Wheelchair for Quadriplegic Patient with Electromyography Signal Control Wireless

Yulianto, Endro; Indrato, Tri Bowo; Nugraha, Bima Triwahyu Mega; Suharyati, Suharyati

doi:10.3991/ijoe.v16i12.15721

Cited by 8 publications

(5 citation statements)

References 12 publications

(18 reference statements)

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“…The whole muscle is responded, and it can show the transmission of mechanical nerve impulses in the spine to the muscles that need to move as shown in Figure 1. [7], MyoWare Muscle Sensor [18] and EMG Muscle Sensor Module. In this research, a basic and low-cost sensor was used.…”

Section: Electromyographymentioning

confidence: 99%

Classification of Electromyography Signal with Machine Learning

Damkilang¹,

Ketcham

2022

Int. J. Onl. Eng.

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The problem of classifying electromyography signals in each gesture occurs due to the use of a constant level of signal amplitude. This research presents an efficiency enhancement of electromyography signal classification in each gesture with machine learning. The performance efficiency of 5 models: SVM, RF, MLP, KNN, and Deep Leaning was compared. The signals were recorded by a low-cost signal sensor. Fist clenching and hand opening gestures were alternately performed every 5 second for 5 times each. Therefore, the total was 4,767 records divided into 3,274 records of hand opening gesture, 1,492 records of fist clenching gesture and 4,833 records of wrist rotating gesture. The results showed that the MLP model was found to have the highest accuracy at 81.45% for fist clenching and hand opening gestures. The Deep Learning model was found to have the highest accuracy at 89.03% for wrist rotating and hand opening gestures.

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Section: Electromyographymentioning

confidence: 99%

Classification of Electromyography Signal with Machine Learning

Damkilang¹,

Ketcham

2022

Int. J. Onl. Eng.

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“…E. Yulianto et al [55] proposed a wheelchair for people with paralysis of both arms and legs or Quadriplegia Patient. The patients cannot use a joystick-controlled wheelchair.…”

Section: 8mentioning

confidence: 99%

Automatically Moving Robot Intended for the Elderly with Voice Control

Piyaneeranart

Ketcham

2021

Int. J. Onl. Eng.

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This research aims at an automatically moving robot intended for the elderly with voice control using voice recognition that gives a direction to the robot by a voice command. The development of this robot employed an algorithm that converts voice to text to control the robot mobility. The performance testing of the automatically moving robot intended for the elderly with Thai language voice commands revealed an accuracy of voice-to-text conversion for commanding the robot to move in designated directions of 100 percent, and the accuracy of voice-to-text conversion for commanding the robot to move in designated directions using English language was 80 percent, while that for both Thai and English commands was 70 percent. Errors in voice-to-text conversion occurred when commands were not clearly spoken or pronounced or when commands were issued from people located in a very noisy place.

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“…is a circuit that serves to increase the dc offset of the EMG signal. In this module, the adder circuit consists of a summing amplifier circuit [20] and a voltage divider circuit. The summing amplifier circuit serves to 'add up' the DC voltage coming from the voltage divider circuit and the EMG signal pulse from the HPF circuit.…”

Section: ) Adder Circuitmentioning

confidence: 99%

Design of an Electromyograph Equipped with Digital Neck Angle Elevation Gauge

Solihin

Yulianto

Ariswati

et al. 2021

j.electron.electromedical.eng.med.inform

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The development of technology also affects human health, including body posture due to poor human position when using gadgets, both smartphones, and laptops. This study is design a tool that can measure the elevation of a person's neck angle equipped with electromyography, to help health workers, medical rehabilitation doctors to diagnose and provide treatment to patients with a bent head posture or forward head posture. In this research, an electromyography module is designed which consists of a series of instruments, a pre-amplifier circuit, a high pass filter, a low pass filter, and a dc offset regulator to be converted to digital so that it can be displayed on a laptop. In this study, the tapped muscle was the upper trapezius muscle using disposable electrodes. Meanwhile, to measure the angular elevation, the MPU 6050 sensor is used to measure the movement of the head forward. The frequency of the electromyography signal is 20-500 Hz. For software or display readings from this tool is Delphy. Meanwhile, the microcontroller used for ADC communication is Arduino Uno. From the research, it was found that the neck angle elevation gauge has a 0,597% error rate, for conditioning conducted on respondents, all respondents experienced a decrease in amplitude on the same frequency spectrum in the last ten minutes. Meanwhile, a drastic decrease occurred at the neck angle of 60°. Thus, it can be concluded that the forward position of the head affects the frequency spectrum of the neck muscles.

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Wheelchair for Quadriplegic Patient with Electromyography Signal Control Wireless

Cited by 8 publications

References 12 publications

Classification of Electromyography Signal with Machine Learning

Classification of Electromyography Signal with Machine Learning

Automatically Moving Robot Intended for the Elderly with Voice Control

Design of an Electromyograph Equipped with Digital Neck Angle Elevation Gauge

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