A Human-Computer Interface based on Electromyography
Command-Proportional Control
Sergey Lobov
1
, Nadia Krilova
1
, Innokentiy Kastalskiy
1
, Victor Kazantsev
1
and Valeri A. Makarov
1,2
1
Lobachevsky State University of Nizhny Novgorod, Gagarin Ave. 23, 603950, Nizhny Novgorod, Russia
2
Instituto de Matemática Interdisciplinar, Applied Mathematics Dept., Universidad Complutense de Madrid,
Avda Complutense s/n, 28040, Madrid, Spain
Keywords: Electromyography, Human-Computer Interface, Pattern Classification, Artificial Neural Networks.
Abstract: Surface electromyographic (sEMG) signals represent a superposition of the motor unit action potentials that
can be recorded by electrodes placed on the skin. Here we explore the use of an easy wearable sEMG
bracelet for a remote interaction with a computer by means of hand gestures. We propose a human-
computer interface that allows simulating “mouse” clicks by separate gestures and provides proportional
control with two degrees of freedom for flexible movement of a cursor on a computer screen. We use an
artificial neural network (ANN) for processing sEMG signals and gesture recognition both for mouse clicks
and gradual cursor movements. At the beginning the ANN goes through an optimized supervised learning
using either rigid or fuzzy class separation. In both cases the learning is fast enough and requires neither
special measurement devices nor specific knowledge from the end-user. Thus, the approach enables
building of low-budget user-friendly sEMG solutions. The interface was tested on twelve healthy subjects.
All of them were able to control the cursor and simulate mouse clicks. The collected data show that at the
beginning users may have difficulties that are reduced with the experience and the cursor movement by
hand gestures becomes smoother, similar to manipulations by a computer mouse.
1 INTRODUCTION
Recent years witness a rapidly growing interest to
the development of devices controlled by
electromyographic (EMG) signals through a human-
machine interface. There have been proposed
interfaces controlling personal computers (PC)
(Chowdhury et al., 2013; “Myo™ Gesture Control
Armband”, 2013), mobile and humanoid robots
(Wang et al., 2012; Lobov et al., 2015a; Lobov et
al., 2015b), powered prostheses (Roche et al., 2014;
Hahne et al., 2014), and exoskeletons (Kiguchi and
Hayashi, 2012; Singh and Chatterji, 2013; Mironov
et al., 2015), among others. Despite technical differ-
rences in the implementation, such devices in gene-
ral exploit quite similar controlling strategies (see,
e.g., Peerdeman et al., 2011; Roche et al., 2014).
The simplest approach uses a single-channel
recording of the bioelectrical activity of a muscle
and applies either proportional (gradual) (Bottomley
and Cowell, 1964) or trigger-like (Kobrinskiy, 1960)
transformation to generate the controlling output.
Multi-channel setups allow for simultaneous
treatment of the activity of several muscles and, in
general, are more promising due to higher number of
degrees of freedom involved in the analysis. Then,
commands sent to an external device can be
evaluated either by a regression over EMG signals
or by a classification EMG in terms of the classical
pattern recognition problem (Kiguchi and Hayashi,
2012; Roche et al., 2014).
Some of the proposed techniques have been
implemented in commercially available devices. For
example, the wearable bracelet MYO™ (Thalmic
Labs Inc.) employs classification of five hand
gestures for managing a personal computer (PC)
(“Myo™ Gesture Control Armband”, 2013). This
bracelet, however, does not implement sEMG-based
proportional control of a PC cursor. Instead, it uses
measurements of spatial coordinates of a hand. This
imposes restrictions for the use of the device by
disabled people, e.g. by amputees.
The powered prostheses available on the market
support either single channel or multichannel
regression strategies generating controlling output
(Roche et al., 2014). At the time being, pattern
Lobov, S., Krilova, N., Kastalskiy, I., Kazantsev, V. and Makarov, V.
A Human-Computer Interface based on Electromyography Command-Proportional Control.
