**1. Introduction**

Quantum chromodynamics (QCD) is established as the fundamental theory governing nuclear matter and hadrons. It holds that hadrons are made from quarks, their antimatter siblings, and gluons that carry the strong/color force binding quarks to each other while being themselves color charged objects. The fundamental laws of QCD are elegantly concise, however, understanding the structural complexity of hadrons in terms of quarks and gluons governed by those laws remains an open challenge.

In the highest energy RHIC and LHC collisions, strongly interacting matter at the relevant energies contains almost as many antiquarks as quarks, which, borrowing condensed matter physics nomenclature, one could call "undoped strongly interacting matter". However, there exist many physical settings, like non-central heavy-ion collisions, the structure of compact stars and the evolution of the early universe, where instead, strongly interacting matter is doped with e.g., an excess of down quarks over up quarks. In the physical systems mentioned above, this translates into an excess of neutrons over protons or positively charged pions over negatively charged pions. To understand these systems, we must map the phase diagram of QCD as a function of both temperature and "doping", which we can express in terms of (negative) isospin density *nI* = *nu* − *nd* or, equivalently, in the grand canonical approach to QCD, in terms of isospin chemical potential *μI* = (*μu* − *μd*)/2.

While systems of isospin-asymmetric matter are typically characterized by nonzero baryon density, that is, they also carry an excess of matter over antimatter encoded in a nonzero baryon chemical potential *μB*, switching on *μB* would hinder direct lattice simulations due to the complex action problem. As a first step it is then certainly useful to study the QCD phase diagram in the

(*T*, *μI*) plane at *μB* = 0, which has the advantage of being fully accessible to standard lattice Monte Carlo techniques in principle. As anticipated by perturbation theory and model calculations [1,2] (Figure 1a), lattice simulations found [3,4] (Figure 1b) an interesting and complex structure with at least three phases.

The main questions we address revolve around the existence of the BCS phase and the location of its boundaries. The existence of a BCS phase is expected due to perturbation theory, which is applicable in the limit |*μI*| Λ*QCD* and predicts that the attractive gluon interaction forms pseudoscalar Cooper pairs of *u* and ¯*d* quarks at zero temperature [1]. Model calculations also confirmed the existence of a BCS phase at nonzero temperature (see e.g., [2]). The transition between the BEC phase and the BCS phase is expected to be an analytic crossover, given that the symmetry breaking pattern is the same. Lattice simulations also show large values for the Polyakov loop within the BEC phase [3]. Those can be considered to be a hint for a superconducting ground state with deconfined quarks, that is for the BCS phase. Here, we propose to look for further signatures for the existence and location of the BCS phase in the complex spectrum of the Dirac operator, which can be related to the BCS gap in a Banks-Casher type relation [5] (cf. Equation (6)).

**Figure 1.** Conjectured (**a**) [1,2] and measured (**b**) [3,4] phase diagram of quantum chromodynamics (QCD) at pure isospin chemical potential.

#### **2. Simulation Setup and Observables**

The fermion matrix M*ud* within the action *Sud* = *ψ*¯M*ud ψ* for the light quarks *ψ* = (*u*, *d*) in Euclidean spacetime and in the continuum, reads

$$\mathcal{M}\_{\rm ud} = \gamma\_{\mu} (\partial\_{\mu} + iA\_{\mu}) \, \mathbb{1} + m\_{\rm ud} \mathbb{1} + \mu\_{I} \gamma\_{4} \, \tau\_{3} + i\lambda \gamma\_{5} \, \tau\_{2} \,. \tag{1}$$

Here *Aμ* is the gluon field and *τa* are the Pauli matrices. Note that in addition to the terms, including the isospin chemical potential *μI* and the light quark mass *mud*, in M*ud* there is also an explicit symmetry breaking term including the parameter *λ*, referred to as pionic source, that couples to the charged pion field *π*± = *u*¯*γ*5*d* − ¯*dγ*5*u*. This unphysical term is needed to enable the observation of the spontaneous breaking of the continuous U*τ*3 (1) symmetry and will act as a regulator for the simulations in the BEC phase [6–8]. Physical results are obtained by taking the *λ* → 0 limit.

