Neurological

Communication

Biosci. Biotech. Res. Comm. 11(3): 363-369 (2018)

Formation of the control signals based on application

of the neural network approaches in spine

rehabilitation systems

Nikolay V. Dorofeev*

, Anastasia V. Grecheneva

and Oleg R. Kuzichkin

Vladimir State University named after A. G. and N. G. Stoletovs, 87 Gorkiy St., 600000, Vladimir, Russia

Belgorod National Research University, 85 Pobedy St., 308015 Belgorod, Russia

ABSTRACT

The article substantiates the necessity of correction of control signals depending on the state of the spine and the

movements performed. The generalized structural scheme of the control unit of executive mechanisms in spine

rehabilitation systems is considered. As a result of the operation of the control unit, motor exercises are corrected in

rehabilitation techniques based on the results of modeling the permissible degree of  exure of the spine. An example

of patterns of motor actions is given and a generalized model of motion patterns is described.

KEY WORDS: DIAGNOSTIC, REHABILITATION, NERVOUS SYSTEM, GONIOMETRY, MUSCULOSKELETAL SYSTEM, NEURAL NETWORK

363

ARTICLE INFORMATION:

*Corresponding Author: russia@prescopus.com

Received 12

July, 2018

Accepted after revision 23

Sep, 2018

BBRC Print ISSN: 0974-6455

Online ISSN: 2321-4007 CODEN: USA BBRCBA

Thomson Reuters ISI ESC / Clarivate Analytics USA and

Crossref Indexed Journal

NAAS Journal Score 2018: 4.31 SJIF 2017: 4.196

Online Contents Available at: http//www.bbrc.in/

DOI: 10.21786/bbrc/11.3/3

INTRODUCTION

The ef ciency of modern technical means makes it pos-

sible to implement algorithms for real- time processing

large amounts information. It is lead to increase the

ef ciency and quality of medical systems, in particu-

lar, rehabilitation systems of the spine. The complexity

of developing of this class systems is associated with a

greater risk of harm to the health of the patient due to

an incorrect diagnosis. This risk also includes erroneous

decisions of the rehabilitation system that are incompat-

ible with the life of the patient. The need for adaptation

to the various physiological parameters of the patient

taking into account of injuries to the spine causes

even greater dif culties,

(Sobolev et al., 2017 Kulik,

2017).

The aim of the present work is to improve the qual-

ity of management of rehabilitation exoskeletons due to

the use of neural network algorithms for estimating the

permissible degree of  exure of the yesterday.

364 FORMATION OF THE CONTROL SIGNALS BASED ON APPLICATION OF THE NEURAL NETWORK APPROACHES BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Nikolay V. Dorofeev etal.

THE PROBLEM OF MANAGEMENT OF THE

REHABILITATION SYSTEMS

Currently, the functionality of existing rehabilitation

systems is insuf cient for patients with spine patholo-

gies. This is due,  rst of all, to their limited application

in conditions of low mobility of the patient (fractures,

gunshot wounds, etc.) or the lack of a priori informa-

tion on the patient’s permissible movements (during

rehabilitation) without causing additional harm to his

health. However, the process of rehabilitation is acceler-

ated if the correct load on the pathological parts of the

spine is calculated (Yezhov et al., 2013; Tuktamyshev

& Bezmaternykh, 2014; Vengerova & Solovyova, 2008;

Zubareva, 2011, Maksimova, 2012; Kulik, 2017, Sobolev

etal., 2017).

The control signals for the rehabilitation exoskeleton

are formed on the basis of the patient’s desired move-

ments and are limited by the physiological parameters

and state of the patient.

Arbitrary movements of the patient are formed by

the exoskeleton on the basis of the recorded nerve

(electroencephalography), muscle (electromyography).

Involuntary movements of the patient are formed by the

mechanical (strain gage) signals of the exoskeleton at

different stages of motor processes in various (informa-

tive) areas of the patient’s body, (Grecheneva et al.,

2017).

