2020 8th IEEE International Conference on Biomedical
Robotics and Biomechatronics (BioRob)
New York, USA. Nov 29 - Dec 1, 2020
Performance Evaluation of Pattern Recognition Algorithms for
Upper Limb Prosthetic Applications
A. Marinelli, Student Member, IEEE, M. Semprini, Member, IEEE, M. Canepa, L. Lombardi, S.
Stedman, A. Dellacasa Bellingegni, M. Chiappalone, Member, IEEE, M. Laffranchi, Member, IEEE,
E. Gruppioni, Member, IEEE, L. De Michieli, Member, IEEE, N. Boccardo

preventing users from perceiving the prosthesis as a true
substitute of the missing limb [3].
Recently, many groups have focused on improving the control
of multi DoF myoelectric hand prostheses [4-6]. These studies
are generally focused on improving pattern recognition
algorithms for detecting the intended movement, in some
cases even reaching accuracy scores greater than 95% [7].
However, these promising outcomes are only achieved in lab
environments and are not translated into useful everyday life
applications mainly because these systems are not robust in
scenarios which differ from that of their training, with a
consequent considerable drop in performance [2]. Often, a
simple change of arm posture makes the prosthesis useless
[8]. Therefore there is an urgent need for robust and reliable
decoders, able to cope with the potential sources of variability
in the input data [9-11]. Some groups attempted to solve these
issues by providing decoders with supplementary input data,
using the approach of data abundance as in [12].
To promote usage in daily life scenario, here we propose
to improve the decoder accuracy without relying on additional
sources of input data, but through optimization of pattern
recognition from the available data using information solely
coming from the EMG sensors embedded in the prosthesis.
We also aimed at reducing the number of EMG sensors. We
tested and optimized several state-of-the-art classifiers: NonLinear Logistic Regression (NLR) [7], Regularized LeastSquare (RLS) [13], Artificial Neural Network (ANN) [14],
Support Vector Machine (SVM) [15], and Linear
Discriminant Analysis (LDA) [7], for simultaneously
decoding multi-joint hand configurations from up to 6 EMG
electrodes placed on the arm. Moreover, NLR, RLS and LDA
combined pattern recognition with evaluation of the
likelihood of the decoded joint positions, thus enabling a
confidence-based rejection criterion, called “abstention”,
which reduced decoding errors [16]. We here show that
accurate tuning of algorithm parameters and the inclusion of
the abstention feature, strongly increased algorithms accuracy
and in particular of the NLR, which outperformed the others.
This was observed not only on healthy subject data, but also
on upper limb amputees controlling a virtual hand.
Abstract— Poly-articulated, myoelectric hand prostheses
reproduce complex multi-degree of freedom movements, which
are fundamental to effectively assist upper limb amputees in the
execution of daily life activities. In this scenario, the control
system consists in a pattern recognition algorithm translating
the recorded electromyographic (EMG) activity into joint
movements. However, the low decoding performance typically
reached by the control system results in poor stability of the
prosthetic device. In order to solve this issue, here we tested
several state-of-the-art classifiers for decoding multi-joint hand
movements from electromyographic recordings of arm muscles,
collected from healthy subjects. Specifically, we tested: NonLinear Logistic Regression (NLR), Regularized Least-Square,
Artificial Neural Network, Support Vector Machine, and Linear
Discriminant Analysis. We aimed at minimizing the number of
EMG electrodes (6 maximum) by optimizing each classifier in
terms of the F1Score, and then we compared the performance of
the classifiers. We found that the NLR algorithm achieved the
best results with only 3 EMG electrodes. The optimized
algorithms were then tested on three right arm amputees
controlling a virtual hand. We obtained that algorithm’s
performance was comparable with that obtained from healthy
subjects. In particular, the NLR classifier achieved 99% correct
classification for all the patients, indicating its potential effective
use in prosthetic applications.
I. INTRODUCTION
Poly-articulated
myoelectric
hand
prostheses
are
characterized by a high number of degrees of freedom (DoF).
