CONTROL FOR ELECTRICAL NEUROMUSCULAR

STIMULATOR USING FUZZY LOGIC

Trainning gait in paraplegics

Leonardo Rodrigues da Silva, Percy Nohama

Depto. de Eletrônica, CPGEI, Centro Federal de Educação Tecnológica do Paraná (CEFET/PR), Curitiba, Brazil

Keywords: Electrical stimulation, fuzzy logic, closed loop, electrical goniometers, spinal cord injury, artificial gait.

Abstract: This article presents a personal computer-based control system for an electrical stimulator using fuzzy logic.

The input signal comes from a goniometer and the output is the stimulation level to be applied in the muscle

of the patient. By this way, that control system is made for the therapist that just specifies the desired joint

angle. The movement that the patient will execute can be imitated from a person with normal movements,

storing his or her joint’s angles during the execution of some task, and later reproducing it in the person

without the voluntary movements. Such movements will be more proper of a human than a planned

execution of a computational system, which the movement is structuralized by means of vectors, angles and

times placed of supposed form.

1 INTRODUCTION

The electrical neuromuscular stimulation is the most

adjusted technique to be applied in cases of

atrophied muscle due to the lack of movements

caused mainly by spinal cord injury or vascular

encephalic accident because it can cause

development and increase of muscular strength, even

in those without a voluntary contraction (Quevedo at

al., 1997).

During and after the development of muscles, it

is possible to make the body get used to certain

movements and create a sensorial engram, so that

lately it comes to execute such movements in an

intuitive way, without the necessity of electrical

stimulation.

A closed loop electrical stimulation system using

goniometers as input is described by Quevedo and

Cliquet Jr. (Quevedo & Cliquet Jr., 1995), who

presented an ideia of neural networks-based control,

which presented the disadvantage of needing to

much time for the network’s training for each type

of movement.

In 1998, Zagheni (Zagheni, 1998) developed a

multi-channel computer controlled neuromuscular

electrical stimulation system which by operating in

open loop and containing 8 analog inputs that allow

processing of electrical physiologic signs,

consequently, served as feedback inputs and, thus, to

control the stimulation parameters automatically.

This article presents a new strategy of control

applied to Zagheni’s stimulator, based in fuzzy

logic, aiming to improve the control of the

stimulation and to facilitate its use in the engram’s

development. This control system was developed

with the objective of monitoring the position of the

stimulated member. Using as input, the signals from

electrical goniometer coupled in the joints under

control and the target position, are obtained as

output the amplitude of the stimulatory signal to be

applied on the muscle, via transcutaneous electrodes.

The in-vivo tests were applied to 4 paraplegic

volunteers, controlling the knee extention and hip.

During the tests two goniometers were used,

controlling both knees independently or one knee

and the hip, in this case being applied the same

stimulation to both legs, standing the volunteer up.

According to W. Dalton Dietrich, Scientific

Director of the The Miami Project to Cure Paralysis,

at the University of Miami School of Medicine,

there are 5 steps for the cure to spinal cord injury:

1 - patient selection and pretraining; 2 - surgical

intervetions and neuroprotection; 3 - tranplantation /

regeneration; 4 - overcoming barriers to regeneration

and 5 - rehabilitation (Dietrich, 2005). The system

presented in this article could be used in steps 1 and

266

Rodrigues da Silva L. and Nohama P. (2005).

CONTROL FOR ELECTRICAL NEUROMUSCULAR STIMULATOR USING FUZZY LOGIC - Trainning gait in paraplegics.

In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics, pages 266-269

 SciTePress

2 PROBLEM FORMULATION

The possibility of the spinal cord injury

rehabilitation could come in a near future. Some

researches are already bringing concrete results in

guineas pigs, like the implantation of stem cells

(Rosano et al., 1999) and cells of Schwann (Oudega

et al., 2001), making a backup of the communication

between the brain and the remaining portion of the

body below of the part affected by the injury

possible.

As for the integral rehabilitation of the disabled

person, we have necessarily to pass through the 5

steps described by Dalton Dietrich (Dietrich, 2005).

So, passing through the pre-training, where it is

needed to fortify the muscle and to verify the

possibility of muscles and bones to support the body.

Also after that possible regeneration of the spinal

cord, we go through a rehabilitation process by

which the not enough trained muscle passes through

the exercising process so that it can fulfill its role. In

these two stages, the use of the electrical stimulator

with closed loop control is important.

Figure 1: Pacient with electrical goniometer to control

knee joint

The feedback signal of the system is gotten from

one goniometer that measures the joint angle of the

inferior limb under control (fig. 1). The computer

has stored the path that the joint must execute. From

these two pieces of information, the system controls

the amplitude of the stimulation so that the limb

stays in the desired position in an instant of time

determined by the operator of the system. The

control system works according to objectives. The

therapist specifies the movement that the member

must execute, specifying the angle desired for each

moment.

