Platform for Multimodal Signal Acquisition for the Control of Lower

Limb Rehabilitation Devices

Douglas Ruy Soprani S. Araujo

1, 2

, Thomaz Rodrigues Botelho

1, 2

, Camila Rodrigues C. Carvalho

Anselmo Frizera

, Andre Ferreira

and Eduardo Rocon

Department of Electrotechnology, Federal Institute of Education, Science and Technology of Esp

ırito Santo,

ao Mateus, ES, Brazil

Department of Electrical Engineering, Federal University of Esp

ırito Santo, Vitoria, ES, Brazil

Bioengineering Group, Consejo Superior de Investigaciones Cientiﬁcas, CSIC, Arganda del Rey, Spain

Keywords:

Electroencephalographic Signals, Electromyographic Signals, Inertial Sensors, Multimodal, Robotic Rehabil-

itation.

Abstract:

Patients with some sort of motor disability may beneﬁt from robotic rehabilitation since it can provide more

control, accuracy and variety of training modes. This enhances the efﬁciency of the rehabilitation and, there-

fore, the recovery of the patient. Assistive devices, like exoskeletons or orthoses, can make use of physiological

data, such as electromyography (EMG) and electroencephalography (EEG), in order to detect the movement

intention. Combination of data can potentially improve the adaptability of assistive devices with respect to the

individual demands. Different methods can be applied depending on the neuromuscular disorder, therapy or

assistive device. In this work, we present a multimodal interface which integrates EEG, EMG and inertial sen-

sors (IMU) signals. Experiments were conducted with healthy subjects performing lower limb motor tasks.

The aim of the proposed system is to analyze the movement intention (EEG signal), the muscle activation

(EMG signal) and the limb motion onset (IMU signal). An experimental protocol is proposed. The results ob-

tained showed that the system is capable to acquire and process the biological signals synchronously. Results

indicated that the system is able to identify the movement intention, based on the EEG signal, the movement

anticipation, based on the muscle activation, and the limb motion onset.

1 INTRODUCTION

The number of individuals with some sort of lower

limbs disability due to several reasons as stroke and

spinal cord injuries is increasing (Tsukahara et al.,

2009). Such disabilities can also lead to secondary

problems, like wheelchair dependence, osteoporosis

and bedsores. Therefore, these disorders are likely

to decrease the quality of life. In order to recover or

enhance the lower limbs functions, rehabilitation pro-

grams are the most used treatment (Ju et al., 2005).

Robots have been introduced in rehabilitation as

a potential tool to implement physical therapies since

they can assist therapists performing repetitive move-

ments (Den

eve et al., 2008).

Exoskeletons or orthoses are assistive devices of-

ten used in robotic rehabilitation. Besides all the chal-

lenges brought from the structural design and con-

struction, the assistive devices should support self-

initiated movements for intuitive interaction. This is

desired in systems controlling neuroprosthetic or neu-

rorobotic devices that aim to assist patients with mo-

tor disabilities as naturally as possible, i.e. reducing

the impact of the assistive technology (Ib

nez et al.,

2013). This can be achieved by adapting the con-

trol of the device with respect to the patient’s inten-

tion (Kirchner et al., 2014). Those devices can make

use of physiological data in order to detect or predict

limb movement. The combination of such data can be

used to improve the reliability of assistive rehabilita-

tion robotic systems.

The integration of physiological data-based recog-

nition into the control of an assistive technical device

has a great advantage which is the earliness of pre-

diction (Muralidharan et al., 2011). A prediction of

movement onset that was based on EEG analysis can,

for example, be conﬁrmed by the detection of mus-

cle activity and corroborated by measuring the limb

Ruy Soprani S. Araujo D., Rodrigues Botelho T., Rodrigues C. Carvalho C., Frizera A., Ferreira A. and Rocon E..

Platform for Multimodal Signal Acquisition for the Control of Lower Limb Rehabilitation Devices.

DOI: 10.5220/0005138900490055

In Proceedings of the 2nd International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX-2014), pages 49-55

ISBN: 978-989-758-056-7

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

motion onset. Thus, a multimodal system provides

not only the intention to move but also shows the ac-

tual execution of the movement and the position of the

lower limbs during the rehabilitation. Thus, it is pos-

sible to characterize a volitional movement from its

planning to its execution (Gallego et al., 2012). This

approach was implemented in different studies.