A crucial feature for their functionality and usability is their
controllability. Indeed, low usage intuitiveness, often due to
the poor ergonomics of the control system [1], lies among the
main causes for prosthesis abandonment. Currently, typical
myoelectric hand prostheses rely on the use of two
electromyographic (EMG) sensors, placed on flexor and
extensor muscles of the wrist, respectively. EMG sensors
detect the difference in electric potential produced by the
underlying muscles, and the control policy regulates the
prosthesis velocity such that it is proportional to the muscle
contraction [2]. However, this does not allow simultaneous
multi-joint control of the prosthesis as it is based on the
control of one joint at the time. It is not very intuitive, and the
resulting movement is far from being smooth and natural,
*Research supported by INAIL, grant PPR-AS 2017-2020.
A. Marinelli, M. Semprini, M. Canepa, L. Lombardi, S. Stedman, M.
Chiappalone, M. Laffranchi, L. De Michieli and N. Boccardo are with Rehab
Technologies IIT-INAL Lab, Istituto Italiano di Tecnologia, Genova 16163
Italy
(Tel.:
0039-010-28961;
e-mails:
andrea.marinelli@iit.it,
marianna.semprini@iit.it, michele.canepa@iit.it, lorenzo.lombardi@iit.it,
978-1-7281-5907-2/20/$31.00 ©2020 IEEE
samuel.stedman@iit.it, michela.chiappalone@iit.it, matteo.laffranchi@iit.it,
lorenzo.demichieli@iit.it, nicolo.boccardo@iit.it).
A. Marinelli is also with the Department of Informatics, Bioengineering,
Robotics and systems Engineering, University of Genova, 16145 Italy.
E. Gruppioni and A. Dellacasa Bellingegni are with the Prosthetic Centre
INAIL, Vigorso di Budrio, Bologna 40054 Italy (e-mails:
a.dellacasabellingegni@inail.it, e.gruppioni@inail.it).
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A
Rest
Closure
Pronation
Opening
B
Supination
sEMG Signals (V)
Hand
Opening/
Closure
Wrist
Pronation/
Supination
Time (s)
Figure 1. A typical example of EMG activity and related joint movement. A: EMG activations and virtual hand speed during different hand and wrist
movements. B: DoFs of the controlled virtual hand.
section II.F). To this end, we customized the EMG Data
Acquisition & Training Software (EDATS), developed by
Centro Protesi INAIL of Budrio [7] (Fig. 2A). This
acquisition system collected EMG data for off-line training of
classifiers, implemented in MATLAB (MathWorks).
Once each classifier was trained, subjects were allowed to
freely perform any of the 4 movements, which was decoded
by the resulting best performing classifier and replicated in
real-time by the virtual hand, whose velocity was linearly
dependent on the root mean square (RMS) of EMG signals.
II. MATERIAL AND METHODS
A. Subjects
We recruited 10 able-bodied, right-handed subjects (6
males, age 36 ± 9 years) and 3 amputated subjects (monolateral, right trans-radial amputation of dominant limb, expert
active prostheses users). All subjects provided written
informed consent. The study conformed to the standard of the
Declaration of Helsinki and was approved by the ethical
committees of Bologna-Imola (CP-PPRAS1/1-01).
D. Training and testing of the classifiers model
The analysed classifiers (see section II.F) were supervised
algorithms and thus needed a specific calibration procedure to
estimate the best set of internal parameters for further on-line
use. The calibration described here, regards the optimization
of internal parameters of each classifier, and hence is not to
be confused with the further optimization of the algorithm
according to other hyperparameters (see section II.G).
Input data for classifiers was composed by single samples
of EMG data. For each classifier, the data was divided into
test set (TS), training set (TR) and validation set (VS). For the
B. Experimental Protocol
Six commercial EMG electrodes (13E200 AC, Otto Bock)
were embedded into a custom-made elastic brace placed
around forearm or stump. This allowed to collect electrical
activity from 6 relevant muscle groups involved in grasping
and prone/supination movements (Fig. 1). Muscles involved
are: Extensor Carpi Radialis Longus Muscle (EMG0),
Palmaris Longus Muscle and Flexor Carpi Ulnaris Muscle
(EMG1), Extensor Digitorum Muscle (EMG2), Flexor Carpi
Radialis Muscle (EMG3), Extensor Carpi Ulnaris Muscle
(EMG4), and Brachio-Radial Muscle (EMG5).
