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Ahmet YARDIMCI Department of Biomedical Equipment Technology, TBMYO Akdeniz University, Kampus, 07059 Antalya, Turkiye e-mail:yardimci@akdeniz.edu.tr web:www.ahmetyardimci.com. Stroke I.

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Ahmet YARDIMCIDepartment of Biomedical Equipment Technology, TBMYOAkdeniz University, Kampus, 07059 Antalya, Turkiyee-mail:yardimci@akdeniz.edu.trweb:www.ahmetyardimci.com


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Stroke I

  • Stroke is the most common neurologic disease that leads to death and disability in the elderly population. Every year, a significant number of stroke patients survive and are left with significant disabilities. Hemiparesis is the most common cause of disability after stroke, affecting 70 –85% of all patients, and it has been estimated that 60% of all surviving stroke patients may require rehabilitation treatment. It is important to identify effective stroke rehabilitation strategies as the number of stroke survivors and medical costs increase.


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Stroke II

  • Different treatment strategies for the rehabilitation of hemiplegic patients are available today, such as conventional exercise programs, proprioceptive neuromuscular facilitation techniques, muscle strengthening and physical conditioning programs, neurophysiologic approaches, and functional electrical stimulation.

  • Rehabilitation techniques have been more successful in restoring function in the lower limbs than in the upper limbs. The assesment of rehabilitation period is very important to find a correct method for treatment process. The aim of this study find a new way to assesment of hemiplegic patients gate.


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Study

Aim of the study

Classification of Hemiplegic Patients

  • Methods

  • Fuzzy Logic ?

  • Neuro Fuzzy (ANFIS) ?

  • NN ?

Stage 1.

Discrimination of subject situation

Healthy?Patient?

Stage 2.

Classification of patients’

Brunnstrom stages III, IV, V, VI


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Software

  • Matlab V6.5, Mathworks

  • FuzzyTECH V5.54d Professional edition, Inform


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Data gathering

Information about Measurements

Subject: 7 healthy elderly subjects and 26 hemiplegic patients

Parameter: Waist acceleration

Condition: Walking on corridor

Instruction: With orthosis and/or cane (hemiplegic patients)

Sampling: 1024Hz



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What we do to reach our aims?

  • Find significant amplitude features of signals,

  • Find symmetry features of signals,

  • Decide to inputs of classification system,

  • Decide to rule blocks,

  • Find suitable rules for all conditions ( consult a specialist),

  • Test the system with your own data,

  • Test the system with blind approach (find test data which is not included the your own data),

  • Turn back if the system response does not satisfy you,

  • Check all steps again from 3 to 8.


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Three orthogonal acceleration signals from a normal healthy subject walking at a normal speed*

*Evans AL, Duncan G, Gilchrist W. Recording accelerations in body movements. Med.& Biol. Eng. & Comput., 1991, 29, 102-104


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Description of accelerometer signal subject walking at a normal speed**

*Bussmann JBJ, Damen L, Stam HJ. Analysis and decomposition of signals obtained by thigh-fixed uni-axial accelerometry during normal walking.Med.Biol.Eng.Comput.,2000, 38, 632-638


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Temporal events in stroke hemiparesis subject walking at a normal speed**

Features

  • Walking speed

  • Stride period

  • Cadence

  • Stride length

  • Stance period

  • Swing Period

  • Stance/swing ratio

  • Double support

  • Stance symmetry

  • Swing symmetry

* Sandra JO, Richards C.Hemiparetic gait following stroke. Part I : Characteristics. Gait & Posture 4 (1996) 136-148


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Temporal events in stroke hemiparesis subject walking at a normal speed**

Features

  • Walking speed

  • Stride period

  • Cadence

  • Stride length

  • Stance period

  • Swing Period

  • Stance/swing ratio

  • Double support

  • Stance symmetry

  • Swing symmetry

All of them temporal gait variables!..

