A Novel Automated Algorithm for Computing Lumbar Flexion Test

Ratios Enhancing Athletes Objective Assessment of Low Back Pain

Francisco Carrillo-Perez

, Ignacio Diaz-Reyes

, Miguel Damas

, Oresti Banos

Victor Manuel Soto-Hermoso

and Alejandro Molina-Molina

Department of Computer Architecture and Computer Technology, E.T.S.I.I.T., Universidad de Granada,

Calle Periodista Daniel Saucedo Aranda, Granada, Spain

Department of Sport and Physical Education, Universidad de Granada, Granada, Spain

Keywords:

sEMG, LBP, Algorithm, FRP, Muscle Activity, LFT.

Abstract:

INTRODUCTION: Low Back Pain is a common muscular disorder that most adults would experience over

their lives. In healthy patients at the end of a lumbar ﬂexion occur a phenomenon called the Flexion Relaxation

Phenomenon. Ratios between different phases of lumbar ﬂexion can help diagnosing LBP patients. The aim

of this work is to create an automated algorithm to compute this ratios helping to discriminate between healthy

and LBP athletes. MATERIALS AND METHODS: 21 participants were recruited: 10 LBP and 11 healthy.

Participants were tested with Lumbar Flexion Test for evaluating LBP. sEMG were recorded in low back

muscles. RESULTS: Athletes diagnosed with LBP showed ratios lower than 1.5. For healthy participants we

observed results greater than 1.5. DISCUSSION AND CONCLUSION: Our results are contained between

rates found in literature. Our algorithm can help the diagnose of athletes with a non-intrusive method and with

little knowledge of sEMG analysis.

1 INTRODUCTION

Low Back Pain (LBP) is a common disorder invol-

ving the muscles, the bones and the nerves of the

low back. About eighty percent of adults experience

low back pain at some point in their lifetimes. It

is the most common cause of job-related disability

and a leading contributor to missed work days (NIH,

2017). A commonly used test for diagnosing LBP is

the Lumbar Flexion Test (LFT). LFT helps diagno-

sing LBP patients because of the phenomenon that

occurs toward the end of the lumbar spine ﬂexion,

named Flexion-Relaxation Phenomenon (FRP) using

surface electromyography (sEMG) (Colloca and Hin-

richs, 2005)(Tabard et al., 2018)(Desai and Bisen,

2017). Toward the end of the lumbar spine ﬂexion the

sEMG activity should be near to zero, producing what

is called myoelectric silence in healthy patients. Ho-

wever this not occur in LBP patients where the sEMG

activity maintains similar values as when performing

the ﬂexion.

LBP is of prevalence in athletes whom perform

high intense sport activity, as well as other types back

abnormalities. (Kujala et al., 1992)(Schmidt et al.,

2014). An objective evaluation of LBP and an ob-

jective tool for recovery measurement is of crucial im-

portance to improve athletes muscle’s health.

The aim of the study was to develop an automa-

tic detection algorithm for computing Lumbar Flex-

ion Test ratios from sEMG and accelerometer signals

to discriminate between those who suffer from LBP

and healthy athletes.

2 MATERIALS AND METHODS

2.1 Materials

We used a sEMG and inertial sensor (Shimmer Sen-

sing, Dublin, Ireland). Data were obtained and pro-

cessed using mDurance (Banos et al., 2015), a soft-

ware for sEMG analysis (MDURANCE SOLUTI-

ONS SL, Granada, Spain).

Recording of raw EMG signals took place at a fre-

quency of 1024Hz, via two channels. IMU signals

were recorded at the same frequency. The raw EMG

signal was band-pass ﬁltered (cutoff frequencies, 20

Hz, high pass; 400 Hz, low pass).

Carrillo-Perez, F., Diaz-Reyes, I., Damas, M., Banos, O., Soto-Hermoso, V. and Molina-Molina, A.

A Novel Automated Algorithm for Computing Lumbar Flexion Test Ratios Enhancing Athletes Objective Assessment of Low Back Pain.

DOI: 10.5220/0006922600340039

In Proceedings of the 6th International Congress on Sport Sciences Research and Technology Support (icSPORTS 2018), pages 34-39

ISBN: 978-989-758-325-4

2.2 Protocol

21 participants were recruited: 10 LBP and 11 he-

althy. Participants were tested with LFT for evalua-

ting LBP. Characteristics of participants can be obser-

ved in 1

Table 1: Mean and SD of athletes’ characteristics.

