furthermore, the filtered signals were amplitude-
normalized to the peak MVIC EMG (nEMG) and
then integrated to produce the integrated EMG
(IEMG) values.
2.1 EMG Analysis using Wavelet
Transforms
Data analysis was performed using the MATLAB
programming language with the signal processing
and Wavelet toolboxes (The Math Works, INC.). The
EMG values were recorded from GMax, TFL, and RF
muscles of the affected sides of the 30 subjects. There
were 5 repetitions for every subject with sampling
frequency of 1000Hz. The number of samples in each
EMG data set was very large, so EMG signals were
pre-processed by using the wavelet transform.
The analysis of the data commenced by removing
any DC offset in order to be ready for the wavelet
families’ analysis. The wavelet analysis was
performed by a function called mother wavelet. There
are different families or set of mother wavelets in the
Wavelet method which differ in their mathematical
principles named as Haar, Daubechies (db1 to db10),
Symlets (sym2 to 8), Coiflets (coif1 to 5),
Biorthogonal (bior1.1 to 6.8), Reverse biorthogonal
(rbio1.1 to 6.8), Meyer (meyr), Discrete
approximation of Meyer (dmey), Gaussian (gaus1 to
8), Mexican hat (mexh), and Morlet (morl). EMG
signals were decomposed using DWT with various
wavelet functions (WFs). We used discrete wavelet,
which allowed us to decompose our EMG signals
based on Haar, Daubechies (db1 to db10), Symlets
(sym2 to 8), Coiflets (coif1 to 5), Biorthogonal
(bior1.1 to 6.8), and discrete approximation of Meyer
(dmey) mother wavelets. Moreover, MATLAB code
was written to apply a DWT to the EMG signals. The
WT decomposes a signal into several multi-resolution
(levels) components based on basis functions or WFs.
These WFs are achieved by dilation, contraction, and
shifts of a unique function. Decomposition of the
signal into basis of wavelet functions implies the
computation of the inner products between the signal
and the basis function, leading to a set of coefficients
called wavelet coefficients.
The maximum level to apply the wavelet transform
depends on how many data points are contained
within our data set, while there is a down-sampling
by 2 operations from one level to the next level. We
used 5 levels of decomposition. Thirty eight different
wavelet functions exerted at decomposition level 1 to
5. The wavelet coefficients from each wavelet
function were used to calculate the energy of the
sEMG signals for each subject in each phase.
2.2 Wavelet Selection based on Energy
Calculation
The wavelet energy was computed for approximation
(Ea) and detail (Ed) coefficients. Ea is the percentage
of energy corresponding to the approximation and Ed
is the vector containing the percentage of energy
corresponding to the details. Ed for each sEMG signal
was collected after using 38 wavelet functions and the
highest five energies were highlighted for each
subject in three phases. Then, the highest repetition
wavelet function was chosen as our wavelet function.
This procedure is indicated in Fig 2.
Figure 2: Procedure of Selecting a WF.
2.3 Wavelet Power Spectrum
The wavelet power spectrum is a way to determine
the distribution of energy along the sEMG signal.
Wavelet coefficients of the selected wavelet function
were computed and the power spectrum was
measured during 5 levels of decomposition. The
distribution of power for each muscle during
descending, stationary, and ascending phase for both
CON and OR groups was determined. Fig. 3 shows
the block diagram for computing power and
discriminating two groups based on their power
spectrum.
Identification of Femoral-Acetabular Symptoms using sEMG Signals during Dynamic Contraction
217
Figure 3: Block Diagram for Computing Power Spectrum.
3 RESULTS
The algorithm developed in this study evaluated for
its performance by discriminating the CON and OR
group from one another.
EMG signals, which were collected from three
muscles, were analysed by 38 wavelet functions with
5 levels of decomposition for three phases of dynamic
contraction during squat task. The five wavelet
functions that represented the highest amount of
energy for each subject were highlighted. The same
scenario was repeated for all subjects by analysing
EMG signals from all three muscles for each
descending (D), stationary (S), and ascending (A)
phase separately. The highest five energies occurred
in various levels for different subjects and muscles.
The five WFs that indicated the highest percentage of
energy were selected for each subject. The total
number of repetitions for the wavelet functions
between CON and OR group during three phases was
computed for each muscle separately. Bior3.9
wavelet function indicated the highest number of
repetitions between subjects for each muscle during
three phases except for muscle 1 of OR group which
Bior3.7 worked best (Table 2). As the difference
between Bior3.7 and Bior3.9 was not significant in
muscle 1 of OR group, Bior3.9 wavelet function was
used for further analyses.
Table 2: Selection of a WF with Highest Repetition of
Energy.