DOI: 10.5220/0006033300570064
In Proceedings of the 4th International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX 2016), pages 57-64
ISBN: 978-989-758-204-2
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
57
recognition methods have not been implemented yet
due to a limited number of possible types of
movements. Although the regression based methods
showed important advances, there also have been
revealed weak points. For example, the setup tuning
procedure usually takes relatively long time and
requires specific instruments for measuring the exact
position of different parts of the arm (Fougner et al.,
2012; Jiang et al., 2012; Hahne et al., 2014).
Artificial neural networks (ANNs) have also
widely been used to solve both sEMG classification
and regression problems. Their accuracy is rather
high in comparison to other approaches (see, e.g.,
Peerdeman et al., 2011; Baspinar et al., 2013).
However, the adaptation of ANNs to commercially-
ready human-computer interfaces is still an open
problem that also requires investigation of the user
experience and potential restrictions.
In this work we describe a low-cost human-
computer interface that uses multichannel sEMG
signals processed by ANN. An output of the ANN is
then used as a controlling signal for a PC. Thus, the
gesture classification and regression are strongly
overlapping processes made by the ANN. Moreover,
the ANN learning can be accomplished relatively
fast and requires no special measurement techniques.
Using this interface a user can move a mouse cursor
on a computer screen by hand movements (muscle
contraction) and simulate mouse clicks. Then
disabled people and amputees can use such an
interface in their daily life.
2 METHODS
2.1 Subjects and Testing Task
For experimental purpose we recruited 12 healthy
volunteers of either sex from 21 to 41 years old. The
study complied with the Helsinki declaration
adopted in June 1964 (Helsinki, Finland) and revised
in October 2000 (Edinburg, Scotland). The Ethics
Committee of the Lobachevsky State University of
Nizhny Novgorod approved the experimental
procedure. All participants gave their written
consent.
After machine learning of the interface ANN
(individual for each subject) all participants were
asked to move remotely (using hand gestures) a PC
pointer in Windows OS and to perform the
following testing task: open calculator application,
type "2 + 2 =", find the result and close the
application. Each participant performed the testing
task twice to examine two types of the learning
procedures (see below). Then each participant
described verbally his/her user experience.
All subjects had no previous experience with
sEMG interfaces, except one who used MYO
bracelet for two weeks. For this "experienced" user
an additional test was developed. The user was
asked to connect four points on the computer screen
forming a diamond: a) by using a computer mouse
and b) by the sEMG interface. Then the performance
of "diagonal” movements was evaluated as
=1
−
, (1)
were
and
are the lengths of the curves drawn
by means of the sEMG interface and computer
mouse, respectively.
Figure 1: The hardware-software system MyoCursor. User
with a MYO bracelet placed on forearm controls a PC.
The bracelet transmits eight sEMG signals through a
Bluetooth interface to the PC equipped with MyoCursor
software.
2.2 Myocursor Hardware-Software
Setup
Figures 1 and 2 show the developed hardware-
software system, called MyoCursor. The system
consists of a MYO bracelet worn on a forearm of the
user and a PC with specially designed software (Fig.
1). The bracelet has eight equispaced sensors
acquiring myographic signals at F = 1 kHz rate.
However, our tests have shown that it cannot deliver
data with the frequency above 300 Hz. Missing data
are filled in by returning previously sampled values.
The sEMG signals are sent through a Bluetooth
interface to a PC. We use the MYO SDK to access
NEUROTECHNIX 2016 - 4th International Congress on Neurotechnology, Electronics and Informatics
58
raw eight-channel data, while the built-in software
of the bracelet is disabled. Acquired signals are then
processed by MyoCursor software in real-time (Fig.
2). The software performs the recognition of hand
gestures and estimates the muscle efforts that finally
control the cursor in Windows OS (Microsoft Inc.)
in a way similar to that one can achieve with
ordinary computer mouse.
Figure 2: MyoCursor interface. The software processes in
real time the sEMG signals and generates commands
controlling the mouse cursor. Left-top: controlling hand
gestures, red button marks the recognized gesture (wrist
flexion). Right: Example of eight sEMG signals
corresponding to the performed gesture (vertical and
horizontal axes are in mV and s, respectively). Left-
bottom: Controlling toolbars.