Height Weight

LBP Athletes 1.67 ± 0.054 64.75 ± 7.17

Healthy Athletes 1.736 ± 0.11 70.30 ± 7.16

Age IMC

LBP Athletes 37.33 ± 9.05 22.04 ± 1.92

Healthy Athletes 26.70 ± 6.59 23.436 ± 2.60

Electrodes were placed at the Erector Spinae (lon-

gissimus) both left and right, following the guidelines

for electrode placement of the Surface ElectroMyo-

Graphy for the Non-Invasive Assessment of Muscle

project (SENIAM), as presented in Figure 1.

sEMG sensor was placed on the back, as presented

in Figure 2.

Participants were asked to perform the FLT. The

FLT consists of the following phases. The participant

starts from a vertical position. This is named as Verti-

cal phase. He is asked to perform a lumbar spine ﬂex-

ion, trying to touch his toes. This is named as Flexion

phase. He is asked to maintain this lumbar spine ﬂex-

ion position between three and ﬁve seconds. This is

named as Maximum Voluntary Flexion (MVF) phase.

He is asked to return to a vertical position. This is

names as Extension phase. A visual representation of

FLT can be observed in Figures 3 and 4 .

Figure 1: Electrodes placement in the Erector Spinae (lon-

gissimus).

2.3 Automatic Measurement of Posture

The measurement of the posture is crucial in order

to run the algorithm. The IMU sensor combines tri-

axial accelerometer, gyroscopes and magnetometers,

enabling the measurement of the absolute attitudes or

Figure 2: sEMG sensor positioning in the back of the

athlete.

Figure 3: Vertical phase of LFT.

Figure 4: MVF phase of LFT.

inclinations of the body part to which the sensor is fas-

tened. IMU technology has been exploited during re-

cent years for body movement analysis. (Banos et al.,

2012)(Banos et al., 2013) (Banos et al., 2014) (Man-

nini et al., 2013) (Lin et al., 2015)

IMUs provide raw acceleration, magnetic ﬁeld

data and angular rate that need to be fused together to

obtain a sole, optimal estimate of orientation. Diffe-

rent algorithms have been proposed in the literature to

that end, including Kalman ﬁlters (Roetenberg et al.,

2005), least squares ﬁlters (Luinge et al., 2007) or

Gaussian particle ﬁlters (Zhang et al., 2010).

In our work, we have used Madgwicks algorithm

(Madgwick et al., 2011), which outperforms most ex-

isting approaches in terms of implementation com-

plexity, sampling rate requirements and computatio-

nal needs. This technique does not suffer from the

well-known limitations of other solutions, like the

singularity problem associated with the Euler angle

representation (gimbal lock). Besides, this method

A Novel Automated Algorithm for Computing Lumbar Flexion Test Ratios Enhancing Athletes Objective Assessment of Low Back Pain

also omits the use of computationally expensive tri-

gonometric functions, making it more efﬁcient and

easier to implement for real-time purposes. Madg-

wicks algorithm employs acceleration, angular rate

and magnetic ﬁeld measurements to analytically de-

rive, through an optimized gradient-descent method, a

quaternion representation of motion (Harrison, 1999).

Thus, the output of the algorithm is a quaternion, a

compact vector in the form (q1,q2,q3,q4), which dy-

namically represents the orientation of the sensor.

Quaternions are frequently used in orientation es-

timation algorithms because of their numerical sta-

bility and computational efﬁciency. However, this

representation is difﬁcult to interpret and visualize,

since it deﬁnes a R

space that cannot be represented

in a human-understandable three-dimensional view.