Subject
Muscle
Wavelet
Function
Total
CON
1 (GMax)
bior3.9
25
OR
1 (GMax)
bior3.7
16
CON
2 (TFL)
bior3.9
20
OR
2 (TFL)
bior3.9
25
CON
3 (RF)
bior3.9
28
OR
3 (RF)
bior3.9
23
3.1 Wavelet Power Spectrum
The WT converts the data array, which is stored from
the EMG recorded signal, into a series of wavelet
coefficients. Each of these coefficients represents the
amplitude of the wavelet function at a specific
location in the array. The best way to specify the
distribution of energy within the data is to compute
the wavelet power. The power, which is the squared
absolute value of the wavelet coefficients, calculated.
The wavelet power spectrum for each muscle during
descending, stationary, and ascending phases was
computed by using Bior3.9 WF. This scenario was
repeated for all subjects from both groups. The
subjects were categorized based on calculated power
of each phase in Table 3. OR and CON groups were
divided into three categories based on the squat
phases. All 16 subjects in the CON group used their
muscle 1 in the ascending phase whereas; most of
them used muscle 2 and muscle 3 in their descending
phase. In OR group, muscle 1 was used in ascending
the same as CON group. However, muscle 2 was used
in ascending and muscle 3 in both descending and
ascending phases.
Table 3: Muscles used by CON and OR During Squat
Cycle.
Descending
Stationary
Ascending
Total
CON
Muscle 1
0
0
16
16
Muscle 2
10
2
4
16
Muscle 3
14
2
0
16
OR
Muscle 1
2
0
10
12
Muscle 2
4
0
8
12
Muscle 3
5
2
5
12
Moreover, after calculating the power of the DWT
coefficients at various levels, the power was then
compared for the sEMG for OR and CON group of
three muscles (GMax, TFL, RF). Fig. 4 and Fig. 5
show the results of each muscle for both groups.
BIOSIGNALS 2017 - 10th International Conference on Bio-inspired Systems and Signal Processing
218
Figure 4: Muscles Power for CON Group.
Figure 5: Muscles Power for OR Group.
As indicated in above graphs, subjects from both
groups used higher power in descending and
ascending phases. In order to indicate whether there
is a significant difference between these two groups
in using their muscles during ascending and
descending phases, the actual A/D ratio for CON and
OR was computed.
By calculating the ratio, we can still see which
phase was the most active; however, we are also able
to see the extent of activity. Therefore, we calculated
the ratio of A/D for every subject in CON and OR
groups and then calculated the average. The resulting
average for each muscle is provided in Table 4 with
the standard deviation for each group.
Table 4: Average Ratio with Standard Deviation Values for
Each Muscle in CON and OR Groups.
CON
OR
Muscle 1 (GMax)
13.38 ± 1.902
11.319 ±1.921
Muscle 2 (TFL)
1.33 ± 0.150
1.829 ± 0.205
Muscle 3 (RF)
0.613 ± 0.090
1.975 ± 0.260
The muscles power is plotted and shown in Fig. 6
for CON and OR group. In CON group muscle 1 and
2 were active in ascending phase whereas, muscle 3
was active in descending phase. Moreover, in the OR
group all three muscles were active in the ascending
phase.
Figure 6: Average Ratio Muscle Power for CON and OR.
In general, the described algorithm can be
summarized as follows:
The raw EMG signals are collected from a
participant for GMax, TFL, RF muscles during
descending, stationary, and ascending phases of squat
task. Then, the collected EMG signals are
normalized. The Bior3.9 wavelet function is applied
to normalized EMG signals and the wavelet
coefficients are computed. Based on wavelet
coefficients the wavelet power spectrum is calculated.
In addition, the ratio power which is the ratio of
ascending power over descending power is computed
in order to identify whether the subject has FAI or not.
If the ratio value for RF muscle is less than zero, it
means the participant used his/her RF muscle in
descending phase and he/she belongs to CON group.
Whereas, if the ratio value is greater than zero, it
means he/she used his/her muscle in ascending phase
and the subject belongs to FAI group.
3.2 Statistical Analysis
In this study an independent samples t-test was used
to check if the two means (averages) from CON and
OR groups are reliably different from one another.
Each t-value has a p-value that is the probability that
the pattern of data in the sample could be produced
by random data. The 2-tail t-test was applied to check
the activities of muscles for CON and OR groups.
Table 5 illustrates no significant difference was found
for GMax and TFL; whereas RF shows a significant
difference between CON and OR groups (p= 0.0670)
although the threshold (p=0.05) was not reached. This
can be due to the fact that the sample size in this study
was small and we require more samples for more
reliable results.
Identification of Femoral-Acetabular Symptoms using sEMG Signals during Dynamic Contraction
219
Table 5: P-value for GMax, TFL, and RF Muscle.
P-value
Muscle 1 (GMax)
0.7477
Muscle 2 (TFL)
0.4997
Muscle 3 (RF)
0.067
4 DISCUSSION
Spectral properties of EMG signals have been defined
by their power spectra. The shape of the power
spectrum can be changed when the EMG signals are
generated from different types of Motor Unit (MU)
(Moritani, 1985; Gerdle, 1998; Elert 1992).
