## 1 Introduction
This multi-scale design allows for the capture of information from diverse-sized areas in the image, thereby enhancing the model's perceptual capabilities. This approach has found application in electromyography gesture recognition as well. For example, [PERSON] et al. [29] introduced a novel CNN incorporating multi-scale kernels and feature fusion (MKFF-CNN), and it was applied to gesture recognition based on sEMG signals, resulting in a significant 6.54% increase in recognition accuracy compared to a single-scale convolutional subnetwork. [PERSON] et al. [30] proposed an sEMG signal gesture classification model based on a multi-scale module, achieving a recognition accuracy of 79.43% on NinaPro DB5. [PERSON] et al. [31] introduced an RIE model based on inception multi-scale fusion convolution and the ECA mechanism, which achieved an average accuracy of 88.27% on NinaPro DB1, surpassing the traditional CNN by 7.89%. There is multi-dimensional information in sEMG signals, such as the time domain, frequency domain, or time-frequency domain, so deep-learning methods for gesture recognition based on sEMG signals include two predominant approaches. The first involves feature extraction from the original sEMG signals, the subsequent conversion of features into maps, and finally the inputting of these maps into the deep-learning model. In this approach, manual feature extraction is required. The second involves the direct conversion of the original sEMG signals into an electromyography image, enabling the deep-learning model to autonomously learn sophisticated features in the gesture recognition process. This approach can avoid the step of manually extracting features and simplify the recognition process. Hence, this study integrates both the attention mechanism and a multi-scale structure into the ResNet50 model in order to enhance the model's proficiency in capturing various receptive field image features and critical area information within sEMG signals. Simultaneously, features in sEMG signals can be autonomously learned, with no manually feature extracting required. This study integrates the attention mechanism with a multi-scale structure into a deep-learning model, proposing the InRes-ACNet model based on ResNet50. Initially, to tackle challenges associated with the potential loss of channel information during feature extraction from sEMG signals, we introduce a multi-scale inception-attention module. This module aims to enhance the model's ability to extract features related to channel information. Furthermore, we incorporate the ACmix module into the ResNet50 model, providing a synergistic blend of the self-attention mechanism and convolution operations. This integration seeks to enhance the model's feature extraction capabilities. The simultaneous use of both the inception-attention and ACmix modules is intended to improve the model's proficiency in gesture recognition based on sEMG images. Additionally, the model's inputs are the original grayscale images of the sEMG signal. This approach eliminates the need for manual feature extraction during sEMG signal preprocessing, thereby streamlining the gesture recognition process. The rest of this paper is organized as follows: Section 2 describes the preprocessing of original sEMG signals and the generation of sEMG grayscale image datasets. Section 3 introduces the proposed InRes-ACNet model. Section 4 verifies the effectiveness of the InRes-ACNet model through experiments and conducts grasping mode prediction for the electromyography manipulator. Section 5 summarizes the results of this paper's work.