Measurement and analysis of those variables did not further characterize the pathologic nature of locomotion in hemiplegic patients. Because most of the relevant temporal information in hemiplegic gaits is included in the measurement of walking speed.


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Some measurable features of gait subject walking at a normal speed*

  • Walking speed m/sn

  • Cadence step/min

  • Step length m

  • Double step length m

  • Step time difference

  • (Mean step time= Mean step length/ walking speed STD= MSTL - MSTR )

  • Double step time difference

  • (Double mean step time= double mean step length/ walking speed DSTD= DMSTL - DMSTR )


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Some important notes from literature subject walking at a normal speed*

Prior studies revealed that temporal variables of hemiplegic gait, (walking speed and symmetry of the swing phases) are significantly related to motor recovery as classified according to defined stages.

Hemiplegic patients, even those with good motor recovery, by comparison all walked much more slowly. Walking speed was related to the clinical status of the patient, being progressively slower as the motor deficit became more severe.

Walking speed is an important temporal variable of hemiplegics gait, as reported by many investigators.

There are several algorithms to compute step times and quantifying symmetry.

(Aminian et al., Sadeghi et al., Robinson et al., Ganguli et al., Vagenas et al.)


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Symmetry and Laterality Quantification subject walking at a normal speed**

Gait symmetry has been defined as a perfect agreement between the actions of the lower limbs.

A way of categorizing different means of determining whether or not symmetry and laterality exist between the lower limbs using indices and statistical analysis.

*Sadeghi H, Allard P, Prince F, Labelle H, Symmetry and Limb dominance in able-bodied gait:a review. Gait and Posture 12 (2000) 34-45


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Symmetry and Laterality Quantification subject walking at a normal speed**

In pathological gait, marked differences have been noted between the affected and unaffected limbs.

Asymmetrical properties were reported for 34 gait variables in a group of 31 hemiplegic subjects*. The gait of hemiparetic patients was characterized by slower velocity and more asymmetry as they swayed more laterally on the unaffected leg compared to healthy persons.

*Sadeghi H, Allard P, Prince F, Labelle H, Symmetry and Limb dominance in able-bodied gait:a review. Gait and Posture 12 (2000) 34-45


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To determines asymmetries: Symmetry Index (SI) subject walking at a normal speed*

1

Robinson RO, Herzog W, Nigg BM. Use of force platform variables to quantify the effects of chiropractic manipulation on gait symmetry. J Manipulative Physiol Ther 1987;10:172-6

2

Ganguli S, Mizrahi J, Bose KS. Gait evaluation of unilateral below–knee amputees fitted with patellar-tendon-bearing prostheses. J Ind Med Assoc 1974;63(8):256-9

R= XR / XL

3

Vagenas G, Hoshizaki B. A multivariable analysis of lower extremity kinematic asymmetry in running. Int J Sports Biomech 1992; 8(1):11-29

4


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Lets see the differences between the SI’s in a sample problem

A

B

1

2

3

SI(A)= -%50

SI(B)= -%33

SI(A)= 0,6

SI(B)= 0,71

SI(A)= -%40

SI(B)= -%28

4

SI(A)= %25

SI(B)= %16

%0 %100

Symmetry Asymmetry


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Hemiplegic gate signals problem

Healthy

ST6

ST5

ST4

ST3

Anteroposterior Acceleration signals


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Feature of signal problem

Amplitude of signal A

Step1 time S1

Step2 time S2

Slope of signal? SL

Two steps time T

Absolute Step Difference ASD=S1-S2

Rate of Step Difference RSD=ASD / T


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Detection algorithm* problem

After find the phl ptl , phr, ptr parameters;

Duration of each gait cycle

gc(i) = phr(i+1) – phr(i) 1 i N

Left stance

LS(i) = pt1(i) – phi(i)

right stance

RS(i) = ptr(i) – phr(i)

Left double support

LDS(i) = ptl(i) - phr(i)