Problems in recording arbitrary movements of the

patient are the error of the measuring path, the quality

of recognition of informative signals and pathology of

the human neuromuscular system.

So, for example, all movements of the musculoskel-

etal system of a human without pathologies begin in

the central nervous system, namely in the motor zone

of the cerebral cortex. The generated electrical signals

of movement (motion impulses) from the brain through

the spinal cord are transmitted to the peripheral nerv-

ous system along those nerve  bers (motor neurons) that

must cause the necessary contractions of the muscular

system, (Sobolev etal., 2017).

Motor neurons have feedbacks, which receive infor-

mation from muscle  bers, receptors and other sensory

receptors, in order to further coordinate movement and

prevent muscle damage. Since the moment of formation

of an impulse in the cerebral cortex before the move-

ment (contraction or relaxation of the muscles), some

time passes, individual intervals of which are described

in (Sinitskaya & Gribanov, 2014; Zakharova etal., 2012;

Grecheneva etal., 2017).

In general, the movement (especially arbitrary) is the

result of complex neuropsychophysiological processes in

which a plan of motion or reaction to stimuli is formed,

and its constant correction occurs throughout the entire

movement. In addition to the motor zone of the cerebral

cortex, other areas of the brain are involved: the poste-

rior parietal cortex, the limbic system, the cerebellum,

the frontal cortex, etc. (

Sinitskaya & Gribanov, 2014).

When processing and analyzing the signals of motor

neuron activity, attention should be paid to the fact that

useful signals, although cyclic, are not stationary. In

addition, the distribution of the noise component of the

signals is not normal (Zakharova etal., 2012).

Functional changes of any part of the path from the

place of formation of motor signals to the muscle cause

changes in the parameters of motion of the involved

kinematic pairs and the musculoskeletal system as a

whole. Figure 1 shows the averaged electromyograms

obtained (Fig. 2a and 2b) and the dynamics of the devia-

tion angle from the axis of the spine (Fig. 2c and 2d) in

the state of rest of a healthy person (Fig. 2a and 2c) and

a person with a tremor of the back muscles (Fig. 2b and

2d) (Butukhanov, 2009).

Deviations in the electrophysiological signals

involved in the locomotion activity of the musculo-

skeletal system, from normal values for healthy people

manifest themselves in amplitude, phase, shape, and

other characteristics of the signals and depend on the

different concentration of the attention, the accuracy

etc. (Voznesenskaya, 2006; Doronin & Doronina, 2010;

Rakhmilevich et al., 2012; E mov, 2012, Zakharova &

Shemirova, 2016; Shchenyavskaya & Zakharova, 2015;

Zakharova etal., 2016).

The need for high accuracy of recording of the patient

movements is due to possible damage to nerve  bers

and the nervous system as a whole. When a nerve tis-

sue is damaged, a number of processes occur succes-

sively, leading to the death of damaged nerve cells and

the subsequent death of intact ones. According to mod-

ern ideas, the main factors leading to the destruction of

nerve cells are a violation of microcirculation, hypoxia

and ischemia. There is a link between the degree of neu-

ronal damage and the change in the level of the constant

potential and the membrane potential of neurons (Su -

anova and Shapkin 2014; Shanitsin etal., 2013).

Damage to the nervous tissues of the spinal cord

changes the frequency and amplitude of the spinal cord

signals and depends on the amount of pressure (com-

pression) and the degree of damage to the nerve  ber.

Relying on the works on a dependence of the amplitude

of the electrospinogram on the subdural pressure can be

described in accordance with Table 1.

Thus, damage to the spinal cord causes an increase

in spontaneous electrical activity, and in case of signi -

cant damage, further decrease in spontaneous electrical

activity. In this case, the frequency characteristics of the

activity of the spinal cord correspond to the frequency

characteristics of activity of the cerebral cortex, but with

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS FORMATION OF THE CONTROL SIGNALS BASED ON APPLICATION OF THE NEURAL NETWORK APPROACHES 365

Nikolay V. Dorofeev etal.