Accordingly, a translation into Euler angles is perfor-

med here, after all of the calculations to estimate the

quaternion are carried out. Euler angles represent the

possible rotations around the three cardinal axes, na-

mely yaw (ϕ), for the X axis, pitch (θ), for the Y axis,

and roll (φ), for the Z axis. Given the estimated qua-

ternion, the Euler angles can be simply obtained as

follows:

ϕ = arctan



2(q

− q

)

1 − 2(q

+ q

)



(1)

θ = arcsin(2(q

− q

)) (2)

φ = arctan



2(q

− q

)

1 − 2(q

+ q

)



(3)

2.4 Automatic Detection of Lumbar

Flexion Test Phases and Angle

Variation

2.4.1 Automatic Detection of Local Maximums

and Minimums

After we have obtained the measurement of posture

from the IMUs signal, a Savitzky-Golay ﬁlter (Savit-

zky and Golay, 1964) is applied to the values compu-

ted, in order to obtain a smoothed signal. In smoo-

thing, the data points of a signal are modiﬁed so that

individual points that are higher than the immedia-

tely adjacent points (presumably because of noise) are

reduced, and points that are lower than the adjacent

points are increased. This naturally leads to a smoot-

her signal (and a slower step response to signal chan-

ges). As long as the true underlying signal is actually

smooth, then the true signal will not be much distorted

by smoothing, but the high frequency noise will be re-

duced. Compared to other smooths of the same width,

the Savitzky-Golay smooth is less effective at redu-

cing noise, but more effective at retaining the shape

of the original signal.

Local maximums and minimums of signal data are

obtained using the ﬁrst and second derivative. The

value of the slope at a local maximum or minimum

would be equal to 0. The value of the slope is com-

puted using the ﬁrst derivate. The second derivative

is used in order to label the point as a maximum or a

minimum depending on the sign of the second deriva-

tive at the point where the slope is 0. If the value is

less than 0 it is a local maximum. If it is greater than

0 it is a local minimum.

2.4.2 Automatic Creation of Lumbar Flexion

Test Phases based on Local Maximums

and Minimums

The algorithm automatically create phases based on

the list of local maximums and minimums. It tests

if appear ﬁrst a maximum or minimum in IMUs sig-

nal. Depending on the result, it would start conca-

tenating maximums and minimums points, until there

are n number of maximum and minimum pairs, repre-

senting n different phases.

Only phases where the angle variation is signiﬁ-

cant are preferred. For making the algorithm robust

when facing little angle variations, a threshold is app-

lied in order to discard phases where this variation is

not signiﬁcant. This threshold is computed using the

three greater angle variations, represented as a

where

> a

i+1

> a

i+2

, in the phases found as follows:

threshold =

+ a

(4)

This posture’s angle variation is also used to la-

bel the phases found by the algorithm. In a Flexion

phase, the posture’s angle variation is negative while

in an Extension phase it is positive. Maximum Volun-

tary Flexion (MVF) and Vertical phases are between

Flexion and Extension phases. If the phase is between

a Flexion and Extension phases then it is labeled as

MVF. On the other hand, if it is between an Extension

and Flexion phases, it is labeled as a Vertical phase.

The pseudocode for algorithm’s steps described in

subsections 2.4.1 and 2.4.2 is as follows:

obtain_smoothed_posture_signal(posture_signal)

get_indexes_local_maximums(smoothed signal)

get_indexes_local_minimums(smoothed signal)

if local_maximums[0] < local_minimums[0]:

for max_i,min_i in maximums,minimums:

create_new_phase(max_i,min_i)

compute_local_variation(new_phase)

if angle variation < 0:

label_phase_as_flexion(new_phase)

icSPORTS 2018 - 6th International Congress on Sport Sciences Research and Technology Support

else:

label_phase_as_extension(new_phase)

previous_phase_vector.add(new_phase)

else:

for max_i,min_i in maximums,minimums:

create_new_phase(min_i,max_i)

compute_local_variation(new_phase)

if angle variation < 0:

label_phase_as_flexion(new_phase)

else:

label_phase_as_extension(new_phase)

previous_phase_vector.add(new_phase)

compute_threshold(previous_phases_vector)

for phase in previous_phases_vector:

if phase[angle variation] > threshold:

final_phases_vector.add(phase)

add_MVF_and_vertical_phases(final_phases_vector)

2.5 Computation of Ratios between

Phases

During the execution of the LFT test, the muscles

are normally subject to an important level of activity

and stress. To monitor the electrical activity produ-

ced by the skeletal muscles a wearable electromyo-

graphy or EMG sensor is used. This sensor consists

of a set of surface electrodes, which are attached to

the skin of the body part to be monitored. The elec-

trodes measure the potential difference between them,

which is translated by the sensor into EMG signals.

