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. 2023 May 21;23(10):4940.
doi: 10.3390/s23104940.

High-Performance Surface Electromyography Armband Design for Gesture Recognition

Ruihao Zhang  1 , Yingping Hong  1 , Huixin Zhang  1 , Lizhi Dang  1 , Yunze Li  1
Affiliations

High-Performance Surface Electromyography Armband Design for Gesture Recognition

Ruihao Zhang et al. Sensors (Basel). .

Abstract

Wearable surface electromyography (sEMG) signal-acquisition devices have considerable potential for medical applications. Signals obtained from sEMG armbands can be used to identify a person's intentions using machine learning. However, the performance and recognition capabilities of commercially available sEMG armbands are generally limited. This paper presents the design of a wireless high-performance sEMG armband (hereinafter referred to as the α Armband), which has 16 channels and a 16-bit analog-to-digital converter and can reach 2000 samples per second per channel (adjustable) with a bandwidth of 0.1-20 kHz (adjustable). The α Armband can configure parameters and interact with sEMG data through low-power Bluetooth. We collected sEMG data from the forearms of 30 subjects using the α Armband and extracted three different image samples from the time-frequency domain for training and testing convolutional neural networks. The average recognition accuracy for 10 hand gestures was as high as 98.6%, indicating that the α Armband is highly practical and robust, with excellent development potential.

Keywords: acquisition system; convolutional neural networks (CNNs); gesture recognition; surface electromyography (sEMG) signal; wearable device.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
α Armband.
Figure 2
Figure 2
sEMG platform.
Figure 3
Figure 3
Ten gestures of a subject.
Figure 4
Figure 4
sEMG time- and frequency-domain plots (before Butterworth notch filtering).
Figure 5
Figure 5
sEMG time- and frequency-domain plots (after Butterworth notch filtering).
Figure 6
Figure 6
CNN for time-domain images (black represents 16 channels; red represents 8 channels).
Figure 7
Figure 7
Stitching of 8- and 16-channel spectral images (Three dots represent the omitted single−channel image, and the arrow points to the synthesized image).
Figure 8
Figure 8
CNN for spectral images (black represents 16 channels; red represents 8 channels).
Figure 9
Figure 9
Stitching of 8- and 16-channel feature-enhanced spectral images(Three dots represent the omitted single−channel image, and the arrow points to the synthesized image. The dotted red box represents the enhanced channel image).
Figure 10
Figure 10
CNN for feature-enhanced spectral images (black represents 16 channels; red represents 8 channels).
Figure 11
Figure 11
Confusion matrices of 8- and 16-channel time-domain images.
Figure 12
Figure 12
Confusion matrices of 8- and 16-channel spectral images.
Figure 13
Figure 13
Confusion matrices of 8- and 16-channel feature-enhanced spectral images.
See this image and copyright information in PMC

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