Finger Movements Recognition Using Naive Bayes Algorithm in

Frequency Domain

Daniel Sutopo Pamungkas and Novi Dharmayani

Dept. of Electrical Engineering Politeknik Negeri Batam, Batam, Indonesia

Keywords: EMG, Myo Armband, Naïve Bayes, Recognition.

Abstract: Rapid technological developments have led to various innovations to overcome existing problems, one of

which is prosthetic hands to facilitate daily activities. The field of biomechanics studies and applies the

concepts of technology, treatment, and diagnosis related to human activities, resulting in new technology in

the form of electromyography (EMG). EMG signals are signals originating from human muscles when they

contract or relax. This study aims to identify the Myo Armband sensor's movement pattern of the human

fingers. The Myo Armband sensor is placed on the forearm of the subject's right hand to receive signals from

the EMG. The data obtained will be converted to the frequency domain using FFT, then 70 percent of the data

from the EMG signal is used as training data to get the results of each movement. The training results will be

tested using 30 percent of the EMG signal data and classified using the Naive Bayes algorithm. The study’s

results show that this system manages to identify the gesture around 80%.

1 INTRODUCTION

Rapid technological developments have led to

various innovations to overcome existing problems,

one of which is prosthetic hands. Prosthetic hands can

facilitate daily activities such as picking up or moving

items. In the medical field, there is a field of

biomechanics that studies human movement. The

area of biomechanics studies and applies the concepts

of technology, treatment, and diagnosis related to

human activities to produce new technology in the

form of electromyography (EMG) (Pamungkas et al.,

2020).

EMG signals come from human muscles when

they contract or relax. EMG has been widely used and

applied as a signal control system in various

applications of the Human Machine Interface because

it can be used to check the condition of muscles and

nerve cells to help detect disturbances in nerves or

muscles.

To detect this type of signal, there are several

methods that researchers use. The first method uses a

needle to place the EMG sensor inside the skin (Dy et

al., 2021). The second method is the sensor location

on the skin’s surface to sense the EMG signal

(Pamungkas et al., 2020). The second method is more

convenient, even though the signal’s noise is higher.

Three methods can be used to apply this signal for

recognizing the pattern of the movement of the

fingers. There are time domains (Pamungkas et al.,

2020), (Esa et al., 2018), frequency domains

(Andrean et al., 2019) (Yousif et al., 2019), and a

combination of time and frequency domains

(Pancholi & Joshi, 2020)(Guo et al., 2004). The

frequency domain tends to obtain higher success in

recognizing the movements. However, this method is

slower than the time domain method because the

computation cost of the frequency domain is higher

(Nossier et al., 2020).

Moreover, several algorithms have been used to

identify the movement of the hand. Namely, Support

Vector Machine (SVM) (Dela et al., 2022), Neural

Network (Andrean et al., 2019), Naïve Bayes, and K-

Nearest Neighbours (K-NN) (Pamungkas et al.,

2020). Most of the research enables us to identify the

movement of the fingers from around 60% to 90%.

The naive Bayes classification method is a simple

probability classification method using a set of

probabilities. In this study, the frequency domain

features are used. The surface EMG (sEMG) sensors

are utilzed. Myo armband is an sEMG type, and it has

eight EMG sensors. While the Naive Bayes algorithm

is used. This method is expected to obtain the results

better than others.

Pamungkas, D. and Dharmayani, N.

Finger Movements Recognition Using Naive Bayes Algorithm in Frequency Domain.

DOI: 10.5220/0011880100003575

In Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2022), pages 777-780

ISBN: 978-989-758-619-4; ISSN: 2975-8246

777

The methods of the study are presented in the

following section. Afterward, the results of the

experiments are provided in the results and discussion

section. Finally, in the last section, the conclusions

are delivered.

2 METHODS

In this study, we utilized a Myo armband as a device

to detect the EMG signal. Those signals are processed

in a PC with core i5 and 8 Gb RAM, as seen in figure

1. The signals from the sensors are in the time domain

and need to be transformed into the frequency

domain. The Fast Fourier Transform (FFT) method is

used for this process. Five features of the frequency

signals are used for this study. Namely mean

frequency, median frequency, peak frequency, mean

power, and total power of the signal.

