Application of Myo Armband System to Control a Robot Interface
Gabriel Doretto Morais, Leonardo C. Neves, Andrey A. Masiero and Maria Claudia F. Castro
Centro Universit
ario da FEI, Av. Humberto Alencar Castelo Branco 3972, S
ao Bernardo do Campo, Brazil
Myoelectric Signal, PeopleBot, Human Machine Interface, Myo, Inertial Measurement Unit.
This paper discusses the application of myoelectric signals to control electronic devices aiming the develop-
ment of a digital controlling interface with Myo Gesture Control Armband System. Through this interface
it is possible to control the movement of a robot and its interaction with the environment, in this case the
robot being PeopleBot, a robot designed for home necessities. Thus, allowing an assessment on the opera-
tion of controlling devices with myoelectric signals and Inertial Measurement Unit (IMU), the advantages and
disadvantages of working with this technology are discussed.
Nowadays, it is impossible to imagine a world with-
out technology. The use of mobile devices such as
mobile phones, tablets, among others, has constantly
evolved in many fields of society such as health,
transportation, communication and security. How-
ever, with the increased integration between technol-
ogy and humans, studies of flexible and bendable
electronic equipment such as roll up displays and
wearable devices, have attracted the public’s atten-
tion (Gwon et al., 2011; Futurecom, 2015). Some
of these devices operate through myoelectric signals,
like Myo Gesture armband. Myo is a wearable device
that uses the concept to create a controlling platform
to other electronic devices with preset gestures.
The myoelectric signal is a biological signal pro-
duced by the electrical activity in a muscle during its
contraction, and it can be detected through electrodes
applied on skin. The use of these signals is very feasi-
ble for analysis of movements and controlling of some
electronic devices, since for each movement it has dif-
ferent modes of muscle activation, which are reflected
in different signatures or patterns for device control.
This technique could be use in the rehabilitation of
people with motor disorders, to control prostheses,
robotic devices, biomechanical and human machine
interfaces (HMI) (da Silva, 2010).
Myo system has been used as a control platform
for TedCube in (Caballero, 2015). According with
the author, into operating rooms, when doctor needs
to rotate a 3D image to see some exam detail, he has
to tell for some assistant do it. In that case, Myo can
help the doctor manipulate system’s interface and see
any detail touchless.
At current market, system for gesture recognition
exists on many different platforms. The most popular
between them is Microsoft Kinect which is in your
second version. This device works with two cameras
and a infrared sensor. This technology calls Time Of
Flight (TOF 3D tracking through lighting pulses)
which, recognizes gestures to execute some action on
software (Canaltech, 2014).
TedCube also uses Microsoft Kinect for gesture
recognition. However, there are many disadvantages
when it compares to Myo system. As TedCube needs
to use commands through gesture, the system focus
on Myo gestures make possible a simple approach
without barrier like Microsoft Kinect could be. Be-
yond that Myo system could offer a most precise
capture for short movements and less aggressive for
surgery center environment which, turns it safety (Ca-
ballero, 2014).
Another application for Myo system is to moni-
tor Parkinson disease development. Usually, doctors
use three different exams, Positron Emission Topog-
raphy (PET), Magnetic Resonance Imaging (MRI)
and DaTSCAN (exam to detect functional images of
the brain used on nuclear medicine). These exams
are expensive and have to be done on specialized im-
age centers. Tremedic, developed by (Song, 2014),
make the monitoring of patient in real time through
Myo device. Current electromyography data are com-
pared with old ones to identify disease symptoms.
It allows that treatment with levodopa (antiparkinson
medicine) could be analyzed for confirming or con-
testing the diagnostic.
Morais, G., Neves, L., Masiero, A. and Castro, M.
Application of Myo Armband System to Control a Robot Interface.