For our measurements we consider 2+1-flavor QCD with *μI* > 0 and *λ* > 0, as already simulated, to map out the phase diagram shown in Figure 1b [3]. The lattices considered so far are *N*3 *s* × *Nt* lattices with *Nt* = 6 at various temperatures *T* = 1/(*Nta*). The Dirac operator is discretized employing the

staggered formulation and the rooting procedure. The partition function of this system is given in terms of the path integral over the gluon link variables *Uμ* = exp(*iaAμ*),

$$\mathcal{Z} = \int \mathcal{D}l I\_{\mu} \, e^{-\oint \mathcal{S}\_{\mathcal{G}}} \left( \det \mathcal{M}\_{\text{ud}} \right)^{1/4} \left( \det \mathcal{M}\_{\text{s}} \right)^{1/4} \,, \tag{2}$$

where *β* = 6/*g*2 is the inverse gauge coupling, *SG* the tree-level Symanzik improved gluon action, M*ud* the light quark matrix in the basis of the up and down quarks and M*s* the strange quark matrix,

$$
\mathcal{M}\_{\rm nd} = \begin{pmatrix}
\mathcal{D}(\mu\_I) + m\_{\rm ud} & \lambda \eta\_5 \\
\end{pmatrix}, \qquad \mathcal{M}\_{\rm s} = \mathcal{D}(0) + m\_{\rm s}.\tag{3}
$$

The argument of *D*/ indicates the chemical potential *μI* and *η*5 the staggered equivalent of *γ*5. The positivity of the integrand of Z can be shown. In particular, both determinants in the measure of the path integral in Equation (2) are positive. The quark masses are tuned to their physical values along the line of constant physics (LCP) from [9], with the pion mass *mπ* ≈ 135 MeV.

In the above setup our main observable is the spectrum of complex eigenvalues of the massless Dirac operator *D*/(*μI*). For the up quark, the eigenproblem reads

$$D(\mu\_I)\,\psi\_n = \nu\_n\,\psi\_n.\tag{4}$$

where the eigenvalues *νn* are complex numbers. The eigenproblem for the down quark can be obtained from Equation (4) using chiral symmetry, i.e., *D*/(*μI*)*η*5 + *η*5*D*/(*μI*) = 0, and hermiticity, i.e., *η*5*D*/(*μI*)*η*5 = *D*/(−*μI*)† and reads

$$
\hat{\psi}\_n^\dagger \mathcal{D}(-\mu\_I) = \hat{\psi}\_n^\dagger \,\nu\_n^\*, \qquad \hat{\psi}\_n = \eta\_5 \psi\_n. \tag{5}
$$

The fact that [*D*/(*μI*), *D*/†(*μI*)] = 0, i.e., that *D*/(*μI*) is not a normal operator, entails that its left and right eigenvectors do not coincide. However, following Equations (4) and (5), for each eigenvalue in the up quark sector there is a complex conjugate pair in the down quark sector.

**Figure 2.** Contour plots of the complex spectrum of the Dirac operator as obtained for a *Ns* = 24, *Nt* = 6 lattice at *λ*/*mud* ∼ 0.29, and *T* = 155 MeV for isospin chemical potentials *μI*/*mπ* = 0.61 (**a**), *μI*/*mπ* = 0.91 (**b**), and *μI*/*mπ* = 2.30 (**c**). The red dot indicates *mud* + *i* · 0.

