Electromyography Signal Acquisition and Analysis System for Finger Movement Classification

Alvarado-Diaz, Witman; Meneses-Claudio, Brian; Román-Gonzalez, Avid

doi:10.14569/ijacsa.2019.0100653

Cited by 5 publications

(10 citation statements)

References 12 publications

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“…Taking in mind that it will first analyze the data that was taken from the left forearm, with the methodology proposed in [2]; Sampling is done as follows: the first 3 seconds no movement is made, the 3 seconds after the representation of a letter of the Peruvian Sign Alphabet is performed, finally in the last 3 seconds no movement is made; It is worth mentioning that samples are taken from a healthy person. Five samples are taken for each of the 27 letters of the alphabet.…”

Section: Resultsmentioning

confidence: 99%

“…For the data acquisition, the system described in [2] in which the Myoware device is used will be used, this device will be connected to an Arduino Uno board, which will acquire the signals and send them by serial port of the computer. In Fig.…”

Section: A Data Acquisitionmentioning

confidence: 99%

“…5, it can observe the stage of characteristics extraction, which consists of 8 characteristics of which 4 are from the domain of frequency and the other 4 in the domain of time, the proposed characteristics are: The maximum value, average data, variance, and standard deviation; then these characteristics are grouped and classified as well used to predict the letter made through sign language. It has to take in mind that it will work with signals with an amplitude between 0 to 10 mV and a frequency of 10 to 100Hz [8], [9], [10] and [11], [2], and that each data channel is extracted, then finding the fast Fourier transform; next, the above-mentioned characteristics are calculated.…”

Section: B Processing and Classificationmentioning

confidence: 99%

“…The Ministry of Education of Perú, through the General Direction of the Basic Special Education (DIGEBE for its acronym in Spanish) makes available the document "Peruvian Sign Language" [2], in which it is mentioned that sign language is a communication system which is perceived through sight and requires the use of the hands as active articulators, the use of space as a place of articulation (phonological structure) and as a temporary reference.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Acquisition and Classification System of EMG Signals for Interpreting the Alphabet of the Sign Language

Alvarado-Diaz¹,

Brian²,

Flores-Medina³

et al. 2019

IJACSA

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Taking into account that in Peru, there is an increase in people with difficulties in speaking or communicating. According to the National Institute of Statistics and Informatics of Peru (INEI for its acronym in Spanish), around 80000 people use the gesturing language. For this reason, this research proposes to use the electromyography (EMG) signals to detect the hand movement and identify the alphabet of the sign language to provide essential communication to people who need it. The idea is to classify the signals and recognize the letters of the Spanish alphabet, interpreted in the Peruvian sign language. The results show the classification of the 27 letters of the alphabet with a general success rate of 93.9%.

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Section: Resultsmentioning

confidence: 99%

Section: A Data Acquisitionmentioning

confidence: 99%

Section: B Processing and Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Acquisition and Classification System of EMG Signals for Interpreting the Alphabet of the Sign Language

Alvarado-Diaz¹,

Brian²,

Flores-Medina³

et al. 2019

IJACSA

Self Cite

View full text Add to dashboard Cite

show abstract

“…The demographic data received was studied carefully and a raw EMG data was made. Witman et al (2019) have explained the methods to get EMG signals and analyze it for finger movement. They used the MyoWare device with an ATmega 329P microcontroller (Fig.…”

Section: Robotic Applications Based On Emg Signalmentioning

confidence: 99%

Review on electromyography signal acquisition and processing

Gohel

Mehendale

2020

Biophys Rev

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Electromyography (EMG) is a technique for recording biomedical electrical signals obtained from the neuromuscular activities. These signals are used to monitor medical abnormalities and activation levels, and also to analyze the biomechanics of any animal movements. In this article, we provide a short review of EMG signal acquisition and processing techniques. The average efficiency of capture of EMG signals with current technologies is around 70%. Once the signal is captured, signal processing algorithms then determine the recognition accuracy, with which signals are decoded for their corresponding purpose (e.g., moving robotic arm, speech recognition, gait analysis). The recognition accuracy can go as high as 99.8%. The accuracy with which the EMG signal is decoded has already crossed 99%, and with improvements in deep learning technology, there is a large scope for improvement in the design hardware that can efficiently capture EMG signals.

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SDAV 1.0: A Low-Cost sEMG Data Acquisition & Processing System For Rehabilitatio

Kachhwaha¹,

Vyas²,

Bhadada³

2023

IJRITCC

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Over the last two decades, myoelectric signals have been widely used in fields including rehabilitation devices and human-machine interfaces. This study aimed to develop an algorithm for surface electromyography (sEMG) data acquisition utilizing low-cost hardware and validate its performance using English vowels as silent speech content. The sEMG data were collected from the three facial muscles of one healthy subject. The sEMG signals were pre-processed, and various time-domain and statistical features were extracted in real time. The raw data and features were then used to train and test three customized machine learning classifiers: k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN). All customized classifiers achieved almost equivalent accuracy rates of 0.83 ± 0.01 in recognizing the English vowels with an improvement of 27.27% (KNN), 3.75% (SVM), and 51.85% (ANN) utilizing the same low-cost data acquisition hardware. Our findings are substantially closers to the results of commercial hardware setups, which raise the possibility of potential usage of low-cost sEMG data acquisition systems with the proposed algorithm in place of commercial hardware setups for rehabilitation devices and other related sectors of human-machine interaction.

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Electromyography Signal Acquisition and Analysis System for Finger Movement Classification

Cited by 5 publications

References 12 publications

Acquisition and Classification System of EMG Signals for Interpreting the Alphabet of the Sign Language

Acquisition and Classification System of EMG Signals for Interpreting the Alphabet of the Sign Language

Review on electromyography signal acquisition and processing

SDAV 1.0: A Low-Cost sEMG Data Acquisition & Processing System For Rehabilitatio

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