After electrodes placement, subjects were positioned in
front of a monitor displaying a virtual hand with an active
wrist (Fig. 1B). We asked them to sequentially perform hand
opening/closing and wrist pronation/supination for 10 times.
We also collected 16 repetitions of hand at rest (duration 2s,
sampling frequency 1kHz).
C. EMG Signal Processing
EMG signals were recorded by custom EMG processing
board programmed by Code Composer software (Texas
Instruments). The EMG processing board performed A/D
conversion and then Bluetooth transmission of data to a
dedicated PC (Dell Xps15, Intel i7, 16Gb Ram, Windows 10).
Here single samples of the EMG signals related to movements
and rest, were used to off-line train several classifiers (see
Figure 2. Experimental Setup. A: a power supply, the EMG processing
board, the sEMG armband, the virtual hand and the EDATS software.
B: a healthy subject performing in Real-Time the virtual hand control.
C: an amputee performing in Real-Time the virtual hand control.
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TABLE I. NLR STRUCTURE OF POLYNOMIAL FEATURES FOR EACH D.
D
Figure 3. Block diagram of the experimental setup.
algorithms NLR, RLS, ANN and SVM, the TS was obtained
after a down-sampling operation from 1 kHz to 25 Hz. This
operation was performed in order to reduce TR size and the
computational time required to create the classifier model, as
in [7]. The TR was composed by 70% of remaining data,
while the VS (not used for the LDA algorithm), was finally
composed by 30% of remaining data. For the LDA, the dataset
was only split into TS (30% of the entire dataset) and TR (the
remaining 70%).
Through a custom MATLAB API, the parameters of the
selected pattern recognition model were tuned off-line on the
TR. A validation phase followed on the VS for tuning the
hyperparameters to prevent overfitting (step 1 in Fig. 3.
Lastly, the performance of the model was assessed on the TS.
Each classifier was optimized according to its model
parameters with a computational time ranging from few
seconds to few minutes, depending on the algorithm. An
evaluation phase followed in which the calibrated algorithm
was used to decode hand motion on-line using a real-time PC
based PR simulator (step 2 in Fig. 3). The resulting outcomes
were then uploaded (step 3 in Fig. 3) to the EMG processing
board (Tiva Cortex-M4) for final on-line classification (step
4 in Fig. 3), during which users were able to freely control the
virtual hand. In this phase the sampling frequency was 300Hz,
thus the classification was updated every 3ms.
Description
Example
1
Linear case (LR)
x1, x2, x3, x4, x5, x6
2
max 2nd degree
3
max 3nd degree
4
max 4nd degree
5
max 5nd degree
6
max 6nd degree
x1, …, x6, x1x2, x1x3, …, x5x6, x12, x22,
…, x62
x1, …, x62, x1x2x3, …, x4x5x6, x13, …,
x63
x1, …, x63, x1x2x3x4, …, x3x4x5x6, x14,
…, x64
x1, …, x64, x1x2x3x4x5, …, x2x3x4x5x6,
x15, …, x65
x1, …, x65, x1x2x3x4x5x6, x16, …, x66
7
max 7nd degree
x1, …, x66, x17, x27, x37, x47, x57, x67
of classes likelihood. During validation, we tested the
F1Score performance of each classifier with respect to the
number of EMG electrodes. This analysis was used to
estimate the minimum number of electrodes. Also, we
optimized other parameters for NLR and ANN classifiers.
For NLR, we also considered the role of the degree (D) of
complexity. This parameter, ranging from 1 to 7, codified a
structure of polynomial features, as reported in TABLE I.
For each D, the obtained polynomial features consisted in:
all the values obtained by the power’s series of each input
until the degree defined by the parameter D (xi1,…xiD; i =
1…6 number of electrodes), and all combinations obtained
through a combination without repetition of a maximum
number of elements equivalent to the degree defined by D.
Instead, for ANN classifier, we first evaluated the role of
the number of hidden layers by setting the number of neurons
to 30 (the maximum), and we then optimized the number of
neurons, using the optimal number of hidden layers.