Rocon et al. proposed functional compensation of

upper limb tremors with a soft wearable robot using

EEG, EMG and IMU analysis (Rocon et al., 2010).

Ibanez et al. proposed an adaptive and asynchronous

EEG-based system for online detection of the inten-

tion to move in patients with tremor (Ib

nez et al.,

2013). Kirchner et al. showed that the combination

of EEG and EMG can potentially be used for move-

ment prediction and improve the adaptability of as-

sistive technical devices through an ofﬂine analysis

(Kirchner et al., 2014).

In this study it is proposed a multimodal platform

that enables the synchronization and the analysis of

electroencephalographic (EEG) signal, electromyo-

graphic (EMG) signal and Inertial Measurement Unit

(IMU) signal. The aim is to analyze the movement in-

tention, the muscle activation and the effective move-

ment of lower limbs through EEG, EMG and IMU

signals, respectively.

This paper will have the focus on the architecture

of the synchronization, and implementation of an of-

ﬂine analysis, of EEG, EMG and IMU data, acquired

in a lower limb movement task, in order to identify

the activation steps. An experimental protocol, which

involves sensors placement, is proposed for the tasks

performed by healthy subjects. The platform can be

used in the development of interfaces for rehabilita-

tion robotics devices aiming at adapting their control

with respect to the patient’s intention.

2 MATERIALS AND METHODS

The equipment used to acquire the EEG and EMG

data was the BrainNet



BNT-36 (Lynx Eletronica,

Brazil). It is based on the requirements of general

safety for medical electrical equipment and has the

approval of the entities responsible for regulation of

medical products.

EEG signal was recorded using a international 10-

20 system cap. The positions used were Fp1, Fp2, F7,

F3, Fz, F4, F8, C3, Cz, C4 and Pz.

A bipolar channel was used to acquire a surface

EMG (sEMG) signal. Passive Ag/AgCl gel electrodes

were positioned on the right subject’s thigh, accord-

ing to the international protocol SENIAM (Hermens

et al., 1999) at the rectus femoris muscle, as it can be

seen in Figure 2.

Tech MCS



(Technaid, Spain) was used to ac-

quire the inertial signals. This system provides real

time spatial orientation for each sensor. Two IMUs

were used in the experiments.

2.1 Control Architecture for Data

Acquisition

A C#-based software was developed for the integra-

tion of both commercial systems described above. It

uses the UDP communication protocol to send and

receive data packages from BrainNet



BNT-36. A

library, provided by the manufacturer of the Tech-

naid system to capture data through the USB com-

puter port was also used. The data were synchronized

and saved in a computer and processed ofﬂine. The

data acquisition of the software developed was vali-

dated using the calibration sinusoidal signal provided

by Brainet



BNT-36 and also by controlled experi-

ments doing rotations of the IMUs. A representation

of the system developed can be seen in Figure 1.

Figure 1: Representation of the platform proposed.

Figure 1 represents the proposed platform, as it

is can be seen, the software proposed integrates the

EEG, EMG signals acquired using the BrainNet



BNT-36 with the IMU signal acquired using the Tech

MCS



system. All data were synchronized and saved

in a computer for a ofﬂine processing, as illustrated.

The sampling frequency of the BrainNet



BNT-

36 was set to 600Hz, the maximum possible value.

The sampling frequency of the Technaid system was

set to 50Hz, compatible with the leg movement per-

formed in the experiments. The data processing is

made by MATLAB



-based software.

2.2 Experimental Protocol

Three female and one male, healthy and right-handed,

subjects with ages between 22 and 24 performed the

experiments. The proposed experiment was devel-

oped according to the protocol approved by Research

NEUROTECHNIX2014-InternationalCongressonNeurotechnology,ElectronicsandInformatics

Ethics Committee of Federal University of Esp

ırito

Santo Health Sciences Center (Project n. 214/10).

During the experimental session, subjects were

comfortably seated with hands resting on the legs and

with feet suspended, without touching the ground.

The angle between thigh and shank was assumed to

be 90

◦

. The calibration of the inertial system occurs

in this step. An acoustic signal indicates the end of

the calibration and the beginning of the experiment.

After about 10 seconds, another acoustic signal indi-

cates that the subject is allowed to perform the exten-

sion and ﬂexion of the knee using both legs, from rest

position up to maximum extension. The Figure 2 il-

lustrates a subject during the experiment wearing all

sensors.

