## 3 Results and Discussion  
### Signal Acquisition of Object Shape  
In this experiment, the designed signal acquisition circuit was employed to collect signals during grasp tests on variously shaped objects, ensuring that the bottom surface of each object remained on the table throughout the testing process. The test subjects included a cone (height 21 cm, diameter 12.5 cm), a rectangular cuboid (length 6 cm, width 6 cm, height 10 cm), a sphere, and a cylinder. Specifically, two spheres (with diameters of 6 cm and 10 cm) and three cylinders (each 10 cm in height, with diameters of 4 cm, 6 cm, and 8 cm) were tested.  
In Figure 4a, the signal data for grasping objects of different shapes and sizes are summarized. Significant signal differences were observed for the various object shapes. For example, when grasping the cone (Figure 4b), channels 3 and 9 exhibited relatively weak signal intensities. In contrast, for the rectangular cuboid (Figure 4c), which requires greater fingertip strength, the primary signals were concentrated in channels 1, 2, 4, 6, and 8. When grasping a sphere with a 10 cm diameter (Figure 4e), channels 3, 4, 6, and 9 displayed varying signal strengths. Specifically, channels 3 and 9 exhibited weaker signals, while channel 6 registered approximately 0.7 V, and channel 4 demonstrated the strongest signal among the four. For a cylinder with a 6 cm diameter (Figure 4g), the signals in channels 3 and 9 showed significant differences from the other channels, with their signal amplitudes being roughly half of those in the others.  
Further analysis was conducted on the signal differences across channels for different object sizes (Figure 4d-h). Comparing the signals for the two spheres (Figure 4d,e), there was a notable difference in intensity in channels 6 and 8. For the smaller sphere, channel 8 was weaker, and channel 6 was slightly stronger; conversely, for the larger sphere, channel 4 exhibited a tendency toward higher signal strength. Figure 4f-h display the signal variations for grasping cylinders of three different sizes, with channels 3 and 9 showing the most significant changes. When grasping the cylinder with a 6 cm diameter, these channels exhibited the strongest signals. For the cylinder with a 4 cm diameter, channel 3 demonstrated moderate signal strength, while it was weakest for the 8 cm diameter cylinder. Conversely, channel 9 exhibited moderate signal strength for the 8 cm cylinder and was weakest for the 4 cm cylinder.  
In summary, the signal intensity across different sensor channels varied significantly when grasping different objects. These differences not only provide a basis for distinguishing objects of various shapes and sizes but also lay a foundation for the intelligent development of artificial tactile systems. Combined with deep learning algorithms, these signal differences can be utilized for more accurate object recognition and classification, enabling robots to perform complex tasks with greater flexibility and precision. In scenarios such as surgery and rescue, tactile feedback allows for safer and more efficient operations. Furthermore, leveraging these signal differences can enhance tactile experiences in virtual (VR) and augmented reality (AR), making human-computer interactions more natural and realistic, as demonstrated by [PERSON] et al. [59], who explored the use of conductive triboelectric textiles for gesture recognition in VR/AR applications.  
Figure 4: Signal responses of the sensor array when a male tester grasps different objects. (**a**) Signals when grasping seven different objects. (**b**) Signals when grasping a cone. (**c**) Signals when grasping a rectangular prism. (**d**) Signals when grasping a sphere with a diameter of 6 cm. (**e**) Signals when grasping a sphere with a diameter of 10 cm. (**f**) Signals when grasping a cylinder with a diameter of 4 cm. (**g**) Signals when grasping a cylinder with a diameter of 6 cm. (**h**) Signals when grasping a cylinder with a diameter of 8 cm, with all cylinders having a height of 10 cm.