Abstract
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.
Highlights
Comparing to traditional peripheral devices such as a mouse or a keyboard, finger gesture recognition (FGR) is much more convenient and natural for users to control an artificial limb and to interact with a computer (Rechy-Ramirez and Hu, 2015)
This study proposes a novel FGR model that consists of two parts, namely, sensing and classification of surface EMG signal (SC-FGR)
It consists of a 5-layer convolution neural network (CNN) that is trained on a spectrum map transformed from the time-domain signals of surface EMG by continuous wavelet transform (CWT)
Summary
Comparing to traditional peripheral devices such as a mouse or a keyboard, finger gesture recognition (FGR) is much more convenient and natural for users to control an artificial limb and to interact with a computer (Rechy-Ramirez and Hu, 2015). Since convolution neural network (CNN) was proposed by Krizhevsky et al in 2012 (Atzori et al, 2016), it has achieved great success in many fields of image recognition, natural language processing, and language translation (Wu et al, 2019b; Yao et al, 2019) As it has much better performance of feature extraction and non-linear fitting than traditional machine learning models, many researchers employed CNN to classify hand gestures from surface EMG signals. (2) A new CNN-FGR algorithm is proposed to accurately classify the surface EMG signals acquired by the developed wireless sensors It consists of a 5-layer CNN that is trained on a spectrum map transformed from the time-domain signals of surface EMG by CWT. The rest of this article is organized as follows: A wireless surface EMG acquisition system is designed in section “A Wireless Surface EMG Acquisition System”; The data processing and CNN-FGR algorithm are described in detail in section “Data Processing and Network Architecture”; The proposed SCFGR model is compared with several related models in Section “Experiment and Results”; and section “Conclusion” concludes this study
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