In addition, for the NLR, RLS and LDA regression
algorithms we introduced a “truth index”, based on
Likelihood threshold [11]. The Likelihood maximizes the
efficiency of each algorithm: we tested different values of
threshold of likelihood from 70 to 100% in order to guarantee
the classification of the voluntary movements only. The
threshold was optimized for each dataset and each algorithm,
by evaluating the F1Score obtained. Movements discarded
from classification were considered as “abstentions” and did
not produce any movement.
E. Virtual hand
The virtual hand was created with the 3D design program
Blender (Blender Foundation). The trajectories of the opening
and closing of the hand, as well as the pronation and
supination of the wrist (Fig.1A), were implemented
coherently with the real joint axes and related joint
movements of the human hand (50th percentile) [17]. The
synergetic behaviour of all the fingers involved in the
movement was mapped by means of a Unity (Unity
Technologies) based software. A final middle-ware software
implementation connected the virtual environment (moving
the virtual hand) to the EDATS software by means of TCP/IP.
G. Optimization and testing of the best configuration of
hyperparameters
The dataset acquired from the healthy population was used
to find the best hyperparameters (i.e. the set of electrodes,
degree of optimal complexity, number of hidden layers, and
number of neurons) for the pattern recognition algorithms.
The TS (section II.D) was used to evaluate the performance
of each classifier according to each specific set of
hyperparameters in terms of F1Score, which takes into
account the rate of false and true positives and of false
negatives [18].
A performance comparison of the algorithms was then
evaluated in terms of classification (the % of correctly
decoded movements, i.e. accuracy), F1Score, and abstention
(the % of non-assigned movements). The best configuration
F. Pattern Recognition algorithms
In order to perform EMG pattern recognition, we tested a
wide range of supervised machine learning algorithms: NLR
[7], RLS [13], ANN [14], SVM [15], and LDA [7]; for each
algorithm we defined the model and their optimization as in
the cited references. ANN and SVM are classifiers and thus
their output is the decoded movement class; instead, the other
algorithms are regressors and thus their output is an estimate
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Figure 4. F1Score obtained by NLR using different number of
electrodes and fixing maximum value of D to 7. NS: not significant.
Figure 5. F1Score obtained by NLR using different maximum value of
parameter D and fixing number of electrodes to 3. NS: not significant.
of each classifier was then used for training the algorithms on
the amputees’ dataset. We also assessed whether the obtained
scores were comparable with those obtained by the healthy.
Statistical analysis was performed with the Wilcoxon
signed rank test and the Bonferroni correction [19].
Ulnaris Muscle (EMG1), and the Extensor Carpi Ulnaris
Muscle (EMG4) was enough to reach the same performance
as with the maximum number of electrodes, Wilcoxon signed
rank test p = 0.084. Fig. 4 shows that for NLR, a significant
decrease of F1Score was obtained when the number of
electrodes was reduced from three to two, Wilcoxon signed
rank test p = 0.0039.
For the other classifiers, the number of electrodes required
to maximize the performance was greater than 3, and was
respectively 4 for the RLS, 6 for ANN and SVM, and 5 for
LDA, as indicated in TABLE II.
III. RESULTS
A. Effect of EMG electrodes number on performance
We first investigated, for each algorithm, the minimum
number of EMG electrodes needed to maximize performance,
expressed as non-statistical difference between the
distributions of F1Score obtained from the TS. Starting from
the full configuration (6 EMG electrodes) we progressively
reduced the number of electrodes by removing those placed
on smaller muscles, according to the following order: EMG5,
EMG2, EMG3, EMG4.
For the NLR algorithm, we found that a configuration of
three electrodes placed on the Extensor Carpi Radialis Longus
Muscle (EMG0), Palmaris Longus Muscle and Flexor Carpi
B. Effect of D parameter on NLR performance
For the NLR algorithm, we swept the maximum value of
the parameter D, which regulates the maximum polynomial
degree, from 1 to 7 and accordingly evaluated the
performance in terms of F1Score. As shown in Fig. 5, we
found that D = 2 maximized the performance.
C. Effect of network architecture on ANN performance
For the ANN classifier, we first tested the role of the
TABLE II: F1SCORE OBTAINED FOR EACH CLASSIFIER WITH A
DIFFERENT NUMBER OF ELECTRODES. In bold are indicated the number
of electrodes which saturated the performance of each classifier.