DOI: 10.5220/0005706302270231
In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 4: BIOSIGNALS, pages 227-231
ISBN: 978-989-758-170-0
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Comparing to others gestures recognition system,
Myo has some disadvantages to define the depth. De-
vice’s algorithm works with data acquired through
eight electromyographic sensors, divide on inertial
sensors and rotational sensors. But this information
cannot generate object reference in a 3D world like
Microsoft Kinect does. But it helps to work with an-
other devices as smart-phones, personal computers,
wearable gadgets, even robots (Bullok, 2015).
In this paper, we focus on the application of Myo
Armband in the development of a controlling inter-
face for PeopleBot robot. This kind of application
could be used in many ways like reaching bio-infected
or radioactive places or any place that would be harm-
ful to human beings. Also, another application is to
control a robot for helping elderly or disabled peo-
ple on tasks involving motion and drag and drop ob-
jects, for example. In that way, we chose PeopleBot
because its hardware is made to human-robot interac-
tion problems. It has wheels to move and a grip to get
objects on the top of a table.
To integrate Myo and PeopleBot, we use ROS
(Robot Operating System) which allows the connec-
tion of the commands interpreted by Myo and sent to
the robot controller. This study shows the advantages
and disadvantages of working with myoelectric con-
trolling devices and what improvements are need to
make it better as a HMI for helping people on daily
At start, it was necessary to study the general opera-
tion of Myo. Initially, we used the Myo Connect soft-
ware, developed by Thalmic labs, that deal with the
events of sensors and was used to control softwares
like media players, slide show presentations programs
and on-line games. Scripts were made in Lua pro-
gramming language as interface (Labs, 2015).
To identify movements through Myo software
needs a classifier to determine the best combination
of the eight existing sensors. To understand how it
works, figure 1 presents sensors raw data for move-
ments from wave-out to wave-in. On figure 1 has the
best combination of the sensors to classify the move-
ments. It can note on graphics the inverse relation-
ship between two sensors located on main muscles to
make the movement. At last graphic the electrodes
have a minimal influence for the movement. The
other graphics are too embracing and it cannot con-
clude anything relevant.
Aiming the application on the ROS platform, the
Figure 1: Relationship between the data electrodes to a
muscle activating for a movement.
use of the Myo Connect and programs developed in
Lua script were not sufficient to create communica-
tion between computer and robot. There are many
factors that influence on it like incompatibility be-
tween operating systems. Thus, the script should be
able to access directly raw information from sensors
(hexadecimal matrix information) to use it in such
manner to control the robot.
Methods to communicate the Myo platform, that
was developed for Windows and MacOS operational
systems, with the ROS, developed for Linux oper-
ational system which controls the robot should be
addressed. The communication between operational
systems, which require a network with at least two
computers was aborted due to its low flexibility and
possible delays in the communication. The alterna-
tive that was chosen receive data from the sensors of
Myo directly on the same platform of the robot.
For signal acquisition, the script developed by
Danny Zhu, under license from MIT (Dzhu, 2015)
was used. Raw data is read from the sensors of the
armband and allows its visualization and the manip-
ulation to be used for PeopleBot controlling (figure
Figure 2: PeopleBot (, 2015).
BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing
2.1 Publishing Myo’s Information
With raw data, a simple algorithm was written to get
then and publish to the robot with the action to be exe-
cuted. Figure 3 shows the system architecture. There
are four algorithms (or topics, as they are called on
ROS) to communicate with the robot:
The topic arm-myo returns data from the sensors,
received as integer numbers of eight bits; to keep
the algorithm functional independent of which
arm is being worn, or how it was positioned, the
topic “armmyo” was created, publishing data in
eight-bit integer numbers;
To obtain the linear speed of the robot, the qy-myo
was written, which converts the information from
the IMU (pitch, roll and yaw) from the built-in
gyro in the armband into numbers for the speed
publisher; to control the linear speed in the Peo-
pleBot’s x axis, a method to normalize the data
from pitch, roll and yaw (specific orientation in
space) was defined, publishing only the pitch on
“qymyo” topic, as the x position of a 3D vec-
tor (x,0,0), in which the maximum value assigned
was 1 and the minimum -1. When passing by 0 it
stops the robot;
yy-myo allows to combine gestures, because with-
out it, there are the limitation of five gesture con-
trols. Combining the IMU with the gesture recog-
nition gave a greater flexibility to increase the
range of control. The technique was the combi-
nation between gesture and position of the arm. It
publishes 64 bit float data to keep the script aware
of the angular position of the arm;
In order to send the original gestures, preset by
Thalmic labs, the topic gest-myo publishes them
into integer numbers of eight bits to the robot.