Our choice of observable is motivated by the extension of the Banks-Casher relation to the case of complex Dirac eigenvalues derived in [5] for the zero-temperature, high-density limits of QCD at nonzero isospin chemical potential. The derived Banks-Casher type relation for massless quarks reads

$$
\Delta^2 = \frac{2\pi^3}{9} \rho(0). \tag{6}
$$

This relation gives us a prescription on how to obtain information on the BCS gap Δ from the density of the complex Dirac eigenvalues extrapolated at the origin *ρ*(0). In [5], the main idea for the extension of the Banks-Casher relation for *T* = 0 and |*μI*| Λ*QCD* is to write down the partition function Z(*M*) as a function of the quark mass matrix *M*, both in the fundamental QCD-like theory and in the corresponding effective theory. Taking suitable derivatives then yields an expression proportional to *ρ*(0) in the fundamental theory and Δ2 in the effective theory. The Banks-Casher-type relation is obtained by identifying these results which leads to Equation (6). A similar relation is also expected to hold at nonzero quark masses and temperatures.


Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses Using Deep Neural Networks

*Ruthber Rodríguez Serrezuela, Enrique Marañón Reyes, Roberto Sagaró Zamora and Alexander Alexeis Suarez Leon*

## **Abstract**

This research compares classification accuracy obtained with the classical classification techniques and the presented convolutional neural network for the recognition of hand gestures used in robotic prostheses for transradial amputees using surface electromyography (sEMG) signals. The first two classifiers are the most used in the literature: support vector machines (SVM) and artificial neural networks (ANN). A new convolutional neural network (CNN) architecture based on the AtzoriNet network is proposed to assess performance according to amputation-related variables. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods and The performance it is compared with other CNN proposed by other authors. The performance of the CNN is evaluated with different metrics, providing good results compared to those proposed by other authors in the literature.

**Keywords:** electromyography, convolutional neural networks, support vector machine, artificial neural network, underactuated hand prosthesis

## **1. Introduction**

Upper limb amputations are injuries that substantially limit a person's quality of life by drastically reducing the number of independent activities they perform in daily life (ADL). Current myoelectric prostheses are electronically controlled by the user's voluntary muscle contractions. A general scheme of how these and other devices that use biosignals work is presented in **Figure 1**. In this sense, the prostheses for amputees with higher performance follow this common pattern of development. There is a wide variety of very sophisticated myoelectric prostheses commercially available that use sEMG signals [1–5].

A relevant limitation in the development of pattern recognition methods for myoelectric control is that their tests are mainly performed offline. It is now established

**Figure 1.** *Most common configuration of human–machine interaction [6].*

that high offline precision, does not necessarily translate into accurate functional control of a physical prosthesis. In this sense, several recent studies have shown the discrepancy between "on and offline" in performance metrics [7–11]. However, very few studies have published validation results of pattern recognition in terms, and even fewer in clinical settings, relating the variability of the signal and the performance of the classifiers with those parameters related to amputation (disability index, length remaining limb, amputation time, phantom limb sensation, etc.) [12–15].

By other hand, the number of features extracted also depends on the number of EMG sensors and the feature extraction strategy for each sensor. Many investigations in have implemented alternatively, dimensionality reduction has been shown to be an effective feature projection (PC) method [16]. Among the most used methods are: principal component analysis (PCA) [17–19], linear-nonlinear PCA composite analysis, self-organizing feature maps [16] and supervised discretization together with PCA [20, 21].

Convolutional neural networks have been applied for myoelectric control with interest in inter-sessions/subjects and inter-session performance, in addition to many other applications in biomedical signal processing [22–24]. Some authors have commented on the advantages of these deep neural networks and their ability to assimilate the recognition of hand gestures corresponding to groups of sEMG signals. Although the results obtained come from a small number of investigations, their employment possibilities are promising [25–27].

However, most of the research has been carried out on healthy subjects. In recent decades, different authors [28–31] have shown that the variation of the signal over time in amputated patients is even greater than in healthy subjects. The EMG signal is weaker due to the amputation of certain muscle groups and as the amputation time elapses, the muscles become more atrophied and weak. There are also few databases of amputees, a situation that constitutes a significant obstacle for these researches. and for the gestures recognition at the international level [29, 30]. Additionally, amputees' performance was found to be proportional to residual limb size, indicating that an anthropomorphic model might be beneficial [28–31]. The previous findings motivated *Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses… DOI: http://dx.doi.org/10.5772/intechopen.107344*

the study on the variance of results between amputee patients and fit populations under disturbances of dynamic factors such as the length of the remaining limb, age, level of atrophy, among others. That is why the results obtained in amputated patients are far from those presented.