FIGURE 1. Recording of the averaged electromyogram (a, b) and the dynamics of the deviation angle from the axis of the

spine in the state of rest of a healthy person (c) and a patient with a tremor of back muscles (d)

(c)

(a)

(b)

(d)

Table 1. Dependence of the amplitude of the electrospinogram from subdural pressure

Pressure, mm. gt; 183 250 300 350 400 450 500 550 560

Amplitude, μV 31±9 40±10 52±12 54,5±15,5 64±18 78±20 82,5±15,5 73±15 68±15

smaller amplitude (in the spinal cord). A rupture of the

spinal cord increases the level of the constant potential

and lowers the values on the electro-spinogram of seg-

ments lying below the trauma, and leads to an increase

in electrophysiological changes as the distance decreases

from damage.

Segments lying above the damage zone are charac-

terized by a decrease in the level of the constant poten-

tial and the total amplitude on the electrospinograms, a

decrease in the magnitude of electrophysiological devia-

tions. With pressure on the spinal cord, the level of the

constant potential shifts and the amplitude decreases

on the electrospinograms. The degree of violations in

signals decreases is removed from the site of pressure.

When the pressure on the spinal cord decreases, repolar-

ization occurs and the amplitude increases again on the

electrospinograms. A complete restoration of the level

of constant potential does not occur. Thus, the complex

processing of the values of the level of the constant

potential and electric activity of the spinal cord makes it

possible to evaluate the electrophysiological violations

and functional changes in the spinal cord both in the

injury zone and in neighboring areas (Kulik, 2017).

Thus, the formation of control signals for the rehabil-

itation exoskeleton is determined not only by the chosen

recovery technique, but also by the patient’s vertebral

state (vertebral, interarticulate  uid, interarticulate car-

tilage and neural  ber regions).

CONTROL UNIT FOR REHABILITATION

EXOSKELETON

The task of the control unit for the rehabilitation exo-

skeleton is to generate control signals for the actuators

(Figure 2).

The control unit consists of two functional parts: a

forecast generation unit and a decision block. The fore-

casting unit evaluates the location, extent and likelihood

of damage to the bony, cartilaginous and nerve tissues

of the spine. The prognostic estimation is formed on the

Nikolay V. Dorofeev etal.

366 FORMATION OF THE CONTROL SIGNALS BASED ON APPLICATION OF THE NEURAL NETWORK APPROACHES BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

basis of the information angular model of the patient,

characterizing the permissible deviations in patient move-

ments, the individual spine model describing the geomet-

ric and spatial parameters of the main parts of the spine,

the data on the movement (type, speed, angles and EMG

and EEG data). The performed movements are performed

in accordance with the rehabilitation technique. The algo-

rithm of the control unit is shown in Figure 3.

A lot of work has been devoted to the main dependen-

cies of the behavior of the musculoskeletal system, joints

and their connecting components, pain sensations and

thresholds of perception of pain, for example (Pezhovic

et al., 2003; Pinchuk et al., 2008; Shilko & Ermakov,

2008; Suslov etal., 2008; Babchina etal., 2017; Grech-

eneva etal., 2016; Grecheneva etal., 2017). Formation of

prognostic estimates of damage to the spinal sections dur-

ing motion on the basis of individual models and param-

eters of movements are given in (Dorofeev etal., 2017).

The vector of predictive estimates for each type of

tissue is described by the vector F={L(X,Y,Z), P(X,Y,Z)},

where L is the three-dimensional vector for estimating

the degree of damage, and P is the damage probability.

The change in the projection vector in time F(t) is used

in conjunction with the vector M(t), describing the reha-

bilitation technique (rehabilitation exercise). The vector

M(t)={K(t), C(t)} characterizes the space-time change in

FIGURE 2. The control unit for rehabilitation exoskeleton

FIGURE 3. The algorithm of the control unit

Nikolay V. Dorofeev etal.

