Temporal deep neural network for tactile sensing in artificial finger pulp skin

Tactile perception plays a vital role in artificial finger pulp skin, especially in regions responsible for grasping and touching tasks, where precise sensing of deformation position and applied force is critical. Conventional demodulation methods often fail to fully leverage temporal correlations in data during the pressing process, limiting the accuracy of tactile demodulation. To address this, we propose a tactile sensing system based on quasi-distributed Fiber Bragg Gratings (FBGs) integrated into artificial finger pulp skin, along with a two-stage hybrid LSTM-Transformer neural network (TSH-LTNN) to jointly reconstruct pressing position and force. The network trains a temporal demodulation model by constructing possible data variations over three consecutive time steps, where the LSTM captures short-term continuity, the Transformer extracts long-range dependencies, and an adaptive fusion module integrates their complementary features. Experimental results show that the proposed model outperforms existing methods. In the 0-30 mm pressing range and 0-14.71 N force range, the mean absolute error (MAE) for position prediction is 0.2331 mm (R² = 0.9971), and for force prediction, it is 0.303 N (R² = 0.9829). Compared to the Random Forest model, the TSH-LTNN achieves a 2.34% improvement in position R² and a 66.06% reduction in MAE. For force prediction, it demonstrates a 2.48% improvement in R² and a 31.94% reduction in MAE. These results confirm that the proposed system offers precise, stable, and real-time pressure-state demodulation, with strong potential for high-precision haptic feedback applications.