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sired position. Furthermore the same gesture can be
performed in various positions and orientation of the
arm (along the body, lifted up, etc.). These changes
affects the classification performance and must be ad-
dressed properly.
Starting from a data collection repeated in differ-
ent days on 10 healthy subjects, this paper proposes
the analysis of the reliability of the gesture recog-
nition in an EMG signal controlled hand prosthesis.
The analysis takes into account that our final target is
an embedded implementation. The analysis is based
on three main elements: (i) the selection of a cor-
rect signal acquisition chain, (ii) the analysis of the
physiological best placement of the EMG sensors to
grant a robust classification, and (iii) the analysis of
the performance of the system through days, evalu-
ating the difference of performance when the sensors
are placed and removed in different sessions, as in the
real use of the device for the classification of natu-
ral movements. The activation of the same gesture is
considered in multiple combinations of the arm orien-
tation and position to better study their influence on
the classification performance and consequently im-
prove the robustness of the classifier.
The paper is organized as follows. Section 2 il-
lustrates background and related works,Section 3 in-
troduces the architecture of the system used to get the
EMG signals. Section 4 describes the tests made with
the collected data. In section 5 we show the results
and discuss the solution advantages. Finally in sec-
tion 6 we draw the conclusions.
2 BACKGROUND AND RELATED
WORKS
Three types of prostheses are widely available for
people with upper limb amputations: passive, body
powered, and electrically powered. Passive prosthe-
ses are often employed for cosmetic purposes and
have limited functionality. Body-powered prostheses
are used to restore basic tasks such as opening and
closing a terminal device. These devices are often
used because they are simple, robust, and relatively
inexpensive. The user can actuate electrically pow-
ered or active prostheses but they are advantageous
w.r.t. body-powered ones because they require less
user effort, as movement is actuated with DC mo-
tors. They can be controlled through a variety of
means such as force sensors, linear potentiometers,
and EMG signals. Electrically powered prostheses re-
store some functionality to amputees, but control of
these devices is typically limited to only one or two
degrees of freedom.
As mentioned, however, recently, multi-finger ac-
tive prostheses of the upper limb have appeared at
commercial level (Bebionics, 2012, TouchBionics,
2013, Ottobock, 2009). These prostheses, driven
by EMG sensors, can replicate most of the princi-
pal movements of the hand. To achieve robustness
the movement of the active prostheses are typically
driven by non-natural activation patterns, i.e. they
decode mainly sequence of flexion and extension of
the wrist (Castellini and Smaag, 2009). The most
accurate EMG signal is taken directly on the spot
near the muscular fibers by use of implantable sens-
ing electrodes; however, they are invasive and pose
safety issue, needing surgery. Our proposed applica-
tion prefers surface EMG sensors. They suffer lack of
performance, due to the noise of the skin surface and
the crosstalk of near muscle. Nevertheless we can use
improving signal methods (Reaz et al., 2006) for a
machine learning approach.
In literature, machine learning algorithms chosen
to extract muscular pattern and classify gestures vary
from Linear Discriminant Analysis (LDA) classifier
(Young et al., 2013) to Neural Network (Matsumura
et a.l, 2002). Liarokapis and al. (2012) compare a
set of classifiers for EMG signal and conclude that
SVM (Boser et al., 1992) is the most accurate algo-
rithm for pattern recognition with these kind of sig-
nals. Other works, like (Oskoei and Hu, 2008) and
(Chen and Wang, 2013), made comparison for EMG
pattern recognition and conclude that SVM gives the
best result for these signals. The work of Englehart
and al. (2001) studies how to enhance the perfor-
mance of the SVM algorithm to reach the best accu-
racy of the classification, by optimizing the classifi-
cation parameters and the feature extraction. These
works give contributions in field of signal processing
because they propose an optimized solution to a clas-
sification problem, but they do not keep in account the
issues related to the use in daily life use, like for ex-
ample the variation of classification accuracy during
arm movements, or the sensor misplacing due to day
by day application of the prosthesis.
Some papers show experiments with a high num-
ber of subjects and features to test the accuracy of
their classification algorithm. These results reach ac-
curacy ratio near 100% but they are not applicable
in real scenarios, because they do not take into ac-
count the variability of the signal caused by the place-
ment of the EMG sensors. The misplacement of the
EMG electrodes among different sessions and the po-
sitions of the arm during the use of the prosthesis af-
fect the classification performance. This work tries to
address the problem analysing the best placement of
four EMG sensors to maximize the recognition per-
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