## **2. Materials and methods**

#### **2.1 Databases subjects**

The review of these databases allows knowing the characteristics of the population involved and the signal capture protocols. The literature review showed that there are few databases with sEMG data collection in a significant number of patients, with subjects without known prior deficiencies and whose data are heterogeneous, so the most used is the NINAPRO database [32–34], which contains the electromyography recording using the system of 8 sEMG sensors Thalmic Labs - MYO. The data in this repository is free to use and is intended for use in developing hand gesture movement classifiers [22]. The NINAPRO database in its DB3 section establishes the parameters with which the sEMG data of 11 subjects with transradial amputation were recorded [35].

In the DB3 dataset, as explained above, the transradial amputee wears two Myo cuffs side by side. The superior MYO cuff is placed closest to the elbow with the first electrode at the radio-humeral joint, following the configuration of the NINAPRO electrode. The lower MYO cuff is placed just below the first one, closer to the amputation region (**Table 1**).

In order to build our own database, the subjects invited to participate in this stage are amputated subjects, without neurological deficiencies. Invited subjects followed the population parameters used in the NINAPRO Database [36, 37]. Ten male and

**Table 1.** *Clinical characteristics of subjects with amputation. NinaPro DB3.* female amputees ranging in age from 24 to 65 years participated in the experiments. The procedures were performed in accordance with the Declaration of Helsinki and were approved by the ethics committee of the Universidad del Tolima (approval number: N – 20,160,021). All subjects participated voluntarily, providing their written informed consent before the experimental procedures. Any amputee who has experience in the use of hand prostheses will be included in the study, registering in advance their experience in the use of passive or myoelectric prostheses.

**Aspects and demographic data to be recorded**: For each subject, age, sex, education level, related to the amputation: dominant hand, amputated side, year of amputation, cause, type of prosthesis if used or has been used, and level of amputation.

**Inclusion criteria**: Adults in an age range of 20–65 years, no history of neurological and/or psychiatric diseases, voluntary participation in the study and acceptance of the medical staff. Only the transradial level of amputation will be considered, amputations above the elbow or beyond the wrist will not be admitted to the study. Any noncompliance with these parameters becomes criteria for exclusion from the study. **Table 2** shows the characteristics of the amputee patients who participated in the trials.

#### **2.2 Sensor EMG MYO armband**

Data was recorded using the commercial MYO armband (MYO). MYO is a portable EMG sensor developed by Thalmic Lab and has eight dry electrode channels with a sampling rate of 200 Hz. It is a low cost, consumer grade device with a nine inertial measurement unit (IMU) [22], that connects wirelessly with the computer via Bluetooth. It is a non-invasive device, easier to use compared to conventional electrodes [38, 39]. Despite the low sampling frequency, its performance has been shown to be similar to that of full-band EMG recordings using conventional electrodes [22, 40], and the technology has been used in many studies [29, 35, 38] (**Figure 2**).

**sEMG recording:** Prior to carrying out the tests, the patients will be instructed on the experimental procedure and as a first step. The sensor operation will be calibrated for both limbs. To make the records in each gesture, the subjects will be seated comfortably in front of the computer with both limbs with their elbows flexed at 90

#### **Table 2.**

*Clinical characteristics of subjects with amputation.*

*Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses… DOI: http://dx.doi.org/10.5772/intechopen.107344*

**Figure 2.**

*Signals acquisition through the application developed in Matlab 2020b. Author. Six movements were identified in MYO sensor to achieve grip improvement: power grip (AP), palm inward (PI), palm outward (PO), open hand (MO), pincer grip (AT) and rest (RE) (Figure 3).*

degrees and will be instructed to perform the gestures that are reflected on the monitor, in the case of amputated patients with the contralateral limb and with the amputated limb (**Figures 4** and **5**).

