Optimal Placement of Multi-Channels sEMG Electrod for Finger Movement Classification

Sueaseenak, Direk; Uburi, Thunchanok; Tirasuwannarat, Paphawarin

doi:10.1145/3168776.3168802

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…The Myo armband is put in the middle of the forearm-length area of the hand. Between three different electrode positions in the superficial forearm muscles' positions such as the extensor digitorum muscle, Flexor digitorum superficialis muscle, and Palmaris longus muscle, setting the armband in the middle of the forearm produces the best results in finger movement classification [16]. This is the ground reason why the Myo armband has been placed in the upper middle of the forearm muscle during the testing of this research.…”

Section: Experiments Protocolsmentioning

confidence: 95%

Design and Prototyping Soft Fingers for a Hand Prosthesis with the Aim of Predicting the Gripping Force Using LSTM

Siavashani

Yousefi‐Koma

Vedadi

2021

2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM)

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Hands are used for communicating with the surrounding environment and have a complex structure that enables them to perform various tasks with their multiple degrees of freedom. Hand amputation can prevent a person from performing their daily activities. In that event, finding a suitable, fast, and reliable alternative for the missing limb can affect the lives of people who suffer from such conditions.As the most important use of the hands is to grasp objects, the purpose of this study is to accurately predict gripping force from surface electromyography (sEMG) signals during a pinch-type grip. In that regard, gripping force and sEMG signals are derived from 10 healthy subjects. Results show that for this task, recurrent networks outperform nonrecurrent ones, such as a fully connected multilayer perceptron (MLP) network. Gated recurrent unit (GRU) and long short-term memory (LSTM) networks can predict the gripping force with R-squared values of 0.994 and 0.992, respectively, and a prediction rate of over 1300 predictions per second. The predominant advantage of using such frameworks is that the gripping force can be predicted straight from preprocessed sEMG signals without any form of feature extraction, not to mention the ability to predict future force values using larger prediction horizons adequately. The methods presented in this study can be used in the myoelectric control of prosthetic hands or robotic grippers.

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Section: Experiments Protocolsmentioning

confidence: 95%

Design and Prototyping Soft Fingers for a Hand Prosthesis with the Aim of Predicting the Gripping Force Using LSTM

Siavashani

Yousefi‐Koma

Vedadi

2021

2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM)

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“…Par défaut, 5 gestes sont reconnus, mais par l'intermédiaire de son SDK, de nouveaux gestes peuvent être appris. Cet appareil est utilisé dans plusieurs travaux relativement récents comme [10,11] pour la performance musicale (violonistes), pour la classification du mouvement des doigts par [32], pour l'analyse de la navigation par geste de la main par [24], pour la réalité virtuelle par [22], pour la création d'un mapping interactif entre les gestes et des sons musicaux [34] etc. Le Myo™ offre plusieurs avantages par rapport aux autres appareils : un coût abordable (environ 250 €), une configuration simple, et la possibilité d'être caché ce qui est très intéressant en terme de performances artistiques [35].…”

Section: Reconnaissance Des Gestes Des Mainsunclassified

A study on the simultaneous consideration of two modalities for the recognition of SoundPainting gestures

Dongo

Jáuregui

Couture

2019

Proceedings of the 31st Conference on l'Interaction Homme-Machine

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Estimation and early prediction of grip force based on sEMG signals and deep recurrent neural networks

Siavashani

Yousefi-Koma

Vedadi

2023

J Braz. Soc. Mech. Sci. Eng.

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Optimal Placement of Multi-Channels sEMG Electrod for Finger Movement Classification

Cited by 5 publications

References 3 publications

Design and Prototyping Soft Fingers for a Hand Prosthesis with the Aim of Predicting the Gripping Force Using LSTM

Design and Prototyping Soft Fingers for a Hand Prosthesis with the Aim of Predicting the Gripping Force Using LSTM

A study on the simultaneous consideration of two modalities for the recognition of SoundPainting gestures

Estimation and early prediction of grip force based on sEMG signals and deep recurrent neural networks

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