Abstract
Hand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, addressing the reliability of such classifiers has been absent, to our best knowledge. This may be due to the lack of consensus on the definition of model reliability in this field. An uncertainty-aware model has the potential to self-evaluate the quality of its inference, thereby making it more reliable. Moreover, uncertainty-based rejection has been shown to improve the performance of sEMG-based hand gesture recognition. Therefore, we first define model reliability here as the quality of its uncertainty estimation and propose an offline framework to quantify it. To promote reliability analysis, we propose a novel end-to-end uncertainty-aware finger movement classifier, i.e., evidential convolutional neural network (ECNN), and illustrate the advantages of its multidimensional uncertainties such as vacuity and dissonance. Extensive comparisons of accuracy and reliability are conducted on NinaPro Database 5, exercise A, across CNN and three variants of ECNN based on different training strategies. The results of classifying 12 finger movements over 10 subjects show that the best mean accuracy achieved by ECNN is 76.34%, which is slightly higher than the state-of-the-art performance. Furthermore, ECNN variants are more reliable than CNN in general, where the highest improvement of reliability of 19.33% is observed. This work demonstrates the potential of ECNN and recommends using the proposed reliability analysis as a supplementary measure for studying sEMG-based hand gesture recognition.
Highlights
Surface electromyography refers to the collective electrical signals from muscles that are collected by noninvasive electrodes
We propose an Evidential Convolutional Neural Network (ECNN) which is designed by integrating an existing end-to-end convolutional neural network [5] with evidential deep learning (EDL)
Unless otherwise stated, the performance of convolutional neural networks (CNNs) is taken as the baseline and compared with evidential convolutional neural network (ECNN) variants using statistical analysis with the Wilcoxon signedrank test, where the null hypothesis assumes that there is no difference of evaluation results between the two models and will be rejected when p-value < 0.05
Summary
Surface electromyography (sEMG) refers to the collective electrical signals from muscles that are collected by noninvasive electrodes. The sEMG-based hand gesture recognition is a practical application of sEMG that has found wide usage in advanced prostheses control [1], [2] and other rehabilitation applications [3]. It is crucial that the development of such a classification-based control scheme highly relies on the accurate and robust hand gesture predictions of users. The current research on sEMG-based hand gesture recognition has focused on improving its accuracy [4]–[6] and robustness [5], [7]–[9] with recent deep learning techniques. Note that model robustness can be summarised as the ability to remain accurate in practical scenarios under many factors that may affect the prediction performance, such as electrode shifts, sweating, limb posture and force changes, and dayto-day variation [7], [10]–[15]. A special case of robustness is to tackle subject variability when considering the userindependent sEMG-based hand gesture recognition [5], [9]
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