The graphic interface will provide the patient with the times for performing the tests and the state of rest (**Figure 5**). Amputee recordings were performed in repeated sessions for 1 week.

 (b) MYO device arrangement.*

#### **Figure 5.**

*User interface that indicates the imaginary movement to be performed and includes completion and rest time.*

The procedure carried out to capture the myoelectric signals is as follows: for each grip or gesture of the hand, 200 samples are taken during an interval of 30 seconds. Transitions are made between each of the six proposed gestures for spaces of 1 minute as recommended in [41]. sEMG signals were captured during several sessions and on different days of the week. The data of these myoelectric signals are stored in dataset for later offline processing.

#### **2.3 Signal pre-processing**

The segmentation and overlay methods used in this work improved the training efficiency by increasing the number of training samples based on recent work such as [17, 20, 42].

#### **2.4 Feature extraction**

Each captured sEMG signal is subdivided into 200 ms windows. The signal captured by the MYO was obtained at a frequency of 200hz [11, 21]. In order to be analyzed, it is divided every 200 ms, leaving a total of 40 data in each sub-window. Each sub-window has a 50% overlap with the immediately previous window, which allows increasing the number of samples and thus expanding the database obtained. The extraction described here is applied to each of the MYO channels. These data obtained for each of the channels [19], are concatenated horizontally, thus allowing a database to be obtained with 10 data features for each channel and a column with the information on the grasping gestures that are performed.

Different kinds of features extracted are used by different researchers, such as mean absolute value (MAV), root mean square (RMS), autoregression coefficients (AC), variance (VAR), standard deviation (SD), crossover by zero (CC), the length of the waveform (LO), Amplitude of Wilson (AW), slope of mean absolute value (PVAM). Features in the time domain were treated in [42]. These extracted features are used in SVM and in ANN and the raw signals for the CNN classifier.

#### **2.5 Classifiers**

Artificial neural networks Artificial neural networks (ANN) are a nonlinear classifier that simulates brain information processing through a series of weighted nodes, called neurons. Neurons are organized in layers and interconnect with each other to

*Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses… DOI: http://dx.doi.org/10.5772/intechopen.107344*

create a network. ANNs use a nonlinear function of a linear combination of the inputs, where the coefficients of the linear combination are adaptive parameters. The basic model of an ANN will be described as a series of functional transformations. First, M linear combinations of the input variables are constructed *x*1, *x*2, … , *xD* in a way:

$$\mathfrak{a}\_{\mathfrak{j}} = \sum\_{i=1}^{D} \mathfrak{w}\_{ji}^{(1)} + \mathfrak{w}\_{j0}^{(1)} \tag{1}$$

where *j* ¼ 1, … , *M*, y the superscript (1) indicates that the corresponding parameters correspond to the first layer of the network. Parameters *w*ð Þ1 *ji* are weights and the parameters *w*ð Þ1 *j*0 are polarization constants. The magnitude *aj* is named activation, and each activation is transformed using a nonlinear and differentiable activation function [22].

In ANN classifier (**Table 3**), the chosen hyper parameters are highlighted and the variations that were proposed for each of them. Among the hyper parameters that were varied are the weight optimizer, the activation method, the maximum number of iterations and the type of learning.

#### **2.6 Support vector machine**

Support vector machines are a very powerful method used to solve classification problems, it is highly efficient on multidimensional data sets. It consists of defining a hyperplane or decision limit that separates the samples into two groups, where those above the decision limit are classified as positive and those below it, are classified as negative. The fundamental objective is to maximize the margin M or distance between the neighboring samples called support vectors and the separating hyperplane (**Figure 6**) [43].

For the SVM classifier, the following kernels were used: linear, polynomial, Gaussian and sigmoid as shown in **Table 4**. In the linear and Gaussian kernels, the Gamma parameter was used with a value of 0.5. In the polynomial and sigmoid nuclei, the Degree parameter was used: between: 0.5 and 3, respectively. For all nuclei, the regularization constant C was used with values of 0.1, 1, 10 and 100 as recommended in the literature [44, 45].