Algorithm
NLR(D=7)
RLS
ANN(L=10, N=30)
SVM
LDA
EMG
[#]
2
3
4
5
6
2
3
4
5
6
2
3
4
5
6
2
3
4
5
6
2
3
4
5
6
F1Score
[%]
82.7 ± 19.7
94.9 ± 10.2
98.2 ± 5.1
99.9 ± 0.2
99.9 ± 0.2
25.0 ± 13.9
50.3 ± 13.0
60.3 ± 10.8
59.9 ± 11.0
57.1 ± 11.6
70.7 ± 5.5
75.7 ± 3.7
77.7 ± 4.8
79.0 ± 4.6
81.7 ± 4.8
70.4 ± 5.5
76.0 ± 3.8
78.6 ± 4.0
80.6 ± 4.3
83.6 ± 4.4
89.1 ± 11.6
96.4 ± 1.5
97.8 ± 1.3
98.5 ± 1.5
98.7 ± 1.4
Figure 6: Optimization of ANN with 6 EMG electrodes. A: F1Score
obtained using different maximum number of hidden layer and fixing
maximum number of neurons to 30. B: F1Score obtained using different
maximum number of neurons and fixing maximum number of hidden
layers according to previous analysis. NS: not significant.
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hidden layers on performance. We fixed the maximum
number of neurons to 30 and computed the F1Score for each
maximum number of hidden layers (ranging from 1 to 10).
We found that L = 6 hidden layers represented the optimal
choice, as the F1Score does not differ from that obtained with
a larger number of layers (Fig. 6A).
We then fixed L = 6 hidden layers and evaluated the
performance of the ANN varying the maximum number of
neurons in the following range: [1, 5, 10, 15, 20, 23, 24, 25,
26, 27, 28, 29, 30]. We found that with N = 24 maximum
number of neurons, performance is maximized (Fig. 6B).
TABLE III: CLASSIFIERS PERFORMANCE SCORES OBTAINED WITH THE
HEALTHY POPULATION DATA. Data are reported as mean ± standard
deviation across subjects. In bold are reported the best scores according
to each evaluated indicator (classification, F1Score, and abstention).
Algorithm
EMG
[#]
Classification
[%]
F1Score
[%]
Abstention
[%]
NLR(D=2)
RLS
ANN(L=6, N=24)
SVM
LDA
3
4
6
6
5
99.9 ± 0.1
62.6 ± 11.0
80.3 ± 4.8
83.5 ± 4.6
98.5 ± 1.4
97.3 ± 7.9
60.3 ± 10.8
80.2 ± 4.7
83.6 ± 4.4
98.5 ± 1.5
66.9 ± 7.4
43.4 ± 8.6
39.5 ± 4.0
recognition. Moreover, NLR led to more consistent results, as
indicated by the small standard deviation. With respect to the
F1Score, LDA obtained the highest value, although with no
statistical difference with NLR (Fig. 7B, TABLE III). NLR
also obtained highest percentage of abstention (Fig. 7C).
D. Comparison of algorithms performances
Performances in terms of F1Score of the NLR, RLS,
ANN, SVM, and LDA methodologies were compared
through a Wilcoxon Signed-Rank test for a different number
of electrodes. We found no statistical difference of F1Score
over different numbers of electrodes. TABLE III summarizes
the performance scores in terms of classification, F1Score,
and abstention for each classifier with the optimized number
of electrodes and hyperparameters, as previously determined.
Fig. 7A reports the classification score obtained by each
classifier. With only 3 EMG electrodes, NLR obtained the
highest score, even higher than that obtained by LDA, which
is the gold standard for applications requiring pattern
E. Algorithms evaluation on the amputees’ dataset
We tested the classifiers on three trans-radial amputees, using
an optimized configuration, as obtained from the analysis of
the healthy dataset. TABLE IV shows the values of
classification, F1Score, and abstention obtained from
patients. Amputees obtained scores matching those obtained
by healthy subjects: NLR obtained the highest classification
score, followed by LDA. F1Scores were highest for LDA,
followed by NLR. NLR obtained greater abstentions.