Figure 2: User wearing the complete set of sensors during

an experimental session.

The inertial sensors were positioned on the right

thigh and shank of the subject. The placement pro-

tocol doesn’t require speciﬁc placement or any align-

ment between the sensors because the alignment was

done virtually during the data processing. The only

recommendation was to place the sensors on the ex-

ternal part of the leg, as it can be seen in Figures 2

and 4. The movement was assumed to be only in the

shank.

The protocol selected for the experiments deﬁnes

a self-chosen moment after hearing the acoustic indi-

cation to start the movement. The subject thinks and

immediately executes the task. The examiner asks to

the subject to perform the movement slowly and to

keep the eyes open, avoid blinking and swallowing.

Each experiment was composed by 30 trials of exten-

sion and ﬂexion movement and one trial lasts approx-

imately 30 seconds. A graphical representation of one

Figure 3: Graphical representation of one trial.

trial can be seen in Figure 3.

Figure 3 illustrates one trial of the experiments

performed. As it can be seen, there is a subject-

dependent period of time before the voluntary move-

ment, after the second acoustic signal.

3 SIGNAL PROCESSING

The signal processing was made of two different

ways. To EEG signal, a multi-trial analysis was per-

formed while the EMG and IMU signals analysis

were a single-trial. Making not possible the presen-

tation of these three signals together.

3.1 EEG Signal Processing

EEG signal was used to estimate the movement in-

tention of the subject. In order to achieve this esti-

mative, the Event Related Desynchronization (ERD)

characteristic was evaluated by the classical method

described by (Pfurtscheller and Lopes da Silva, 1999).

The signals were ﬁltered at µ and β bands by But-

terworth 4th order bandpass ﬁlters and the energy was

obtained.

The EEG signals were then analyzed under a per-

centage scale based on a baseline, represented in Fig-

ure 3. The baseline was evaluated between the 5th and

the 8th seconds of the experiment, while the subject

was resting before the second acoustic signal.

3.2 EMG Signal Processing

EMG signal was used to estimate the muscle activa-

tion of the subject during the experiment. For each

trial, the data was preprocessed with a variance ﬁl-

ter deﬁned by the Equation 1. In Equation 1, N is

the length of the window used for ﬁltering, x is the

EMG raw signal and v is the EMG preprocessed sig-

nal. This processing was chosen for preserving the

initial muscle activation moment and for providing

enough differentiation between the rest and contrac-

tion positions, as described by (Kirchner et al., 2014;

Tabie and Kirchner, 2013).

An adaptive threshold classiﬁer, as described by

the Equation 2, based on the mean and standard de-

viation, obtained in the baseline evaluates the initial

PlatformforMultimodalSignalAcquisitionfortheControlofLowerLimbRehabilitationDevices

muscle activation. The signals were processed on

windows with a length of 10 samples with 90% of

overlapping.

v(t) =

N − 1

∑

i=0

[x(t − i)]

−

N − 1

∑

i=0

x(t − i)

(1)

T (t) = m

(t) + pd(t)

(2)

In Equation 2, T is the threshold, m is the mean

value, d is the standard deviation and p the sensitiv-

ity factor of the threshold. N represents the length

of the window used to obtain the mean and the stan-

dard deviation. The moment of muscle activation is

deﬁned when the ﬁrst window, between a predeter-

mined number of consecutive windows, exceeds the

threshold. For each subject a leave-one-out cross val-

idation analysis of the data was performed and for the

parameters optimization a grid search was used.

3.3 IMU Signal Processing

IMU signal was used to detect the movement onset,

i.e. the beginning of the limb displacement, and to

measure the angles of the knee joint. The three clin-

ical knee rotations occur: ﬂexion/extension, abduc-

tion/adduction and internal/external rotation (Favre

et al., 2009). The thigh and shank can be represented

by links and the clinical rotations by β, γ and α, re-

spectively, as illustrated in Figure 4.

Figure 4: Inertial sensors placement, link-segment repre-

sentation of the leg and clinical rotations.

The angles measured between the thigh and the

shank were the relative angles between the sensors.

In order to measure these angles, the world reference

frame of the shank sensor was transformed to refer-

ence frame of the thigh sensor.

The sensors were supposed to be aligned in x and

z axes and to have an angle equal to 90

◦

between the

y axis when the calibration of the inertial system oc-

curs, as described in the experimental protocol and

illustrated in Figure 5.