#### **2.7 Convolutional neural networks**

An CNN is a deep learning algorithm able to collecting an input matrix of size M X N and attributing weights and biases in parallel under the constraints of a predictive

**Table 3.** *Hyper-parameters. ANN.*

#### **Figure 6.**

*Optimal separation hyperplane, for linearly separable classes.*

**Table 4.** *SVM classifier parameters.*

problem [46], resulting in specific features. A convolutional layer performs a dot product between two arrays, where one array is the set of learnable parameters and the other is known as the kernel, producing an activation map, as shown below:

$$G[m,n] = (f,h)[m,n] = \sum\_{j}^{M} \sum\_{k}^{N} h[j,k], f[m-j,n-k],\tag{2}$$

Where:

The input matrix is f and the kernel is denoted as h.

**m** is number of rows in the input matrix

**n** is the number of columns in the input matrix

**j** is the index for the offset in the rows of the input

**k** is the index for the offset in the columns of the input (**Figure 7**)

**Table 5** shows the parameters used in the CNN that were selected for the experiment carried out. Highlighted in the text are the hyper parameters: the batch size and the number of epochs that yielded the best results. The batch sizes were worked with values of 64, 128 and 256 respectively. The CNN architecture has four hidden convolutional layers and one output layer (**Figure 8**).

The first two hidden layers consist of 32 filters of size 8x1 and 3 � 3. The third consists of 64 filters of size 5 � 5. The fourth layer contains 64 filters of size 5 � 1, while the last one is a convolutional layer with six possible outputs with 1 � 1 filters, *Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses… DOI: http://dx.doi.org/10.5772/intechopen.107344*

**Figure 7.** *Architecture of the convolutional neural network used.*

**Figure 8.** *Proposed model of CNN.*

corresponding to the six gestures to classify. Followed by Nonlinear Rectification Units (ReLU) and Dropout layer with a probability of 0.15 to put to zero the output of a hidden unit. Also, a subsampling layer performs maximum pooling in a 3 � 3 window after removal of the second and third layers. Finally, the last convolutional layer is followed by a Softmax activation function (**Table 5**).

#### **2.8 Metrics**

The confusion matrix is used to calculate many common classification metrics. The diagonal represents correct predictions and the other positions of the matrix indicate incorrect predictions. If the sample is positive and is classified as positive, i.e. correctly classified positive sample, and it is considered as a true positive (TP); if it is classified as negative, it is considered a false negative (FN). If the sample is negative and is classified as negative, it is considered true negative (TN); If the sample is negative and is classified as negative, it is considered true negative (TN); if it is classified as positive, it is counted as a false positive (FP), false alarm. The most common metrics are sensitivity (Se), specificity (Sp), which indicate the ability of the CNN to identify hand gestures. Accuracy (ACC) is used to assess overall detection performance and Precision (Pr) is used to measure model quality in posture classification tasks. Likewise, the F1 score (F1) is a measure of the precision of a test, it is the average precision and sensitivity of the classification. It has a maximum score of 1 (perfect accuracy and sensitivity) and a minimum of 0. Overall, it is a measure of the accuracy and robustness of your model. This metric gives information about the quantity that the model is able to identify. In this work, six commonly used evaluation metrics were used: accuracy, precision, sensitivity, specificity and F1 to evaluate the performance of CNN:

$$\text{Accuracy} \,(\text{ACC}) = \frac{\text{TP} + \text{TN}}{\text{TP} + \text{TN} + \text{FP} + \text{FN}} \tag{3}$$

$$Precision\ \left(\text{Pr}\right) = \frac{TP}{TP + FP} \tag{4}$$

$$\text{Sensitivity} \,(\text{Se}) = \frac{\text{TP}}{\text{TP} + \text{FN}} \tag{5}$$

$$\text{Specificity} \,(\text{Sp}) = \frac{\text{TN}}{\text{TN} + \text{FP}} \tag{6}$$

$$F1score\,(F1) = \frac{2^\*TP}{TP + FP + FN} \tag{7}$$

#### **Table 5.** *Hyper parameters tuning.*

*Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses… DOI: http://dx.doi.org/10.5772/intechopen.107344*

## **3. Results**

The analysis in amputated patients is preceded by the great variability of the sEMG signal. **Figure 9** shows this behavior of the sEMG signals for the power grab gesture in amputated subjects. It is observed that the data are very scattered and do not have the same symmetry, either the same mean, either standard deviation between each one of patients.

