## 4 Discussion
Although there was a deviation between the study approach using simulated signals and that using real EMG signals, it is this deviation that precisely validated the generalization performance of EONND in processing different types of signals, providing the possibility to expand EONND to other signal areas (such as speech, ECG, EEG, etc.). Therefore, the study approach using simulated signals can still reflect the performance differences between detectors. Performance metrics show significant degradation on real data. The detection results of the simulated data were significantly better than the real data, both in terms of reliability and accuracy metrics. There are two reasons for this phenomenon: (1) the device used to synchronize the onset and offset time labels and (2) the way the confusion matrix used to calculate the statistical metrics was constructed. As mentioned above, the real-world data for this paper were collected during an experiment based on steering wheel rotation. Since the temporal bias of the algorithm was heavily dependent on the responsiveness of the steering wheel (ignoring the physiological electro-mechanical delay) and the rotational torque of the steering wheel affected the features of the EMG signals, low values of the statistical metrics resulted. Figure 11a illustrates a raw EMG signal and the onset and offset points labelled by the motion data acquired through the game steering wheel. With Figure 11a as a reference,  
Figure 11b-g shows the detection results of the six detectors. The vertical solid lines are the ground truth values, while the vertical dashed lines are the change points detected by the detectors. Since the ground truth values were automatically labelled by the experimental equipment, they were more objective than the manual labelling carried out by human experts. However, it is obvious that there was a period of EMG silence during the muscle voluntary contraction, called the silent period (SP) [63]. All sampling points in the SP were classified as FN by the confusion matrix. Therefore, for the real data, the performance metric Recall was significantly lower than the simulated data, as was the F1 score for the same reason. Nevertheless, the Precision of EONND was 98.75% (as shown in Figure 10d), indicating that the false detection rate of EONND was quite low. 5 EONND applicability issues. With the current proliferation of wearable devices, it is valuable to build an algorithmic framework with low computational complexity for low-computing-power devices. The  
Figure 11: Comparison of detection results of six detectors on real EMG signals. Subplot (**a**) shows the raw EMG signal with ground truth labels; (**b–g**) are schematic diagrams of the differences between the detection results and ground truth labels. The dashed vertical lines are the change points detected by the detectors. efficiency of EONND has been demonstrated by comparison with simple methods. EONND operates almost as fast as the scheme combining the TKEO with a threshold detector and about 56% faster than the scheme combining the MTEO with a threshold detector. In addition, EONND does not require any prior knowledge about the noise segment of the signal, which is advantageous for the application of EONND to other signals with change-point detection tasks (e.g., speech signals, ECG, EEG, etc. ).