Muscle fatigue detection is of great significance to human physiological activities, but many complex factors increase the difficulty of this task. In this article, we integrate several effective techniques to distinguish muscle states under fatigue and nonfatigue conditions via surface electromyography (sEMG) signals. First, we perform an isometric contraction experiment of biceps brachii to collect sEMG signals. Second, we propose a neural architecture search (NAS) framework based on reinforcement learning to autogenerate neural networks. Finally, we present an effective two-step training strategy to improve the performance by combining CNN with three types of commonly used statistical algorithms. Meanwhile, we propose a data enhancement algorithm based on empirical mode decomposition (EMD) to generate time-series data for expanding the dataset. The results show that this search algorithm can hunt for high-performing networks, and the accuracy of the best-selected model combined with support vector machine (SVM) for the group is 96.5%. With the same architecture, the average accuracy in individual models is 97.8%. The proposed data enhancement technique can effectively improve the fatigue detection performance, which allows further implementations in the human-exoskeleton interaction systems.

Deep learning (DL) technologies have recently shown great potential in emotion recognition based on electroencephalography (EEG). However, existing DL-based EEG emotion recognition methods are built on single-task learning, i.e., learning arousal, valence, and dominance individually, which may ignore the complementary information of different tasks. In addition, single-task learning involves a new round of training every time a new task appears, which is time consuming. To this end, we propose a novel method for EEG-based emotion recognition based on multi-task learning with capsule network (CapsNet) and attention mechanism. First, multi-task learning can learn multiple tasks simultaneously while exploiting commonalities and differences across tasks, it can also obtain more data from different tasks, which can improve generalization and robustness. Second, the innovative structure of the CapsNet enables it to effectively characterize the intrinsic relationship among various EEG channels. Finally, the attention mechanism can change the weight of different channels to extract important information. In the DEAP dataset, the average accuracy reached 97.25%, 97.41%, and 98.35% on arousal, valence, and dominance, respectively. In the DREAMER dataset, average accuracy reached 94.96%, 95.54%, and 95.52% on arousal, valence, and dominance, respectively. Experimental results demonstrate the efficiency of the proposed method for EEG emotion recognition.

The detection of muscle activation intervals is of great significance to the application of gait, gesture, and some other biomedical movements. Surface electromyographic (sEMG) signal can record the electrical activity of muscles effectively. This kind of signal is sometimes, however, corrupted by background noises. Traditionally, the amplitude analysis of the sEMG signal is an alternative approach for the identification of onsets and offsets. In this work, the sEMG signal was analyzed using global-based rapid composite multiscale sample entropy and compared to the other popular algorithms. A double threshold method with an interlocking structure was applied to complete the onsets and offsets detection task. The proposed algorithm was tested in semisynthetic and recorded signals, and it can take advantage of the nonlinear properties of entropy to distinguish the sEMG signal from motion artifacts and tonic spikes. By using the proposed algorithm, the median values of absolute error time for onsets and offsets estimation were 32 and 60 ms, respectively. Meanwhile, the accuracy, false-alarm rate, and missing-alarm rate were 86.1%, 5.7%, and 9.7%, respectively. Our findings suggest that the proposed method can effectively detect muscle activation intervals.

The analysis of human muscle fatigue is of great significance to human physiological activities. Surface electromyography (sEMG) is the widely used technique to analyze muscle fatigue due to its non-invasiveness. However, sEMG signals are complex, non-linear, and multiple time scales. Hence, multiscale entropy is an effective method to quantify the process of muscle fatigue. In this study, we proposed rapid refined composite multiscale sample entropy (R2CMSE) and used it to analyze the process of muscle fatigue. Firstly, R2CMSE was utilized to characterize the complexity and validity of noise signals on different scales and lengths. Secondly, isometric contraction activities of the biceps brachii were recorded by using sEMG from ten subjects. Then, combined with the sEMG signals of all subjects, a three-dimensional map of scale-length-entropy was constructed to determine an appropriate time scale and data length. Meanwhile, a two-way repeated-measures ANOVA was operated, and the results showed that non-fatigue and fatigue conditions exist significant differences under all algorithms. Finally, R2CMSE was used to quantify the fatigue process to analyze the reliability of its application to different subjects. It was shown that compared with other multiscale entropy algorithms, R2CMSE was faster in the calculation, less dependent on different data lengths, and more robust at different time scales. The proposed algorithm can also extract the hidden information of sEMG signals and investigate the process of muscle fatigue more effectively.