Myo armbands are used in the upper right hand of

the subject. Figure 2 shows that the subject wears the

device. The subject is a right-hand male without

disorders in neurological and muscular. The location

of sensor no 4 is approximately in the back of the

hand’s middle (see figure 2). To obtain the signals,

the subject is sitting in a chair. His hand is placed on

the desk in front of him. At the same time, the subject

moves their fingers. Five hand gestures are conducted

simultaneously in this study, as shown in Figure 3.

For every pose, the subject start by opening all the

fingers and bending the finger, then hold for 5

seconds and goes back to the initial position. For each

pose, the subject repeated ten times.

There are two phases for this system to identify

the movements of the fingers. First is the training

phase, then the test phase of the system. From each

pose, seven data are used for the training data, while

three other data are used for the test. The flow chart

of both steps can be seen in Figure 4. The raw signals

are obtained when the subject moves his finger.

Signals EMG from the sensor on the subject are

transformed to the frequency domain using the FFT

equation next step is to acquire features of the

transformed signal. The training data are obtained in

the training phase and then used in the test phase.

Figure 1: Diagram block of the identified system.

Figure 2: Subject wears the sensors.

Figure 3: Pose of the hand.

Figure 4: The flow chart of the system.

3 RESULTS AND DISCUSSION

The EMG signals are enabled to collect from the

sensor. Figure 5 shows the combination EMG signal

from all the sensors in the time domain for the thumb

finger pose. In comparison, each sensor‘s signal can

be seen in Figure 6 for the same pose.

After each finger's raw EMG data signal has been

input, FFT converts the time domain into the

frequency domain. The FFT data EMG signal is

iCAST-ES 2022 - International Conference on Applied Science and Technology on Engineering Science

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shown in Figure 7, where the X axis is the frequency,

the Y axis is the amplitude, and the FFT of each

sensor data is shown in Figure 8. Then the EMG data

signal is extracted using five extraction features:

mean frequency, median frequency, peak frequency,

mean power, and total power contained in Table 1.

Figure 5: Graph of EMG raw data signal.

Figure 6: Graph of EMG Raw data each sensor signal.

Figure 7: Graph of FFT result of EMG signal data.

Figure 8: Graph of FFT result of each sensor data EMG

signal data.

Table 1: Features of the thumb pose.

Mean

Freq

Median

Freq

Peak

Freq

Mean

Power

Total

Power

Sensor

0.433

-1695 386 -2.9973 -3390

Sensor

0.429

-1695 238 -2.9973

-3.39E

+ 03

Sensor

0.455

-1695 485 -5.9947

-6.78E

+ 03

Sensor

0.422

-1695 481 -2.9973

-3.39E

+ 03

Sensor

0.394

-1695 407 -1.9982

-2.26E

+ 03

Sensor

0.449

-1695 619 -3.9965

-4.52E

+ 03

Sensor

0.456 -1695 513 -3.9965

-4.52E

+ 03

Sensor

0.454

-1695 759 -3.9965

-4.52E

+ 03

Sensor

0.567

2.06E +

505 2.9978

4.11E +

Sensor

0.528

2.06E +

899 6.9949

9.60E +

Sensor

0.742

2.06E +

277 0.9993 1371

Sensor

6.81E

+ 12

2.06E +

739

1.99E -

2.73E-

Sensor

0.288

2.06E +

1 -0.9993

-1.37E

+ 03

Sensor

0.704

2.06E +

1 0.9993

1.37E +

Sensor

0.440

2.06E +

481 -2.9978

-4.11E

+ 03

Sensor

0.587

2.06E +

1 1.9985

2.74E +

In the naive Bayes classification method, testing

for the five fingers obtained results, as shown in Table

2. From this confusion, matrix results show that the

system enables to identify of around 80%. Some

errors occur in determining the middle, ring, and little

fingers. Those failures are because the movement of

those fingers also activates others fingers.

Table 2: Confusion matrix of the result.

Movement Pattern

(actual data)

Experiment and Resul

1 2 3

Thumb Thumb Thumb Thumb

Index Index Index Index

Middle Middle Ring Middle

Ring Little Ring Ring

Little Little Little Ring

Finger Movements Recognition Using Naive Bayes Algorithm in Frequency Domain

779

4 CONCLUSIONS

This study attempts to recognize the fingers' gestures

using sEMG sensors and the Naive Bayes algorithm.

This system is capable of identifying the poses of the

subject’s fingers. Results show that the percentage of

this system is 80% to acknowledge gestures. In the

future, this system will be used in real time and

implemented for controlling hardware.

ACKNOWLEDGMENTS

The DRPM research fund funded this research.

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