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS FORMATION OF THE CONTROL SIGNALS BASED ON APPLICATION OF THE NEURAL NETWORK APPROACHES 367

FIGURE 4. Sample Motion Action Templates

FIGURE 5. The structure of the neural network.

Input signals of such a network are stator current

signals i

S

i

S

of the i-node of the exoskeleton, 

the rotor frequency,



(t) is the vector of spatial

change in the position of the kinematic pairs;



(t),



Un(t) are the vectors describing the change

in electrophysiological parameters during the

time of motion and feedback c from the neural

network output delayed by one step of training

(Delay block), and output signals - signals that

determine the mode of operation of the exoskel-

eton nodes 

S



S

the position of the kinematic pairs of the spine K(t) and

the space-time characteristics of the motion (tempo, fre-

quency, etc.) C(t).

Classi cation of movements occurs on the basis of

pre-formed patterns of motor actions (Figure 4). The

motion pattern can be described by the vector

(3)

where

(t)



is the vector of spatial change in the posi-

tion of the kinematic pairs;

Ib(t), Un(t)



are the vectors

describing the change in electrophysiological parame-

ters during the time of motion (some examples are pre-

sented in [16]);

Pst



is the vector describing the spec-

tral-temporal characteristics (frequency, power spectra,

etc.) of patterns of goniometric and electrophysiologi-

cal signals;



Ppt

is the vector describing the space-time

characteristics of the motion (tempo, amplitude, speed,

acceleration, etc.).

Motion patterns are stored in a database, sup-

plemented for individual characteristics and various

pathologies. Database updates are necessary for auto-

matic learning and retraining of the neural network.

When implementing the system of direct control of

the exoskeleton, it is necessary to have information

about the magnitude and position of the goniometric

vectors and vectors of the stator and exoskeleton rotor

linkage, which are measured by means of various sen-

sors. Unlike vector systems, the direct torque control

system uses only current and voltage sensors and does

not require the use of a speed sensor. However, accu-

rate estimation of the position of the  ux-linkage vector

of each of the exoskeleton servo drives is problematic,

therefore, state observers are often used to determine

the  ux linkage. In the case of a medical rehabilitation

exoskeleton, the patient’s angular model is an observer

of the state, the input of which is measured goniometric

data, the components of the servo vector of the state,

and an output of the general state vector is output. As

an observer, it is proposed to use an arti cial neural

network (Fig. 5).

It is assumed that combining the method of direct

torque control and neural network technologies will sig-

T(t) = {T

(t), Ib(t), Un(t), Pst,Ppt}

 

Nikolay V. Dorofeev etal.

368 FORMATION OF THE CONTROL SIGNALS BASED ON APPLICATION OF THE NEURAL NETWORK APPROACHES BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

ni cantly improve the quality of control of an asyn-

chronous traction electric drive, as well as the robust-

ness of the control system (resistance to changes in the

parameters of the control object), thereby improving the

quality of control and identi cation.

Simulation of the operation of the control unit was

performed on the CT of patient data, which has a curva-

ture of the cervical spine. When the head was tilted to

an angle of more than 59 degrees on average, the patient

experienced pain. In 87% of cases, the head inclinations

were accompanied by a slight crunch in the cervical

region.

Initial exercises for modeling the operation of the

control unit included the inclination of the head by 90

degrees. As a result of the operation of the control unit,

the initial exercises were adjusted, the maximum incli-

nation of the head was 64 degrees (Figure 6).

CONCLUSION

Thus, the developed algorithms of the control unit allow

to correct motor exercises in the rehabilitation technique

for the physiological characteristics of the patient, and

also do not allow the executive mechanisms to make

movements dangerous to health. It should be noted that

the permissible limits for the search for optimal exercises

of the control unit are set by the expert and for auto-

mated work should be automatically determined from

the CT data and the simulation results. These bounda-

ries in the example under consideration were set rigidly,

which was the reason for the discrepancy between the

results of modeling and pain sensations of the patient

being studied.

ACKNOWLEDGEMENTS

The work was supported by RFBR grant No. 16-08-

00992_a

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