Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors

Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very p...

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Autores principales:	Jianting Fu, Shizhou Cao, Linqin Cai, Lechan Yang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	surface EMG EMG sensor finger gesture recognition convolution neural network artificial limb Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/a0c73a47fabd49a1b898f845c4fce494
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spelling	oai:doaj.org-article:a0c73a47fabd49a1b898f845c4fce4942021-11-11T06:10:57ZFinger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors1662-518810.3389/fncom.2021.770692https://doaj.org/article/a0c73a47fabd49a1b898f845c4fce4942021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fncom.2021.770692/fullhttps://doaj.org/toc/1662-5188Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very popular and successful because surface EMG is a cumulative bioelectric signal from the surface of the skin that can accurately and intuitively represent the force of the fingers. However, existing surface EMG-based methods still cannot fully satisfy the required recognition accuracy for artificial limb control as the lack of high-precision sensor and high-accurate recognition model. To address this issue, this study proposes a novel FGR model that consists of sensing and classification of surface EMG signals (SC-FGR). In the proposed SC-FGR model, wireless sensors with high-precision surface EMG are first developed for acquiring multichannel surface EMG signals from the forearm. Its resolution is 16 Bits, the sampling rate is 2 kHz, the common-mode rejection ratio (CMRR) is less than 70 dB, and the short-circuit noise (SCN) is less than 1.5 μV. In addition, a convolution neural network (CNN)-based classification algorithm is proposed to achieve FGR based on acquired surface EMG signals. The CNN is trained on a spectrum map transformed from the time-domain surface EMG by continuous wavelet transform (CWT). To evaluate the proposed SC-FGR model, we compared it with seven state-of-the-art models. The experimental results demonstrate that SC-FGR achieves 97.5% recognition accuracy on eight kinds of finger gestures with five subjects, which is much higher than that of comparable models.Jianting FuShizhou CaoLinqin CaiLechan YangFrontiers Media S.A.articlesurface EMGEMG sensorfinger gesture recognitionconvolution neural networkartificial limbNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Computational Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	surface EMG EMG sensor finger gesture recognition convolution neural network artificial limb Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	surface EMG EMG sensor finger gesture recognition convolution neural network artificial limb Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Jianting Fu Shizhou Cao Linqin Cai Lechan Yang Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors
description	Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very popular and successful because surface EMG is a cumulative bioelectric signal from the surface of the skin that can accurately and intuitively represent the force of the fingers. However, existing surface EMG-based methods still cannot fully satisfy the required recognition accuracy for artificial limb control as the lack of high-precision sensor and high-accurate recognition model. To address this issue, this study proposes a novel FGR model that consists of sensing and classification of surface EMG signals (SC-FGR). In the proposed SC-FGR model, wireless sensors with high-precision surface EMG are first developed for acquiring multichannel surface EMG signals from the forearm. Its resolution is 16 Bits, the sampling rate is 2 kHz, the common-mode rejection ratio (CMRR) is less than 70 dB, and the short-circuit noise (SCN) is less than 1.5 μV. In addition, a convolution neural network (CNN)-based classification algorithm is proposed to achieve FGR based on acquired surface EMG signals. The CNN is trained on a spectrum map transformed from the time-domain surface EMG by continuous wavelet transform (CWT). To evaluate the proposed SC-FGR model, we compared it with seven state-of-the-art models. The experimental results demonstrate that SC-FGR achieves 97.5% recognition accuracy on eight kinds of finger gestures with five subjects, which is much higher than that of comparable models.
format	article
author	Jianting Fu Shizhou Cao Linqin Cai Lechan Yang
author_facet	Jianting Fu Shizhou Cao Linqin Cai Lechan Yang
author_sort	Jianting Fu
title	Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors
title_short	Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors
title_full	Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors
title_fullStr	Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors
title_full_unstemmed	Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors
title_sort	finger gesture recognition using sensing and classification of surface electromyography signals with high-precision wireless surface electromyography sensors
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/a0c73a47fabd49a1b898f845c4fce494
work_keys_str_mv	AT jiantingfu fingergesturerecognitionusingsensingandclassificationofsurfaceelectromyographysignalswithhighprecisionwirelesssurfaceelectromyographysensors AT shizhoucao fingergesturerecognitionusingsensingandclassificationofsurfaceelectromyographysignalswithhighprecisionwirelesssurfaceelectromyographysensors AT linqincai fingergesturerecognitionusingsensingandclassificationofsurfaceelectromyographysignalswithhighprecisionwirelesssurfaceelectromyographysensors AT lechanyang fingergesturerecognitionusingsensingandclassificationofsurfaceelectromyographysignalswithhighprecisionwirelesssurfaceelectromyographysensors
_version_	1718439520467156992

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors

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