Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN)

Ahsan, Md. Rezwanul; Ibrahimy, Muhammad Ibn; Khalifa, Othman O.

doi:10.1109/icom.2011.5937135

Cited by 113 publications

(46 citation statements)

References 15 publications

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“…It was equipped with data acquisition unit MP100A-CE, universal interface module UIM100C, electromyogram amplifier module EMG100C and acquisition software AcqKnowledge. v 3.1.9 [23]. The sampling frequency was 1000 Hz and gain set to 1000.…”

Section: A Acquisition Of Emg Signalsmentioning

confidence: 99%

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Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions

Ibrahimy¹,

Ahsan²,

Khalifa³

2013

Measurement Science Review

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Section: A Acquisition Of Emg Signalsmentioning

confidence: 99%

“…A 4-level discrete wavelet transform (DWT) is used for the decompositions of EMG signal with Daubechies (db2) mother wavelet function according to the previous research [26]. Later on, the features were extracted for each type of hand movement from the denoised EMG signals [23].…”

Section: B Preprocessing and Feature Extractionmentioning

confidence: 99%

Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions

Ibrahimy¹,

Ahsan²,

Khalifa³

2013

Measurement Science Review

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“…The later measure the electrical activities of the muscles and extract the electromyographic signals [9][10][11]. These signals can be processed and classified for gestures recognition.…”

Section: Introductionmentioning

confidence: 99%

A Cost Effective Deaf-Mute Electronic Assistant System Using Myo Armband and Smartphone

2017

IJSR

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Communication is essential feature in human communities. For some reasons, deaf-mute disabled people lose their ability to hear, speak, or both which makes them suffer to communicate and convey their ideas, especially with normal people. Sign language is the solution for communication in the deaf-mute societies, but it is difficult for the rest of people to understand. Therefore, in this work, a cost effective Deaf-Mute Electronic Assistant System (DMEAS) has been developed to help in solving the communication problem between the deaf-mute people and the normal people. The system hardware consists only of a Myo armband from Thalmic Labs and a smartphone. The Myo armband reads the electromyographic signals of the muscles of the disabled person's forearm through non-invasive, surface mounted electrodes (sEMG) and sends the data directly to the smartphone via Bluetooth. The smartphone will recognize and interpret them to a predefined word depending on the hand gestures. The recognized gesture will be displayed on the smartphone screen as well as displaying the text of the word related to the gesture and its voice record. All the EMG signals are processed using discrete wavelet transform and classified by neural network classifier. The system was extensively tested through experiments by a normal subject to prove its functionality.

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“…The feature extraction process involves the transforming of raw EMG signals into feature vector that are assigned to represent different motions. Several features extraction methods have been suggested and these features can be sorted into time domain features [17], frequency domain features [18], and time-frequency domain features [24]. Time domain features are generally evaluated based on signal amplitude that varies with time.…”

Section: Related Workmentioning

confidence: 99%

Experimental Study of Real-Time Classification of 17 Voluntary Movements for Multi-Degree Myoelectric Prosthetic Hand

et al. 2017

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Featured Application: This work is intended for the development of myoelectric prosthetic hand systems. Furthermore, the outcome of this study may also benefit other electromyographic based human-machine interfaces. Abstract:The myoelectric prosthetic hand is a powerful tool developed to help people with upper limb loss restore the functions of a biological hand. Recognizing multiple hand motions from only a few electromyography (EMG) sensors is one of the requirements for the development of prosthetic hands with high level of usability. This task is highly challenging because both classification rate and misclassification rate worsen with additional hand motions. This paper presents a signal processing technique that uses spectral features and an artificial neural network to classify 17 voluntary movements from EMG signals. The main highlight will be on the use of a small set of low-cost EMG sensor for classification of a reasonably large number of hand movements. The aim of this work is to extend the capabilities to recognize and produce multiple movements beyond what is currently feasible. This work will also show and discuss about how tailoring the number of hand motions for a specific task can help develop a more reliable prosthetic hand system. Online classification experiments have been conducted on seven male and five female participants to evaluate the validity of the proposed method. The proposed algorithm achieves an overall correct classification rate of up to 83%, thus, demonstrating the potential to classify 17 movements from 6 EMG sensors. Furthermore, classifying 9 motions using this method could achieve an accuracy of up to 92%. These results show that if the prosthetic hand is intended for a specific task, limiting the number of motions can significantly increase the performance and usability.

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Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN)

Cited by 113 publications

References 15 publications

Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions

Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions

A Cost Effective Deaf-Mute Electronic Assistant System Using Myo Armband and Smartphone

Experimental Study of Real-Time Classification of 17 Voluntary Movements for Multi-Degree Myoelectric Prosthetic Hand

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