Right double support

RDS = ptr(i) - phl(i)

Aminian K, Rezahhanlou K, Andres E, Fritsch C, Leyvraz PF, Robert P. Temporal feature estimation during walking using miniature accelerometers: an analysis of gait improvement after hip arthroplasty. Medical&Biological Engineering& Computing, 1999 Vol.37,p.686-691


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Anteroposterior Step times problem

Step time comparison









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Fuzzy logic based classification (Range, max, min)

A

N

A

L

Y

S

I

S

Temporal Features of Gait Signals

Symmetri Features of signals

Physiological Features of Subject

Amplitudes of signals

?


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ZLR (Range, max, min)

YVR

S2

XAST

XASI

S1

XAR

XAM

Preferred features of acceleration signals



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System Structure (Range, max, min)

XAM

XAR

Signal Peak Features

81 rules

YVR

ZLR

Main Decision Rule Block

25 rules

XASI

Signal Symmetry Features

9 rules

XST


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Fuzzy logic system diagram (Range, max, min)


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Membership Functions of 1 (Range, max, min)st Rule Block

MBF of XAM

MBF of XAR

MBF of ZLR

MBF of YVR


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Membership Functions of 2 (Range, max, min)nd and 3rd Rule Blocks

MBF of XASI

MBF of XAST

MBF of Classification


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Rules (Range, max, min)

1st RB

81 rules

2nd RB

3rd RB

25 rules


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Test (MoM) (Range, max, min)


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Test Results (Range, max, min)

Healthy

ST6

ST5

ST4

ST3

XAM,XAR,XASI,XAST,YVR,ZLR,Classification,__flags_

-0.2446,1.555,0.031,0.4183,1.211,1.0468,1,0

-0.157,0.7305,0.1575,0.6081,1.0172,0.9236,1,0

-0.1128,0.6394,0.101,0.6157,0.8952,0.6339,3,0

-0.047,0.5791,0.2849,1.06,1.2105,0.7844,3,0

0.025,5846,0.5633,2.39,0.8012,0.6479,5,0

Healthy

Healthy

ST3

ST3

ST5

+

+

+

ST4

+


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Test (Range, max, min)s

1

2




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PMCC (Pearson product-moment correlation coefficient) results

The Pearson coefficient is a statistic which estimates the correlation of the two given random variables. The linear equation that best describes the relationship between X and Y can be found by linear regression.

This equation can be used to "predict" the value of one measurement from knowledge of the other. That is, for each value of X the equation calculates a value which is the best estimate of the values of Y corresponding the specific value. We denote this predicted variable by Y'.

Correlation coefficient is 0.85


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Statistical Results results

1

Successful discrimination rate of Patients

Successful discrimination rate of Healthy Subjects

100%

100%

2

Successful classification rate of hemiplegic patients

ST6→ 66%

ST5→ 66%

ST4→ 66%

ST3→ 46%

Good results for discrimination of subjects as healthy and patient!

Low success for classification of ST3 patients.

This study has shown that it is possible to discriminate subjects as healthy or patients with fuzzy logic approach. But successful classification of patients, due to the unstable behaviors of signals, is rather difficult than discrimination of subjects for fuzzy approach.


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Future works results

  • Check the wrong results to find whether a failure in system.

  • Check all the rules.

  • If necessary do some fine arrangements on rules and membership functions.

  • Expand the system by adding new inputs.


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Future works results

XAM

XAR

Signal Peak Features

81 rules

YVR

ZLR

Main Decision Rule Block

?

XASI

Signal Symmetry Features

9 rules

XST

Age

Height

Subjects Physiological Features

?

Weight

Gender


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Future works results

  • Examine the literature on detection algorithms.

  • Develop an algorithm for detect precise moments and compute temporal parameters.

  • Make new measurements with using footswitch equipped shoe.

  • Try the neuro-fuzzy methods to produce membership functions and rule base from the data records.

  • Compare results with prior studies.