Figure 3: Operating flowchart between nodes of Myo and
2.2 Robot Control
To allow the controlling of the robot, is necessary for
Myo to be synchronized. This is done with the stan-
dard gesture from Thalmic Labs. The movement of
the robot depends on the state of the motors, so for ac-
tivate them we defined the fingers-spread gesture with
angular information under 0.5. All the controlling of
the robot depends on the angular position of the user’s
arm, defined by: Above 0.5: robot’s gripper control,
below 0.5: robot’s movement control.
Figure 4: Illustration of the control area.
The movement of the robot was set through ges-
tures as shown in figure 6:
Fingers spread – enables and disable of motors;
Fist – stops the robot;
Wave in – turns the robot clockwise;
Wave out – turns the robot anticlockwise;
Double tap enables linear movement of the
robot. Figure 5 shows how to move the arm to
control the robot velocity.
Gripper movements:
Fingers spread – opens the gripper;
Fist – closes the gripper;
Wave in – raises the gripper;
Wave out – lowers the gripper;
Double tap – stops all movements.
With the study of PeopleBot’s operation and the ROS
platform, allied to the knowledge about Myo, it was
possible to create a functional interface to the robot
using the myoelectric technology. PeobleBot could
be controlled to perform movements and grips.
One of the advantages of controlling computa-
tional applications with Myo is that the controlling re-
sponse is very fast, furthermore, having a gyroscope,
accelerometer and control of its raw data, despite the
lack of gestures, can create combinations that over-
comes some of this limitations, and is very practi-
cal/comfortable for user.
Application of Myo Armband System to Control a Robot Interface
Figure 5: Example of arm motion to control the linear ve-
Figure 6: Illustration to control PeopleBot’s motors and its
Figure 7: Illustration of gripper control.
To analyze how precise are the detection of move-
ments by the software, some tests were executed on
five volunteers. Before test begins, volunteers make
the gestures a few times to be familiarized with move-
ments and also to get the timing for performing each
gesture. It needs a interval from one second between
each one. After training time, users made 20 times
each gesture in a random sequence. The mean clas-
sification rate to distinguish among movements was
93,6% which, determining an efficient scan of myo-
electric signs by the hardware also due to feedback
interaction with the robot.
During the creation process and the studies of the
interface, we found some difficulties: For the number
of tasks performed by the robot, we had few gestures
available. Thus, it can be concluded that the gestu-
ral options offered by Myo are limited for control-
ling more complex devices. Another point noted was
that the continuous use of the equipment can cause
some muscle discomfort due to fatigue generated by
the repetition of gestures, which also causes problems
in the recognition of the data. Apparently, muscle fa-
tigue causes different myoelectric signals to the ges-
ture performed or just turns it more difficult to be rec-
ognized by the algorithm. Physical activity before the
use of Myo seems to cause difficulty on determining
the gestures as well.
BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing
This work was just one example of a huge range of
things the Myo armband can do. Even considering
the “young age” of this device, it is very useful, has an
excellent acquisition and processing of signals, more-
over, in the future it is possible that new gestures will
be implemented and used to open a wider range of
options for the user. Furthermore, the research should
generate new information about the continuous use of
myoelectric detection devices, the effects on the user
and its limitations.
For next steps, we have more 15 volunteers to help
us for creating new gestures recognition system using
the same hardware Myo with a different classifier al-
gorithm to improve its capabilities adding other ges-
tures and after that apply it on another systems.
This study was produced with FEI, CAPES and
FAPESP funding.
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Application of Myo Armband System to Control a Robot Interface