Tactile Recognition Using PVDF Sensor Arrays With Time-Series Image Encoding and CNN

To enhance the recognition performance of flexible tactile sensing systems in human-computer interaction (HCI), this letter proposes a tactile signal recognition method for polyvinylidene fluoride (PVDF) sensor arrays based on time-series imaging. In this study, a 4×4 PVDF sensor array was constructed for signal acquisition. The key innovation is the conversion of preprocessed voltage time series into two distinct image representations—Gramian Angular Field (GAF) and Markov Transition Field (MTF) images—to fully exploit the dynamic features of the signals. These images are then fed into a Convolutional Neural Network (CNN) for end-to-end classification. Experimental results demonstrate that the proposed method achieves outstanding performance in contact state recognition, with both the GAF+CNN and MTF+CNN models exceeding 95% in accuracy, precision, recall, and F1-score. The MTF+CNN model shows a slight advantage in recall and F1-score. In terms of deployment, the system achieves millisecond-level single-sample inference latency on a general-purpose computing platform (MacBook Pro 2022, M2 chip), proving its potential for real-time practical applications. This work provides an effective solution for developing high-precision, low-latency tactile sensing systems.