The training for development of sensorial

feedback was applied in cats for the retrain of the

march. A full spinal cord injury was provoked at the

level T13, the locomotion capacity returned

gradually and in 1 to 3 weeks the cat started to have

the locomotion movements of posterior legs, being

able to hold its body’s weight (Rossignol et al.,

2000).

The theory of sensorial feedback is based on the

theory of the neural plasticity of the spinal cord,

from which it was concluded that, among other

things, the spinal cord drive the step processing

complicated sensorial information of the peripheral

nerves (APA, 1998). The training and the learning

are fully specific, because if a cat is trained to take a

step, it will take a step; if it is trained to stand still, it

will do so, but it will not take a step (Rossignol et

al., 2000).

The system can be applied to human beings with

problems in the central nervous system (CNS) so

that they can develop new abilities as it has been

demonstrated by Calancie (Calancie et al.), who says

that there is a central pattern generator (CPG)

located in the spinal cord for the generation of the

step. According to that, a person without voluntary

movements, after spending intensive physiotherapy

sessions walking, even lying down, kept the pattern

of gait, showing that the body had learned. As a

result, we can assume that after the stimulation of

the patient’s muscle to walk, he or she must learn to

do this alone.

3 PROBLEM SOLUTION

So that the system could be tested before its in-vivo

application, a muscle simulator was developed. It

gave some evaluation of the response for a real

stimulation. The simulator takes into consideration

the angle of the joint, the applied force and the

amount of fatigue beyond the amount applied

stimulation (Silva & Nohama, 2000,1)(Silva &

Nohama, 2000,2).

The system was initially developed in the

Simulink of the Matlab (Matlab, 5.2.0). Due to its

assembly, tests and alterations easinesses, besides

providing one easy interaction with the logic fuzzy

blocks used for the control and simulation system.

To establish the artificial gait using fuzzy logic,

first, the control of the knee extension was

developed with the patient sat, attempting an

CONTROL FOR ELECTRICAL NEUROMUSCULAR STIMULATOR USING FUZZY LOGIC - Trainning gait in

paraplegics

267

effective control of quadriceps muscles contraction

and, thereby, control the movement of the member.

The Zagheni’s software for the electrical

stimulator was developed using Visual C++.

Currently, we have upgraded that software with the

fuzzy controller algorithm in two channels of input

and sexteen channels of output, eight for each input.

In the present moment, it allows two goniometers

connected. Therefore, the knees joint and hip were

chosen for tests, made stimulating and controlling

the Rectus femoris, Gluteus maximus and Vastus

lateralis muscles, and used Gastrocnemius to help

stabilising when the volunteer stands up.

The triangular membership functions of the fuzzy

system had been chosen by being the most

commonly employed, being able to be adjusted later.

The controller was designed with an input called

angle input with 3 membership functions, another

input called the difference between active angle and

the desired one, with 5 membership functions and

the output is the difference of stimulation with 5

membership functions (Silva & Nohama, 2000,2).

In the output of the system fuzzy we had the

value to be calculated from the value currently

applied to obtain a new amplitude. To become the

generic system, at first model, all the values are

normalized (between 0 and 1), because the majority

of the parameters vary from patient to patient, thus,

the data needs to be processed for the input after an

output of the fuzzy system.

We did tests in-vivo to verify the necessity for

adjustments in membership functions. In the in-vivo

application we feel the necessity to establish a

minimum value of stimulation, because there is, in

each muscle of each patient, a sensibility threshold,

a contraction threshold value (when the muscle starts

to contract) and a maximum value of stimulation.

Above that maximum value, there is the risk to

cause damage to the muscle.

In one test a fixed angle of, more or less, 45

degrees was used as target of the member; the

member was initiating the motion with an angle of

more or less 85 degrees (fig. 1 and 2). That angle

was chosen due the difficulty to be kept during

electrical stimulation.

In figure 3, we have the amount of stimulation

applied to the muscle, we can notice the

compensation that the system makes due to the

fatigue that the muscle is submitted to during the

stimulation, also it is important to place that during

these tests, at any moment the stimulation arrived in

the maximum defined for that muscle, in that

patient. It had a small variation above and below the

objective angle that was left on purpose, because,

during our daily activity, the movements are not

totally precise, so an alteration of stimulation for

small natural variations in the contraction wasn’t

necessary.

The noise present in the input signal will be

filtered in the future.

So that the movement can be more natural and

can have the possibility of a bigger gamma of

movements, with more easiness of configuration, it

is in final phase of development be read the angles

of the joints from a person with normal movements

for posterior reproduction in one patient, through

electrical stimulation. With this feature, the

movement pattern is easier to be constructed than

that one through the planning of computational

systems, where related movements are structured by

means of vectors, on which angles and times are

placed in the way they’re supposed to. In this way,

the movement is better assimilated and later

reproduced through the process of the Central

Pattern Generator demonstrated by Calancie

(Calancie et al.) and also by already existing a

previously stored engram, when the person had the

normal control of its movements, helping the

rehabilitation work if the cure of spinal cord injury

had been discovered.