Figure 5: Representation of the virtual alignment between

the inertial sensors.

A virtual alignment between the sensors was done,

achieving this conﬁguration, in order to provide a

placement protocol regardless of sensors position.

Figure 5 shows the real shank IMU represented by

(2), the virtual shank IMU represented by (3) and the

thigh IMU is represented by (1).

The data from the IMUs were analyzed using a

threshold in order to determine the start of effective

movement. The data were also converted into Euler

angles in order represent the angles of the knee joint

movement along the tasks.

4 RESULTS AND DISCUSSION

In Figure 6, the relative energy of µ and β bands of

the EEG signal is shown for the subject two. The ﬁrst

ﬁve seconds were not shown because they represent

a signal stabilization and should not be taken into ac-

count. The last ﬁve seconds were not shown because

the trials were synchronized to the ﬁrst moment of

movement along the trials, thus some samples had to

be discarded in the end of each trial. The ERD char-

acteristic can be seen in all channels, mainly in the

Cz, C3 and C4, which are positioned on the motor

cortex. In these channels the relative energy reaches

a decrease of approximately 80% in the µ band. The

ERDs are seen, approximately, from the 10th second

on. This is consistent with the movements performed

in the experiments.

In Figure 7, one trial of EMG, preprocessed with a

variance ﬁlter, of the subject two is shown. The time

scale is the same of Figure 6. An initial muscle acti-

vation can be seen in the 11th second, approximately.

The end of the muscle activation occurs in the 13th

second, approximately. This is consistent with the

time that the subject perform the extension and ﬂex-

ion of the knee.

In Figure 8, the Euler angles related to the IMUs

signals for the same subject are shown. The angles

NEUROTECHNIX2014-InternationalCongressonNeurotechnology,ElectronicsandInformatics

10 20

−100

100

Fp1

10 20

−100

100

Fp2

10 20

−100

100

10 20

−100

100

10 20

−100

100

10 20

−100

100

10 20

−100

100

10 20

−100

100

10 20

−100

100

10 20

−100

100

10 20

−100

100

Figure 6: Relative energy of µ (black) and β (gray) bands,

in percent scale vs. time in seconds, obtained by an average

of 30 trials.

5 10 15 20 25

−2

x 10

EMG signal

Time (s)

A.U.

Figure 7: One trial of EMG signal preprocessed with a vari-

ance ﬁlter.

are related to the thigh sensor in calibration position.

In this ﬁgure, above, are shown the thigh sensor an-

gles. As it can be seen, only a small displacement oc-

curs. Thus, the movement can be considered only in

the shank. Below, the shank sensor angles are shown.

As it can be seen, mainly displacement occurs in Yaw,

which is related to the extension and ﬂexion. The ini-

tial value was 90

◦

, as considered in the experimen-

tal protocol and obtained with the virtual alignment.

The ﬁnal value depends on the subject’s extension. A

small displacement occurs in Roll and Pitch, which is

consistent with the clinical knee rotations. As it can

be seen, the initial limb displacement occurs, approx-

imately, in the 11th second.

In Figure 9, the distribution of prediction times

for EMG-based movement prediction for the subject

three is shown. Time point zero corresponds to the

physical movement onset, represented by the red line.

The dash-dot green line at time −0.5s indicates the

range up to where predictions based on EMG were

allowed. The movement onset up to the line at time

−0.5s was considered the EMG-window. The dashed

blue line represents the mean of EMG-based time pre-

dictions, which in this case was 72.3ms before the

0 10 20

−150

−100

−50

100

150

(a) Roll

Time (s)

Angle (Degrees)

0 10 20

−150

−100

−50

100

150

(b) Pitch

Time (s)

Angle (Degrees)

0 10 20

−150

−100

−50

100

150

Time (s)

Angle (Degrees)

0 10 20

−150

−100

−50

100

150

(d) Roll

Time (s)

Angle (Degrees)

0 10 20

−150

−100

−50

100

150

(e) Pitch

Time (s)

Angle (Degrees)

0 10 20

−150

−100

−50

100

150

(f) Yaw

Time (s)

Angle (Degrees)

Figure 8: One trial of IMU signals from both sensors, show-

ing Roll (a), Pitch (b) and Yaw (c) for the thigh sensor, and

Roll (d), Pitch (e) and Yaw (f) for the shank sensor.