2.3 Basic Hand Gestures Imitating
Mouse Manipulations
Natural hand gestures can be extremely rich. For the
human-computer interface we have selected the
following seven static hand gestures as basic motor
patterns: 1) hand at rest is used for eliminating the
cursor trend (see below); 2) hand clenched in a fist
simulates mouse-left click; 3,4) wrist flexion and
extension imitates the cursor movement to the left
and to the right, respectively; 5,6) radial and ulnar
deviations simulate up and down cursor movements,
respectively; and 7) extended palm (fingers together
or separately) is used for imitation of the mouse-
right click. An artificial neural network (see below)
should learn sEMG patterns associated with these
basic motor patterns. For machine learning we
adopted two procedures:
i) A user performs two series each consisting
seven basic gestures.
ii) A user performs two series as in (i) and four
additional gestures that are combination of pair
gestures 3-6 (e.g. simultaneous wrist flexion
(3) and radial deviation (5), which serves for
diagonal left-up movement).
In either case the users performed each gesture
during 2-3 seconds with a 2-3 seconds relaxing
pause between gestures.
2.4 Signal Analysis and Neural
Network
We divide in real-time the sEMG data flow, x(t),
into 100 ms time windows (
(
)
∈ℝ
). Windowing
is performed every 50 ms. At this rate an artificial
neural network performs calculations and provides
the cursor controlling signal (Fig. 3).
At the first step the root mean square (RMS) of
the EMG activity over 100 ms time window is
evaluated:
(
)
=
1

(
−
)


,
(2)
where N = 0.1F is the number of samples in time
window. The RMS data, as a composite feature of
the current hand gesture, are fed into an ANN with
one hidden layer containing eight neurons (Fig. 3,
but see also Sect. 3.2). Each network neuron applies
weighted sum over its inputs and uses sigmoidal
activation function to generate the output, y:
=
(
∙
)
,()=


,
(3)
where
is the vector of synaptic weights
related to the given neuron and dot stands for inner
product. The learning, i.e., adjustment of the
neuronal weights {w}, is achieved by the standard
back-propagation algorithm (Rumelhart et al., 1985).
For training and testing purposes we use sets
containing 40-60 samples corresponding to each
class. The classification error is calculated both for
the training and for testing sets. It serves as a
criterion for stopping the learning procedure as soon
as the error starts increasing on test samples. In
average the learning process on an optimized ANN
requires about 5000 training epochs and takes less
than 1 min on a standard Intel Core i5 PC. In case of
examining different parameters of the ANN (e.g. the
number of neurons in the hidden layer) the learning
time changes accordingly (could be higher or lower).
For the first learning procedure, (i), each gesture
corresponds to a single target class (Fig. 2). This
facilitates the learning procedure since each output
neuron should produce binary output: 1 for its own
A Human-Computer Interface based on Electromyography Command-Proportional Control
59
Figure 3: Data flux in the MyoCursor system. Raw sEMG
activity is mapped into cursor movements and mouse
clicks in Windows OS. First RMS and MAV activity is
evaluated in a 100 ms time window. The RMS pattern is
fed into the input layer of an artificial neural network with
one hidden layer. Every 50 ms the network output from
seven neurons provides two commands for mouse-like
clicking and four commands for cursor movements. The
latter are multiplied by the MAV to gain the cursor speed.
class and 0 for the others. To accommodate
compound gestures added in the second learning
procedure, (ii), we used the target value 1/
2
for
the two output neurons participating in the
corresponding compound gesture. Such choice of the
target value ensures the generation of a compound
vector output with unitary length when both neurons
are activated (i.e., adding two orthogonal vectors of
this length results in a unitary length vector).
2.5 Proportional Control of Cursor
Once the learning is deemed finished, online
controlling of the Windows interface can be enabled.
The cursor movement along the X-axis (Y-axis) is
proportional to the difference of the output neurons
responsible for the gestures "left" and "right" ("up"
and "down", Fig. 3). This difference is a step-like
function, which is not optimal for the cursor
manipulation. To introduce a proportional control
we employ an approach similar to that described by
Lobov et al. (2015b).