A
IV. DISCUSSION
We tested several pattern recognition algorithms for
decoding hand movements and we identified NRL as the one
producing best performance. Indeed, our results demonstrate
that NLR is the only algorithm, among those tested, which
reached the highest classification performance with only three
EMG electrodes. This is particularly desirable in the context
of hand prosthetics, as placing each EMG sensor requires a
corresponding hole in the socket. Therefore, the smaller the
number of electrodes, the smaller the possibility of
undermining the socket robustness as well as the stability of
the entire prosthetic system. This is crucial for amputees
whose residual arm is proximal, resulting in an internal
lamination of the socket that is drastically reduced. Reducing
B
TABLE IV: CLASSIFIERS PERFORMANCE SCORES OBTAINED BY
bold are reported the best scores according to each
indicator (classification, F1Score, and abstention) for each patient.
AMPUTEES. In
P.
C
1
2
3
Figure 7. Algorithm comparison. A: Fraction of correct classification
obtained by each classifier. B: F1Score obtained by each classifier. C:
Fraction of abstention obtained by each classifier. NS: not significant.
Algorithm
NLR
RLS
ANN
SVM
LDA
NLR
RLS
ANN
SVM
LDA
NLR
RLS
ANN
SVM
LDA
EMG
[#]
3
4
6
6
5
3
4
6
6
5
3
4
6
6
5
Classification
[%]
99.0
39.0
70.5
75.1
96.0
99.9
60.5
72.3
73.3
94.7
99.9
60.6
80.3
84.1
96.5
F1Score
[%]
82.9
36.1
71,3
76.0
96.0
85.5
54.1
73.8
75.0
94.5
93.4
53.5
80.5
84.4
96.4
Abstention
[%]
77.6
47.4
39.7
75.0
42.9
34.4
72.3
39.1
35.3
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the number of electrodes therefore, increases the physical
robustness of the prosthesis once donned on the residual limb.
Moreover, fewer electrodes substantially reduce the
prosthesis cost. All these features may increase the number of
patients adopting multi-electrodes control strategies.
With respect to NLR algorithm, we also found that
keeping a small degree of polynomial complexity (D = 2)
maintains the same level of performance as with higher
degrees. This affects the computational burden, as algorithm
can still achieve high performance, but more efficiently and
in shorter times, a highly desirable feature when designing
embedded devices, such as myoelectric prostheses. We were
also able to simplify network architecture for ANN classifier,
by optimizing the maximum number of hidden layers and
neurons, and setting them to 6 and 24 respectively.
When comparing classifiers in their optimized form, we
found that, also in this case, the NLR reached highest
classification and F1Score. NLR also obtained a greater
number of abstentions. However, this apparent weakness is
counterbalanced by the high classification frequency set. In
fact, implementation of abstention is generally aimed at
reducing the number of classification artifacts, but often
comes at the cost of adding delays in the control loop, because
the abstention percentage limits the number of decoded
movements. Generally classifiers work with a time window
of 200 ms (5 Hz), meaning that every second, 5 movements
are decoded [20]. A high percentage of abstention may thus
result in a latency between movement intention and actual
movement of the controlled device. However, our classifiers
are implemented with frequency of 300 Hz, meaning that 300
movements per second are decoded and thus even with a high
abstention percentage, the control loop is not affected by
noticeable delays, and users therefore are not perceiving any
latency between movement intentions and resulting action.
Overall, our analysis revealed that the NLR classifier is
the best option for pattern recognition application in the
framework of EMG signal acquisitions. We confirmed this
result with upper limb trans-radial amputees, who were able
to successfully control a virtual hand by controlling the
residual muscles of the stump. Clearly, we need to extend the
study to a wider population of amputees and we also need to
confirm these promising results with clinical trials on a
physical hand prosthesis. However, as verbally described by
the amputees, the NLR algorithm allows them to reliably
translate real-time movement intentions into actions. We plan
to assess online performance of the classifier with a target
achievement control (TAC) test, which measures the ability
to move a virtual arm into a target posture [21].
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V. CONCLUSION
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We tested several pattern recognition algorithms for hand
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