## 5 Discussion  
Haptic technology provides real-time feedback on external force changes through flexible sensors mounted on or inside mechanical surfaces, providing an aid to scene understanding beyond vision. Therefore, recognition based on haptic information has become important for smart devices and has wide application prospects in human-computer interaction, intelligent machinery, and biomedicine. The intelligence level of human-computer interaction is improved by adding haptic perception. In addition, distribute pressure data feedback and analysis is important in many industrial sensing fields. Generally, the haptic information is converted into a grayscale image and then transferred to image-processing methods for subsequent recognition. However, compared with visual images, haptic images are still challenging because of their small dimensions, low resolution, and blurred edges. The experimental results show that MoAS-CNN can realize accurate haptic perception, and the highest accuracy of haptic letters with complex shapes was 98.65%. The cross-stream spatial interactive feature as a second-order response and multiorder feature fusion can significantly improve the extraction of haptic shapes by the network.  
Figure 12: Comparison of recognition results between MoAS-CNN and human for some difficult samples. (**a**) Inference results of sample with label “C”, (**b**) inference results of sample with label “G”, (**c**) inference results of sample with label “E”, (**d**) inference results of sample with label “M”, (**e**) inference results of sample with label “K”, (**f**) inference results of sample with label “K”, (**g**) inference results of sample with label “V”, (**h**) inference results of sample with label “N”, (**i**) inference results of sample with label “H”.  
Although the proposed model has proven to be accurate and effective, it does have limitations. Through the analysis of the misclassified samples, some different classes with similar shapes cannot be clearly classified by the network. This may be attributed to two factors. Firstly, the feature extraction of the proposed method for haptic images is insufficient, and some information is lost in the extraction of high-level semantic information, especially for images with lower resolution, such as 16 \(\times\) 16. Secondly, the characteristics of the haptic task itself, including the low resolution of the sensor array, the small number of pixels, and the blurred edge, lead to inaccurate mapping of the original shape, and the multiple meanings of individual images, resulting in lower-than-average recognition rates for some categories.  
More importantly, smallness and low-resolution inputs are also present for practical vision tasks due to factors such as suboptimal raw data and environmental interference. Therefore, we tried to validate our method on another small general-purpose dataset CIFAR-10 [26], and the recognition accuracy was 98.81%. Compared with the current state-of-the-art results, it is lower than the largest-scale visual transformer methods [27; 28] and essentially on par with the deeper CNN architecture [29]. This study is based on touch recognition based on a single image after touch completion. In the future, we plan to study recognition methods based on multiple dynamic touches and further improve the recognition performance of touch perception through reasonable optimization algorithms.