We estimate the muscle effort by evaluating the
mean absolute value (MAV) averaged over all EMG
sensors:
(
)
=
1


|
(
−
)|



,
(4)
where K is the number of sEMG channels (in our
case K = 8). Then the cursor speed can be set
proportional to the MAV (Fig. 3). However, due to
some intrinsic jitter in the muscle tone we usually
have observed a slow involuntary cursor drift. To
eliminate this artifact, the trend defined by relaxed
hand state is subtracted from the cursor controlling
signals. Thus, we define the cursor velocity by:
()=(
()

)(
()

)
(5)
where H is the Heaviside step function and
ℎ
is the
drift threshold, corresponding to A(t) evaluated over
time intervals with hand at rest.
Finally, the cursor displacement, , along the X-
and Y-axes is given by:
=
5
(
−
)
,
=
5
(
−
)
, (6)
where
,
,
, and
are the network output (Fig.
3) corresponding to the gestures “move right”,
“move left”, “move up”, and “move down”,
respectively.
3 RESULTS
To perform the testing task described in Methods a
user should be able to move the PC cursor on the
screen and to simulate clicks of mouse buttons. We
then implemented the hardware-software setup that
replaced a physical computer mouse by a virtual
pointer controlled by the human-computer interface
based on sEMG signals (Fig. 1).
3.1 Mouse Clicks
We associated the left and right clicks of “mouse
buttons” to two single hand gestures (see Methods).
The gesture selection was not trivial. Indeed, the
gestures assigned to the clicks must differ
significantly from gestures for cursor movements.
Otherwise, the ANN may confuse them, which can
significantly diminish the user experience (usually
clicks should be done at precise cursor positions).
At the beginning we performed experiments
using nine gestures, including besides those
described in Methods the supination and pronation.
Then the latter two were refused since supination
was badly recognized due to the electrode
localization (around forearm), while pronation was
confused sometimes with the forearm flexion.
To optimize the ANN performance we ran the
process of gesture recognition on the same set of
data varying the number of neurons in the hidden
layer of the ANN and the learning rate. Figure 4
shows the results. The ANN error drops significantly
between 4 and 8 neurons and then stays unchanged,
while the learning time increases (Fig. 4A). Thus,
we selected a network with eight hidden neurons for
NEUROTECHNIX 2016 - 4th International Congress on Neurotechnology, Electronics and Informatics
60
further experiments. We also observe that the
learning rate 0.01 optimizes both the learning error
and the learning time. Thus, this value has been used
in all experimental tests of the interface.
Figure 4: Performance of the artificial neural network
(mean squared error and learning time) at classifying hand
gestures with different number of neurons in the hidden
layer (A) and different values of the learning rate (B). In
case (A) the learning rate was set to 0.01. Error bars show
the standard error.
3.2 Cursor Movement: Naïve
Approach
A naïve approach to control the cursor movements
can be implemented by the event coding similar to
that used for the mouse clicks described above. We
can set the speed of the cursor movement to a
constant. Then the user will use gestures to start and
stop the movement. The drawback of such a strategy
resides in the inevitable trade off between the speed
(responsiveness of the interface) and the accuracy of
the cursor movement. Our experiments have shown
that this strategy significantly downgrades the user
experience. Nevertheless, we used these data to
Table 1: Gesture classification error and time of execution
of the user task (see Methods). User’s body types: asthenic
(a), normosthenic (n), and hypersthenic (h).
Subject number;
sex; body type;
age (years)
Classification
error, %
Task execution
time, s
rigid
classes
fuzzy
classes
rigid
classes
fuzzy
classes
1 male n 28 0.4 6.6 46 56
2 female n 28 2.1 7.3 83 110
3 male n 41 3.8 4.8 44 71
4 female n 40 0.8 6.7 64 300
5 male n 28 1.1 8.5 55 235
6 female n 21 1.2 8 76 207
7 male n 35 0.7 4.8 47 60
8 female h 21 7.8 12 169 257
9 female a 26 2.2 5.8 103 109
10 female n 28 1.4 3.1 113 179
11 male n 21 5.6 9.7 113 113
12 female h 23 4.6 5.8 120 100
mean ± s.e. 2.6±0.7 6.9 ± 0.7 86 ± 11 150 ± 23
evaluate the classification accuracy that can be
achieved in real tasks (Table 1, column “rigid
classes”). Our results confirmed that the mean ANN
error (2.6± 0.7%) is low enough for implementing
the sEMG interface.
