Quantitative identification of regular texture spacing via spatiotemporal signal analysis of pressure sensor...

Tactile sensing in biological systems is a sophisticated spatiotemporal process where mechanoreceptors decode surface properties through rhythmic interactions. However, current artificial tactile recognition predominantly relies on discrete classification under highly constrained conditions, making it difficult to capture the quantitative physical dimensions of stimuli in naturalistic settings. This study proposes a spatiotemporal method for the quantitative identification of regular texture spacing under naturally varying tactile interaction conditions, bridging the gap between raw sensor dynamics and geometric perception. We utilized a prosthetic finger integrated with a piezoresistive sensor array to capture tactile responses during dynamic sliding. To characterize the frequency-tuning of the sensor array and the energy distribution of rhythmic excitations, Spectral Centroid Analysis (SCA) was employed for initial feature extraction, revealing a spatial coupling effect during tactile interaction. Subsequently, a hierarchical deep learning model was developed, combining Convolutional Neural Networks (CNN) to extract localized spatial contact patterns and Long Short-Term Memory (LSTM) units to integrate quasi-periodic temporal modulations. A channel-wise attention mechanism was incorporated to adaptively weight the contributions of non-uniform sensing regions. Experimental results demonstrate that the proposed approach achieves a recognition accuracy of 76.80% in identifying texture spacing, while indicating improved stability compared to baseline models that lack spatiotemporal integration. Crucially, interpretability analysis reveals that the attention mechanism effectively prioritizes informative sensing channels amidst the signal variability induced by unconstrained force. Our work establishes a attention-based approach to tactile decoding, demonstrating that the coupling of spatial heterogeneity and temporal periodicity is essential for robust perception. This approach provides a computational foundation for developing advanced neuro-prosthetics and autonomous robots capable of human-like tactile discernment in unconstrained environments.