Recognition of Basic Hand Movements Using Electromyography

8/15/2019 Recognition of Basic Hand Movements Using Electromyography

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Page 2

Introduction

Application using EMG signalThe use of the EMG signal is quite prevalent in a wide range of applications

!ne of the practical applications of the EMG signal is the use for control

e"os#eleton limps of $od%& such as hand It is more comforta$le for a hand

amputee to wear a glove that includes the EMG electrodes than using the

rather promising Electroencephalograph% 'EEG( electrodes in the area of thehead This issue is of significant importance for using such a s%stem in a dail%

$asis

The hand controlled from a single& highl% intelligent control s%stem that

uses two sensors to open and close the hand

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E"perimental Procedure

AI: to identif% si" $asic movements of the hand!pherical ')(:

holding an elastic $all

"ylindrical '*(:

holding a glass of water#ip 'T(:

the touch of the thum$ to the inde"

Palmar 'P(:

holding a pen

$ateral '+(:

holding a credit card

%oo& ',(:

holding the handle of -+ water $ottle

EA!'REE(#!:Five health% su$jects 'two males and three females( of the same age appro"imatel% './

to ..0%ear0old( were as#ed to repeat the si" $asic movements 3) times* using t+o

sensors for acquiring the data

)ampling rate: 1// ,2& 3utterworth 3and Pass filter 4101// ,2& 5otch 6ilter 1/ ,2

The total time for each measurement was 5 seconds






Page ,

E-. Empirical ode -ecomposition

•

EMD provides a novel adaptive method for anal%2ing non0linearand7or nonstationar% signals It decompose the signal into a num$er

of IM6s






Page /

6eatures E"tractionMEA58ch4

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MA948ch4 'Mean A$solute value4(

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4< E"perimental )etup5o filters0 1*90 Total Data)et

! Rip , Ib0 Adaboost Rand4orest

Accurac% ==11> ?@> .=> 1=> @444> @.11>

I$B: for several values of #: *onfusion matri" ;and6orest:





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C-: )everal +evels of pruning

*onfidence 6actor/4 *onfidence 6actor4

;elativel% high level of accurac% in

correspondence of *6/.

'standard value in e#a(

Moreover& an higher value of *6

causes an increase of

computational time





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.< E"perimental )etup5o filters0 Manual 1*9:4 6old per )u$ject

Ib0 Random 4orest

Accurac% ..--> .@>

ale1 4---> 111>

ale2 > 4=44>4emale1 .==?> ==?>

4emale2 -/> ?..>

4emale3 ....> 1>

9er% low levels of accurac%F






< E"perimental )etup

5o filters0 4.*9 for each )u$ject/ trials for each movement for each su$ject '= movements(: 4. folds

This time the $est classifier

was found to $e I$= '$etter

than ;andom 6orest(

Paper results:





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Attri$ute )election: 4/ most significant 6eatures:

"fs!ubsetEval Info7ain P"A

;esults:

Attribute!elector

"lassification Results

I80 Rand4orest rip , ! Adaboost

P*A 80.67% 88.11% 62.11% 65.44% 57.33% 76.55%

*fs

88.56% 90.78% 79.44% 79.44% 64.66% 89.89%

InfoGain 87.66% 91.66% 80.77% 84.57% 62.11% 92%






*onclusions Attri$ute )election vs 5o Attri$ute selection






)catter Plot








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Recognition of Basic Hand Movements Using Electromyography