3.3 Proportional Control of Cursor
As abovementioned, to achieve a flexible cursor
movement we aim at a combined command-
proportional control with two degrees of freedom. In
this case the cursor movement direction is defined
by gestures, while its speed is controlled by the
degree of muscle contraction (MAV), which is
almost equivalent to the palm angle. This may
significantly improve the user experience.
Experiments conducted with twelve subjects
showed that all users were able to move the cursor
and successfully simulate left and right mouse
clicks. Then we studied the performance in cases of
using rigid and fuzzy classes (see also Sect. 2.3).
3.3.1 Rigid Classes
After the network training with rigid correspondence
between hand gestures and cursor movements, all
users managed to control the cursor. Performing the
testing task (Sect. 2.1) took from 44 s to 169 s
depending on the user with the mean 86 ± 11 s
(Table 1, “rigid classes”). Nevertheless, after the test
users reported a number of repetitive difficulties: i)
Performing the task using the sEMG-interface was
much harder than using a physical mouse. For
comparison, the same test performed by using a
hardware mouse was 10 times faster in average; ii)
Diagonal cursor movements, requiring simultaneous
displacement along the X and Y axes, were
significantly more difficult than movements
involving one axis only.
3.3.2 Fuzzy Classes
An important limiting factor of the “rigid classes”
scheme resides in the intrinsic feature of the
standard neural network approach, i.e., sharp
boundaries among classes. It leads to the “winner
takes all” phenomenon and difficulties in smooth
controlling the cursor movement. The cursor usually
follows a steps-like trajectory advancing in X or Y
directions separately instead of a smooth diagonal
movement (Fig. 5, green curve). To overcome this
problem we introduced fuzzy class overlapping (see
Methods).
The implementation of fuzzy classes indeed
facilitated the diagonal movements of the cursor.
A Human-Computer Interface based on Electromyography Command-Proportional Control
61
However, our tests showed that only 4 out of 12
users found this way better than using the rigid
classes approach. In average the error of gesture
identification increased to 6.9 ± 0.7% (Table 1).
Moreover, the testing task execution time
significantly increased to 150 ± 23 s. Subjectively
this performance downgrade the users explained by
the need of making unnatural gestures. For example,
simultaneous wrist extension and ulnar deviation
(required for the diagonal right-down movement)
were reported as a pattern complex to perform.
Then, an increase in wrong classification of
compound gestures leaded to the cursor movement
in wrong direction. This, in turn, increased the test
time. Table 2 summarizes the subjective user
experience and comparison of both schemes.
Figure 5: Representative example of line drawing by an
experienced user. The task consists in connecting blue
circles by a cursor by following directions shown by blue
arrows. Grey, green, and red curves mark cursor traces
corresponding to the use of a physical mouse, sEMG with
ridged classes, and sEMG with fuzzy classes interfaces,
respectively.
3.3.3 Performance of Experienced User
Since the results of experimental tests were quite
unexpected, we hypothesized that the inconvenience
of working with the sEMG-interface with fuzzy
classes might be explained by the absence of the
experience of dealing with such an interface. Indeed,
all subjects were used to common mouse interface,
while working with sEMG may require some
preliminary practice. Thus, we selected one of the
users and asked him to work with the sEMG-
interface regularly during two weeks. Then we
repeated the testing task.
Figure 5 shows the drawing made by this user
employing three different interfaces: 1) Common
mouse (grey curve); 2) sEMG with rigid classes
(green curve); and 3) sEMG with fuzzy classes (red
curve). As expected, the training significantly
improved the sEMG performance. Taking the
performance of the mouse interface as 100%, we
obtained 75.1% for the "diagonal” performance (see
Methods) by using MyoCursor with rigid classes and
92.5% for MyoCursor with fuzzy classes. Thus,
training may improve significantly the user
experience and the user may approach the
performance close to the mouse interface.
Table 2: Subjective user experience with different types of
interfaces.