## **3.1 ANN classifier**

The results obtained with this classifier are shown in **Table 6** in the specific patients. The own database has been used for the test data set with the following configuration: Optimizer: ADAM, activation function: ReLu, L2 regularization with a constant of 0.0001, a constant learning rate of 0.001, with three hidden layers and all layers with eight neurons.

On both data sets, that is, the test and trial data set, the ANN classifier showed an increase in the accuracy metric, especially when increasing the number of training

**Figure 9.**

*Box and Whisker Plot of the sEMG for the power grab gesture in amputees.*

#### **Table 6.**

*Accuracy results with the ANN classifier on the test data set at 100 epochs.*

epochs, passing on average from 76.66% to 100. periods with a minimum of 56.04% and a maximum of 85.77%, respectively. The average accuracy was consistent with the results shown by other authors for this classifier [35, 44, 45, 47].

In **Table 6**, superior results can also be seen on subjects P01, P02, P04, P05, P07 and P10, which is outstanding, since their accuracy was above 80% with this classifier with a standard deviation of 9.23. This low standard deviation indicates that most of the accuracies obtained tend to be clustered close to their mean.

#### **3.2 SVM classifier**

As mentioned above, different kernels were used: linear, polynomial, Gaussian and sigmoid. Likewise, we worked on the regularization constant C with values of 0.1, 1, 10 and 100. On both data sets with the SVM classifier using the RBF and Sigmoid kernels, the best results were obtained when evaluating the accuracy metric, obtaining results up to 80%. in this metric. **Table 7** shows the results obtained with all the kernels.

#### **3.3 CNN Classifier**

**Table 8** shows the comparative results of the CNN classifier evaluated in different patients using regularization techniques such as: early stopping, dropout and batch normalization. Which is a technique that tries to apply certain rules to know when it is time to stop training, so that there is no overfitting to the input data, nor under fitting. The average time required to train each convolutional neural network was 1 hour and 25 minutes. The average time needed to test the network was 15.2 s using a GPU NVIDIA Titan-V, 12 GB RAM HBM2 y 640 Tensor Cores.

**Table 9** shows a summary of the different classifiers by means of the accuracy metric in the different patients. In patients P01 and P02, the best classifier is ANN. Although the accuracy shown by the CNN is acceptable. These patients have a DASH index of 45 and 19, respectively. They also present different levels of amputation: one at 10 cm from the elbow and the other at the level of the wrist. Likewise, they present amputation times of 1 and 30 years, correspondingly. These clinical factors affect the performance of the classifiers.

## **4. Discussion**

From the results obtained, the following points are analyzed. **Figure 10** presents the confusion matrix of each of the classifiers (SVM, ANN and CNN) corresponding to the patient (P03 0 P09), It is observed that both the SVM and ANN show a low number of hits in the different gestures in this patient whose cause of amputation is due to congenital factors that further affect the variability of the signal. Even though only one case is analyzed in this work, this type of behavior has been reported by other research works in patients of this type.

Once again, the performance of the SVM and the ANN is significantly affected. The results of the present work show a significant accuracy rate for the classification of various classes of amputated subjects in comparison with other studies carried out (**Table 10**).