−0.5 −0.0723 0

Time (s)

Trials

Time of EMG detection

Time of IMU detection

Average of correct detected EMG

Beginning of EMG window

Figure 9: Distribution of prediction times for EMG-based

movement prediction for the subject three.

movement onset. Only the classiﬁcations in the EMG

window are shown.

The classiﬁcation results are summarized in Table

1. In Table 1 it is shown the accuracy of the EMG

classiﬁer and the mean time of initial activation, tak-

ing into account only classiﬁcations inside the EMG-

window with the standard deviation. The accuracy is

deﬁned by the Equation (3).

Ac(%) =

EMGW

.100

(3)

Where Ac is the accuracy of the system, N

EMGW

is the number of trials in which the EMG is classi-

ﬁed in the EMG window and N

is the total number

of trials per experiment. As it can be seen, the ac-

curacy range was 46.67% up to 93.33%. The system

can classify the activation before the EMG window,

inside the EMG window, after the movement onset or

can not identify the activation. Thus, for the subject

PlatformforMultimodalSignalAcquisitionfortheControlofLowerLimbRehabilitationDevices

Table 1: Classiﬁcation results of EMG-based prediction for

all subjects. The accuracy of the classiﬁcation and the mean

time of EMG-based prediction inside the EMG-window

with the standard deviation.

Subject Accuracy (%) Time (ms)

1 70.00 −91.60 ± 91.30

2 46.67 −90.90 ± 130.70

3 93.33 −72.30 ± 46.20

4 57.67 −52.25 ± 51.40

Average 66.92 −76.74 ± 79.90

one and, mainly, for the subject three, is reached a

high level of accuracy. According to (Kirchner et al.,

2014), activations before the EMG-window were con-

sidered false positives. Taking into account all the

classes of classiﬁcation, regardless of whether the de-

tection anticipates or not the movement onset or the

detection is classiﬁed before the EMG-window, 100%

of the trials were detected by the EMG classiﬁer for

all subjects.

The difference of accuracy between the subjects is

directly related to the EMG time of activation, since

it deﬁnes the position of the EMG classiﬁcation be-

tween the classes. The EMG time of activation de-

pends on many factors and vary between subjects and

trials, as it can be seen in Figure 9 and according to

the results achieved by (Kirchner et al., 2014).

The execution of the movement depends on many

motor unit-related parameters, e.g. twitch force, con-

tractile speed, axonal conduction velocity, fatigue re-

sistance, among others (Pons, 2008). Furthermore,

the surface electromyographic also depends on the

skin-electrode coupling which contributes to differ-

ent signals between subjects. Based on the study of

these characteristics, the length of the EMG-window

can be adapted for each subject. This can decrease the

number of wrong classiﬁcations of false positives. For

subjects whose accuracy is lower, the movement pre-

diction can be complemented by an EEG-based pre-

diction.

Depending on the type of neuromuscular disorder

and state of therapy, both, single EEG or single EMG-

based movement prediction, might no longer result in

good performance. In this case, the combination of

multimodal data should be even more relevant (Kirch-

ner et al., 2014).

5 CONCLUSIONS

A multimodal platform for lower limb studies in re-

habilitation based on EEG, EMG and IMU signals

was developed. An experimental protocol, applied

in healthy subjects to acquire data from the platform,

was also proposed. An ofﬂine analysis of the data

generated by this system in a lower limb movement

task was showed.

The analysis estimates the subject’s movement in-

tention evaluating the ERD characteristic in the EEG

signal and the muscle activation in the EMG signal.

The beginning of the movement was evaluated by

IMUs data, which also measure the angles between

the thigh and the shank of the subject during the tasks

performed.

The accuracy and the mean time showed the fea-

sibility of the interface proposed. Under a qualita-

tive analysis and based on the Figures 7, 8 and 9, the

system proposed was capable to acquire, synchronize

and process the combined signals. Thus, the platform

proposed can be used in the study and development of

multimodal interfaces. Prosthesis or orthoses whose

purpose is motor rehabilitation, adapting the control

of devices with respect to the patient’s intention, can

be developed with the platform proposed.

Results presented here were achieved in an ofﬂine

processing. In future work, a single trial algorithm

whose purpose is to analyze the EEG signal in order

to ﬁnd MRPs will be applied. A platform which uses

online detection of EEG and EMG data will be ap-

plied to an exoskeleton, which is currently under de-

velopment.

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