There exist some well-known metrics that help cate-

gorize the level of the muscle fatigue. The root mean

square (RMS) and the maximum voluntary muscle

contraction (MVC) are generally used as indices of

muscle fatigue. (Al Zaman et al., 2007) (Kim et al.,

2007). RMS and MVC can be computed as follows:

RMS =

∑

k=1

EMG

(k)

(5)

MVC = max(EMG) (6)

RMS is computed for each phase found by the al-

gorithm and it is normalize by the maximum histo-

rical MVC of the patient. RMS is normalize by the

MVC as follows:

RMSnormalized =



RMS

MVC



∗ 100 (7)

RMS ratios between Flexion and Extension pha-

ses and MVF and Vertical phases have been proved

as good discriminators between healthy and LBP pa-

tients (Watson et al., 1997)(S

anchez-Zuriaga et al.,

2015)(Neblett et al., 2013). Ratios are computed bet-

ween phases. The mean and standard deviation from

all repetitions ratios is obtained.

The pseudocode for the computation of ratios be-

tween phases by the algorithm is as follows:

for phase in final_phases_vector:

get_RMS_normalized_by_MVC(phase)

compute_flexion_mvf_ratios(phases)

compute_extension_mvf_ratios(phases)

get_mean_flexion_mvf_ratios(flexion_mvf_ratios)

get_mean_extension_mvf_ratios(extension_mvf_ratios)

3 RESULTS

Flexion/MVF and Extesion/MVF are the main ratios

of interests for discriminate between healthy and LBP

athletes. In MVF phase is where the FRP occurs.

Therefore much greater ratios’ values must be obtai-

ned in healthy athletes than in LBP athletes.

Athletes diagnosed with LBP showed ratios lower

than 1.5. For healthy participants we observed results

greater than 1.5. Results are showed in Table 2.

Table 2: Mean and standard deviation of ratios between

phases in healthy and LBP athletes.

Flexion/MVF Extension/MVF

LBP Athletes 1.22 ± 0.33 1.58 ± 0.28

Healthy Athletes 3.07 ± 0.67 6.10 ± 0.96

4 DISCUSSION

Following the aim of developing a quantitative met-

hod for monitoring LBP athletes, we saw an evident

threshold to discriminate between athletes with LBP

and healthy.

Our results are enclosed in the same rates as those

found in literature, both for healthy and LBP partici-

pants. (Watson et al., 1997) , (Alschuler et al., 2009)

and (Neblett et al., 2013) showed similar results. A

comparison of our results with the one presented in

(Watson et al., 1997), (Alschuler et al., 2009) and

(Neblett et al., 2013) can be observed in Tables 3 and

4. Our results prove the validity of the algorithm com-

paring our results with those found in literature.

A Novel Automated Algorithm for Computing Lumbar Flexion Test Ratios Enhancing Athletes Objective Assessment of Low Back Pain

Table 3: Comparison of our results with those found in lite-

rature for Flexion/MVF ratios in healthy patients.

Flexion/MVF

Our results 3.07 ± 0.67

(Watson et al., 1997) 13.98 ± 11

(Neblett et al., 2013) 15.1 ± 7.7

Table 4: Comparison of our results with those found in lite-

rature for Flexion/MVF ratios in LBP patients.

Flexion/MVF

Our results 1.22 ± 0.33

(Watson et al., 1997) 2.72 ± 2.7

(Alschuler et al., 2009) 0.19 ± 0.47

5 CONCLUSION

Our algorithm helps automate the evaluation of the

athletes without any knowledge of sEMG nor of LFT

and our results agree with other studies. Our results

prove the validity of the algorithm for computing ra-

tios based on those presented in literature. This tool

would help sport specialists to evaluate their sports-

men and observe an objective progress over their re-

habilitation with little preprocessing that can be per-

formed with sEMG analysis software (Banos et al.,

2015).

Our algorithm could increase the objective asses-

sment during the sportsmen injury time as well as

a tool during injury free periods to evaluate if LBP

could be a plausible consequence of an overload trai-

ning. Automatically computed ratios can served as a

guide to perform changes in the recovery plan, decre-

asing injury’s time.

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A Novel Automated Algorithm for Computing Lumbar Flexion Test Ratios Enhancing Athletes Objective Assessment of Low Back Pain