November 4, 2025

EMG pattern recognition by neural networks for multi fingers control

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Recognition of finger movements based on EMG achieved successfully, showing effective classification.
Experimental studies demonstrate robust performance in EMG pattern recognition for joint angles and finger motion.
Neural networks utilized for the analysis and real-time simulation of multifinger control in robotic applications.
Highlights the potential use of neural networks for advanced robotic hand designs, enhancing dexterity.

Implication

Experiment reveals neural networks can classify EMG patterns for finger movements, implying potential for robotic hand control.

Abstract

This paper proposes that EMG patterns can be analyzed and classified by neural networks. Through experiments and on-line simulations, it is demonstrated that recognition of not only finger movement but also joint angles in continuously finger movement, based on EMG patterns, can be successfully accomplished.We also demonstrate a EMG controlled 5 fingers 10 joints robot hand.

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Cite This Study

Uchida et al. (1992) studied this question.

synapsesocial.com/papers/690aab1a5b68e76fd9e7a49d https://doi.org/10.1109/iembs.1992.5761230

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Implication

Experiment reveals neural networks can classify EMG patterns for finger movements, implying potential for robotic hand control.

EMG pattern recognition by neural networks for multi fingers control

Key Points

Implication

Abstract

Discussion

Cite This Study

EMG pattern recognition by neural networks for multi fingers control

Key Points

Implication

Abstract

Discussion

Cite This Study

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