This study has indicated that the wavelet transform
method can be used to quantify features of the muscle
activity for dynamic contraction. The fundamental
properties of EMG spectra are conserved across
dynamic contraction, therefore the WT will be a
useful tool for studying EMG signals. The wavelet
power spectrum of OR and CON groups was analysed
for GMax, TFL, and RF muscles in the hip area by
using the Bior3.9 wavelet function in order to
discriminate the two groups. The power was
calculated for each muscle and an independent
sample t-test was used to check the activity of three
muscles in hip area to discriminate an OR patient
from a healthy person. The p-value for GMax, TFL,
and RF muscles is 0.7477, 0.4997, and 0.0670
respectively for 2-tail test. Therefore, RF muscle is
statistically significant although the threshold
(p=0.05) was not reached due to small sample size.
As a result, RF muscle is more active in ascending
than descending phase for OR people, whereas it is
more active in descending phase for CON people.
Muscle contraction is produced by a sequence of
electrical and chemical events, which start with an
action potential, which is created at the
neuromuscular junction. Individual muscle fibres are
classified into three primary muscle fibre types
named type I, type IIA, and type IIB based on their
contractile and metabolic properties. Type I is
referred to slow twitch oxidative, type IIA is fast
twitch oxidative and type IIB is fast twitch glycolytic
(Ethier, 2007). These three types of muscle fibres
have very different functional characteristics. Type I
fibre is characterized by low force, power, speed
production and high endurance. Type IIB has high
force, power, speed production and low endurance,
while type IIA indicates feature between the two
other types. The MU consists of a single motoneuron
and the group of muscle fibre it innervates. All
muscle fibres in a single MU contain the same muscle
fibre type. Three types of MUs (slow, fast fatigue-
resistant, and fast fatigable) are categorized on the
basis of their twitch speed and fatigability. The slow
twitch MU is small and can produce less force
compare to fast twitch MU. Type I muscle MUs
contract slower, and they reach to peak power slower
and highly resistant to fatigue compare to type II fast
twitch MUs. Type IIA and IIB are capable of the same
amount of peak force, however type IIA fibres take
longer to reach their peak power compare to type IIB.
Therefore, the total peak power by type IIB is higher
than type IIA. In other words, type I has low intensity,
lower frequency, and low power compare to type II.
The average ratio (ascending over descending phase)
power for GMax, TFL, and RF muscles, as indicated
in Table 4, presents the power of RF muscle is higher
in OR group than CON group. The faster motor units
generate higher frequencies in their power spectra
(Wakeling, 2004). Thus, the larger numbers of MUs
are recruitment in RF muscle for OR than CON.
Therefore, type II (fast twitch) MUs are active in OR
group, while in CON group type I (slow twitch) MUs
are used.
5 CONCLUSION & FUTURE
WORK
The algorithm developed in this study aimed to
automatically discriminate CON and OR groups
based on sEMG recorded from three hip muscles
(GMax, TFL, and RF) during dynamic contraction.
The program was capable of identifying OR from
healthy people by analysing the activity of hip
muscles.
The novel method developed in this study was used
to analyse EMG signals recorded from GMax, TFL,
and RF muscles for 16 CON and 14 OR subjects
during squat cycles. In this study the DWT was
selected and various wavelet functions from this WT
method were applied to EMG signals from three
muscles in order to select the best possible energy
localization in the time-frequency plane. This
analysis showed the Bior3.9 wavelet function
provided higher amount of energy for most of our
subjects. By selecting Bior3.9 the wavelet power
spectrum was computed for CON and OR during 5
levels of decomposition. The result indicated the RF
muscle (muscle 3) is more active in the ascending
phase than the descending phase for OR, while it is
more active in descending phase for CON.
BIOSIGNALS 2017 - 10th International Conference on Bio-inspired Systems and Signal Processing
220
During dynamic contraction there is a progressive
recruitment of faster MUs in OR group during
ascending phase. EMG activity at higher frequencies
correlated with higher contractile force, and with the
progressively faster types of MU, which can be
assumed to be recruited. Thus, during dynamic
contraction the higher wavelet power in RF muscle of
OR group demonstrates that faster MUs were active,
while lower power in CON group related to the fact
that slower MUs were active.
This research work showed that the proposed
algorithm can find a good solution for pre-screening
problem. Nevertheless, some more improvements
could be achieved. In this algorithm, only the three
muscles of hip area were considered, this might be
different if we consider all the muscles in the hip area.
Another limitation is that the physical activity and age
parameters of subjects were not available. Therefore,
research should be conducted in a wider range,
parameters like physical activity, age, and gender
could be considered.
Some future work from this thesis may consist of
considering a larger sample size for more accurate
and reliable values. The goal can be developing a
software application, which can assist doctors and
physicians to diagnose FAI faster and easier.
ACKNOWLEDGEMENTS
The work in this paper was funded and supported by
the Canadian NSERC Collaborative Health Research
Project.
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