Enhancing Real-Time Tactile Interaction Information: A Novel Approach Using Gaussian Process Regression and Differential Pulse Code Modulation

In recent years, tactile interaction has gained prominence in applications where real-time feedback is crucial, such as robotic surgery and tactile internet applications. These applications demand not only high accuracy but also real-time performance in the transmission and processing of tactile information. This article proposes a new method for tactile interaction information transmission utilizing a combination of Gaussian process regression and differential pulse code modulation (GPR-DPCM) to effectively predict and compress vibration signals as effective tactile interaction information, thereby enhancing real-time performance. The method leverages spatial-temporal correlations to optimize the extraction and compression process of vibration signals, achieving a balance between accuracy and computational complexity. Additionally, this article improves the adaptive local noise reduction (ALNR) filter based on the vibration signals to enhance signal clarity without sacrificing real-time performance. Experimental results demonstrate that the GPR-DPCM algorithm enhances real-time performance as evaluated using the mean squared error (mse) to assess the accuracy and timeliness of tactile interaction information processing and the improved ALNR (IALNR) filter can improve the filtering effect, which is crucial for applications requiring immediate responses, such as robotic surgery and tactile internet applications.