N
User comments
Preferred
method
Critical remarks
1 rigid
- Cursor drift
- Direction of movement coincides badly
with the desired direction
- Mouse clicks are difficult because of the
high threshold
2 fuzzy Clicks provoke cursor jumps
3 fuzzy
Fuzzy method is easier to move the cursor
diagonally
4 rigid
5 rigid
6 rigid
"Right-down" movement is confused with
plain "down"
7 rigid
- "Right-down" is badly detected
- 2nd test is done with a tired hand
8 fuzzy
- "Left" movement is confused with plain
"down" and "rest"
- If the hand is not relaxed before click, then
the cursor goes down
9 rigid "Right-up" is confused with plain "right"
10 fuzzy
- "Right-down" is confused with plain
"down"
- Clicks are complicated
11 rigid
- "Right-down" is confused with plain
"down"
- Clicks are complicated
12 rigid
- "Right-down" is confused with plain
"down"
4 DISCUSSION
In this work we have proposed a human-computer
interface based on a real-time recording and
processing of the surface electromyographic signals.
The interface allows controlling a PC with Windows
OS by natural hand gestures. The signal acquisition
has been implemented through an easy wearable
commercially available sEMG bracelet. This,
together with simplified software learning
NEUROTECHNIX 2016 - 4th International Congress on Neurotechnology, Electronics and Informatics
62
procedure, enables building low-budget and user-
friendly sEMG solutions that may also be useful for
disabled people and amputees.
The main difference in the algorithmic part of
our approach with existing methods based on the
regression techniques (Roche et al., 2014; Hahne et
al., 2014; Fougner et al., 2014) is the use of an
artificial neural network performing the gesture
classification. The ANN is trained at the beginning
by a relatively small set of simple hand gestures:
seven or eleven gestures depending on the method
type. This allows avoiding long lasting tuning
process common for the regression approaches,
which stems from gradual sampling of changes of
muscle tension in different movements and their
combinations. Once the ANN has been trained, it
can detect commands for simulating the right and
left mouse clicks, and for moving cursor on the PC
screen. Using an estimate of the mean muscle effort
we have implemented a proportional control of the
cursor movement. Thus, the user can easily change
the cursor velocity and hence the movement
precision by “applying” more or less effort to the
gesture.
We have tested the method on twelve healthy
subjects of either sex. To do it we implemented two
types of the cursor controlling strategies: “rigid”
classes with four individual gestures for moving
right, left, up, and down; and "fuzzy" classes with
additional compound gestures for diagonal
movements. In both cases all subjects were able to
control cursor. Our experience suggests that the
fuzzy approach is potential preferable (see Fig. 5).
However, the experimental results have shown that
in average the controlling performance decreases for
this approach, despite a theoretically attractive
possibility to move the cursor diagonally.
The subjective evaluation of the user experience
has suggested that, on the one hand, the performance
reduction can be linked with the requirement to
perform unnatural gestures (for example
simultaneous wrist extension and ulnar deviation).
On the other hand, we can anticipate that in the
fuzzy case there may exist a competition in the
output layer of the ANN, which may have negative
influence on the cursor controlling function. Thus,
we can alert the reader on the necessity of future
research involving optimization of the set of gestures
and the ANN architecture.
In the present study specific features of the users
(e.g. the degree of fitness) have been left out due to
small size of the data set. However, the collected
data allow us foreseeing that the type of constitution
may play an important role in the success of the
human-computer interface. For example, Table 1
suggests that hypersthenics may show worst results,
though statistically significant conclusions require
additional experiments.
Another point for discussion is the user readiness
to a specific control of a PC by gestures. It is worth
noting that all subjects had no previous experience
in the use of such type of interfaces, while all of
them used the common mouse interface in their
daily life. Therefore, fair comparison between the
mouse and gesture types of interfaces requires either
special sampling over subjects (for example the use
of elderly, with no experience with PC) or training
subjects to use the MyoCursor system before testing.
An experiment with one user has shown that the user
training may improve significantly the ability to use
the fuzzy sEMG-interface in such a way that its
performance may approach the performance of the
mouse interface (92% vs 100% performance reached
in the test).
ACKNOWLEDGEMENTS
This work was supported by the Russian Ministry of
Education and Science under the Federal Program
(unique identification number RFMEFI58114
X0011).
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