Transfer learning for sEMG hand gestures recognition using convolutional neural networks

Abstract

In the realm of surface electromyography (sEMG) gesture recognition, deep learning algorithms are seldom employed. This is due in part to the large quantity of data required for them to train on. Consequently, it would be prohibitively time consuming for a single user to generate a sufficient amount of data for training such algorithms. In this paper, two datasets of 18 and 17 able-bodied participants respectively are recorded using a low-cost, low-sampling rate (200Hz), 8-channel, consumer-grade, dry electrode sEMG device named Myo armband (Thalmic Labs). A convolutional neural network (CNN) is augmented using transfer learning techniques to leverage inter-user data from the first dataset and alleviate the data generation burden imposed on a single individual. The results show that the proposed classifier is robust and precise enough to guide a 6DoF robotic arm (in conjunction with orientation data) with the same speed and precision as with a joystick. Furthermore, the proposed CNN achieves an average accuracy of 97.81% on seven hand/wrist gestures on the 17 participants of the second dataset.

EMG Pattern Recognition in the Era of Big Data and Deep Learning

“…Several factors have contributed to the recent expansion of EMG data resources such that big data approaches are beginning to be viable. First, EMG data sets collected as part of individual research studies are now being made available online instead of residing solely on hard drives within the laboratories of individual researchers (e.g., [10][11][12]). Secondly, as in other research communities, the availability of benchmark EMG databases has been critical to the growth of the field [13].…”

“…With the advent of shared bigger EMG data sets and recent advances in techniques for addressing overfitting problems, most emerging deep learning architectures and methods have now been employed in EMG pattern recognition systems (e.g., [14,23,24]). In some cases, both feature engineering and learning are combined by inputing pre-processed data or pre-extracted features to a deep learning algorithm with some benefits having been shown (e.g., references [11,23,24]). Here, we provide a comprehensive review of the recent research and development in deep learning for EMG pattern recognition.…”

sEMG-Based Gesture Recognition with Convolution Neural Networks

“…Meanwhile, the size of kernel filter is smaller than used in previous comparison experiments but is similar to the state-of-the-art methods [35][36][37][38][39]. The first four convolution layers contain 40 2D filters of 3 3…”

“…As listed in Table 3, the average recognition accuracy reached 78.86%. Atzori et al [35] Compared with existing convolution architecture applied in sEMG-based hand gesture recognition [35][36][37][38][39]47], the parallel multiple-scale convolutional layers and filter kernel size of C-B1PB2 are the most significant disparities. As described in Section 2.2, the first dimension of the filter kernel corresponds to the electrodes, and the second dimension of the filter kernel corresponds to the sampling points.…”

“…It indicates that a larger receptive field can cause a slight increase in classification accuracy. The most commonly used kernel filter of 3 × 3 in the state-of-the-art methods of CNN-based hand gesture recognition [35][36][37][38][39] is not optimal. This may be because, for the same kernel size, the higher the sampling rate of the sEMG signal, the less information it contains.…”