## 1 Introduction
Although the execution time of the statistics-based method can be improved by assuming that the underlying data distribution is Laplace (e.g., PLM-Lap) and by replacing the exhaustive search method with a discrete Fibonacci search to seek the maximum of profile likelihood function [24], ensuring the accuracy of the distribution fit is unreliable for a small number of sampling points. In other words, statistical-based detectors, when dealing with different types of EMG signals (e.g., fast, slow, or maximum voluntary contractions), are likely to fail if a false prior assumption is selected [23]; moreover, the number of sampling points for data distribution estimation should not be too small. In summary, while many new CPD techniques offer relatively good detection performance, the algorithms themselves are becoming increasingly complex, which leads to more demanding prerequisites for their implementation and deployment. Because of this, it has been shown that the use of a simple but well-configured detector can match or even surpass the performance of many complex models when dealing with time-series tasks, such as outlier detection, anomaly detection, or change-point detection [26]. Due to the similarity between biosignals and speech signals, many techniques used to process speech signals can be used to process biosignals, such as Teager-Kaiser energy operator (TKEO) [27; 28]. An important feature of the TKEO is that the time-frequency information of signals can be extracted through an extremely narrow window containing only three sampling points [9]. Therefore, the TKEO is a very effective pre-processing technique with the advantages of low computational complexity and high real-time performance, and it has been proven to be effective in improving the performance of EMG signal CPD [23; 29; 30; 31; 32; 33]. Most previous studies in this area have employed a combination strategy of energy operators and threshold-based detectors [34; 35; 36], because threshold-based detectors can retain the real-time advantage of energy operators. Trained neural network detectors have the same advantage of a fast detection speed, and, at the same time, they are more robust [20; 21]. Furthermore, extended versions of TKEO have been demonstrated to have improved detection performance for multiple detectors [37; 38]. In this paper, a novel EMG activity detector (EONND, energy operator neural network detector) is proposed, which uses the energy operators to define the structure and set the parameters of a convolutional neural network, replacing the training process of the neural network, thus solving the problems that the update of neural network parameters requires a large amount of data with labels and that the design of the network structure is too complicated. Compared to several existing detectors, EONND has higher validity and reliability, and it has a definite advantage in terms of running time. The remainder of this paper is organized as follows: Section 2 introduces the fundamental theory of EONND. We simulated EMG signals with a variety of signal-to-noise ratios (SNRs) in Section 3 and compared several existing methods using simulated and real data to verify the validity of EONND. In Section 4, the experimental results are discussed. Finally, the conclusions are drawn in Section 5.