Figure 2: Leg’s angle during the electrical stimulation

Figure 3: Amount of stimulus applied at Rectus femoris e

Vastus lateralis

4 CONCLUSION

In the continuation, the number of goniometers will

be expanded to be possible doing a gait at a

paraplegic. It needs to make a better design of

goniometers to be better adjusted to each joint.

Assembling a major number of goniometers,

allows us to test more complex movements. The

loop of control is already prepared and software will

need small implementations making possible for the

patient to execute movements like walk, ride a

bicycle or go up stairs, depending only on the

correct pattern of the angles to be executed. For the

future, an input system to acquire the intention of the

patient can be installed (Kostov et al., 1995), to

ICINCO 2005 - INTELLIGENT CONTROL SYSTEMS AND OPTIMIZATION

268

make the system slave of patient volitions and not

only of what is pre-determined by it. The

improvement of this technique will represent a great

advance in the area of functional electric stimulation

for paralyzed members, making the practical

application of this therapeutical feature easier.

These innovations bring a new age in the control

of neuromuscular electrical stimulator, allowing a

person without the voluntary control of its

movements to imitate another one who have full

movements. It could give him or her a much more

worthy and calm life. By using techniques of

implantation of electrodes, which already exist,

through a surgery, apparent wires will no longer be

necessary. And with the movements soften than ones

gotten through the systems of control currently in

use, the deficiency can be unnoticed.

REFERENCES

A. A. F. Quevedo, A. E. Patla, and A. Cliquet Jr., A

Methodology for Definition of Neuromuscular

Electrical Stimulation Sequences: An Application

Toward Overcoming Small Obstacles., IEEE

Transactions on Rehabilitation Engeneering, Vol. 5,

No. 1, pp. 30-39, 1997.

A. A. F. Quevedo, and A. Cliquet Jr., A paradigm for

Design of Closed Loop Neuromuscular Electrical

Stimulator Control Systems., Artificial Organs,

Vol. 19, No. 3, pp. 280-284, 1995.

A. L. Zagheni, Sistema EENM Multicanal Controlado por

Computador para Aplicações em Locomoção

Artificial. Dissertação de Mestrado. CPGEI / CEFET -

PR. Curitiba, 30 abr 1998.

W. D. Dietrich, http://www.miamiproject.

miami.edu/miami-proje t/5steps.htm, 10 apr 2005.

C. Rosano, E. Felipe-Cuervo, and P. M. Wood.

Regenerative potential of

adult O1+ oligodendrocytes. Glia. Vol. 27, pp.189-

202, 1999.

M. Oudega,S. E. Gautier, P. Chapon, M. Fragoso, M. L.

Bates, J. M. Parel and M. B. Bunge, Axonal

regeneration into Schwann cell grafts within

resorbable poly(a-hydroxyacid) guidance channels in

the adult rat spinal cord. Biomaterials, Vol. 22, pp.

1125-1136, 2001.

S.Rossignol, at al., Locomotor plasticity after central tracts

or peripheral nerve lesions in the cat, Symposium on

Spinal Cord Plasticity, Abstracts, Nov 1998. Apud

http://graulab.tamu.edu/Spinal/ Plasticity.html, jul.

2000.

APA. Locomotor Training May Lead the Paralyzed to

Walk. American Paralysis Association, 35 ed.,

Summer 1998,

http://paralysis.apacure.org/publications/ pir35-

summer98.html#LocomotorTraining, 06 ago 1999.

B. Calancie, et al., Involuntary stepping after chronic

spinal cord injury. Evidence for a central rhythm

generator for locomotion in man. Brain, Vol. 117,

Issue 5, pp. 1143-1159.

L. R. Silva, P. Nohama, Controle de Eletroestimulador em

Malha Fechada e Simulação de Resposta Muscular

Utilizando Lógica Fuzzy, XVII Congresso Brasileiro

de Engenharia Biomédica, set 2000.

L. R. Silva, P. Nohama, Estimulador Elétrico

Neuromuscular empregando Lógica Fuzzy, Iberdiscap

2000 - Congresso Iberoamericano 3° de Comunicación

Alternativa y Aumentativa y 1° de Tecnologías de

Apoyo para la Discapacidad, Madrid, 2000

Software Matlab, version 5.2.0, The MathWorks Inc.

A. Kostov, B. J. Andrews, D. B. Popović, R. B. Stein, and

W. W. Armstrong, Machine Learning In Control Of

Functional Electrical Stimulation System For

Locomotion, IEEE Transactions on Biomedical

Engeneering, Vol. 42, No. 6, pp. 541-551, 1995.

CONTROL FOR ELECTRICAL NEUROMUSCULAR STIMULATOR USING FUZZY LOGIC - Trainning gait in

paraplegics

269