## *Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses… DOI: http://dx.doi.org/10.5772/intechopen.107344*

**Table 7.**

*Results of the metrics with the SVM classifier in the specific patients.*

*Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses… DOI: http://dx.doi.org/10.5772/intechopen.107344*

> **Table 8.**

*Comparative results of the CNN classifier of the different patients at different*

 *epochs.*

#### *Human-Robot Interaction - Perspectives and Applications*

#### **Table 9.**

*Accuracy metric comparison between all classifiers. The values in bold represent the highest accuracy values recorded by each patient in the classifiers.*

#### **Figure 10.**

*Confusion matrices of the different classifiers for the patient P09 (a) SVM (b) ANN (c) CNN using the metric of the accuracy of the data corresponding to the training (session two).*

#### **Table 10.**

*Studies conducted using CNN as an EMG-based hand prosthesis movement classifier in healthy subjects and amputated subjects.*

*Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses… DOI: http://dx.doi.org/10.5772/intechopen.107344*

## **5. Conclusions**

Over the past decade, deep learning and convolutional neural networks have revolutionized several fields of machine learning, including speech recognition and computer vision. Therefore, its use is promising for obtaining better classification indexes of the sEMG signals if the great variation of these is considered in accordance with clinical variables of the amputation, all of which would contribute to closing the gap between the prosthesis market (which requires fast and robust control methods) and the results of recent scientific research in disability support technologies.

The protocol for obtaining sEMG measurements in amputated patients was applied, as well as the extraction and classification of the signal, all of which is consistent with the proposal for the integrated design of the prosthesis. A database of 10 amputated patients according to the 6 defined hand gestures was constructed. The data is publicly available in the repository of the Huila-Corhuila University Corporation (CORHUILA).

The classification accuracy obtained with CNN using the proposed architecture is 80.46%, but the most significant thing is its ability to obtain a higher performance in the classification between subjects in relation to parameters such as length of the remaining limb, years of amputation or disability index, compared with the results obtained by conventional classifiers such as the support vector machine and artificial neural networks.

## **Author details**

Ruthber Rodríguez Serrezuela1 , Enrique Marañón Reyes2 , Roberto Sagaró Zamora3 \* and Alexander Alexeis Suarez Leon4

1 University Corporation of Huila, Colombia

2 Centro de Estudios de Neurociencias y Procesamiento de Imágenes y Señales, Universidad de Oriente, Santiago de Cuba, Cuba

3 Departamento de Mecánica y Diseño (MyD), Tribology Group, Universidad de Oriente, Santiago de Cuba, Cuba

4 Biomedical Engineering Department, Universidad de Oriente, Santiago de Cuba, Cuba

\*Address all correspondence to<EMAIL_ADDRESS>
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## **Chapter 4**


6. Conclusions

In this chapter, we introduce an interesting method, named FLM, which assumes a systematic framework to estimate the local objects and align them to reveal a single global low-dimensional coordinate space. Within the framework, we can fuse together the geometric information learned from different local methods easily and effectively to better discover the underlying manifold structure. Experimental results on both the synthetic and real-world data sets show that the proposed method leads to satisfactory results.


**3.2 Measurement of variables**

A survey method was used for all the variables in the present study. Respondents were asked to indicate their levels of agreement with descriptive statements using a 5 point Likert scale (range, 1 = strongly disagree to 5 = strongly agree).

*Quality management*. To measure the effectiveness of integrated quality management, following the study by Vanichchinchai and Igel [37] and Coyle-Shapiro [38], the present research employed the following seven variables:

*Organizational learning capability*. Based on the study by [28] learning capability was operationalized as top executive commitment, system perspectives, organizational experimentations, and knowledge transfer initiatives.

*Organizational Innovations.* Exploring new ways of things in the organization requires managerial decisions on innovations and reallocation of valued resources toward new processes, products, and services [9, 35]. Following the study by Parjogo et al., innovation performance was operationalized as product/service innovation, performance innovation, and overall organizational innovations.

*Organizational Performance.* Organizational performance can be defined as the desired outcome within organizations. Performance is multi-dimensional and may be measured as such. Following the study by Santos and Brito [39], the present research operationalized performance as employee satisfaction, response to environmental changes, sustainability, customer satisfaction, and projected revenue from new products/services.