Texture Perception Using Tactile Sensing Glove Based on PVDF Sensors and Machine Learning

The dataset was collected using a PVDF-based tactile sensing glove designed for naturalistic texture discrimination. Seven healthy participants explored six surface textures: Carpet, Soft-PVC, Plastic Mesh, Paperboard, Wood, and Foam. During data acquisition, each participant wore the tactile glove and slid the index finger over each texture in both forward and backward directions. The sliding motion was performed freely, without controlling the applied force or sliding velocity, to reproduce realistic tactile exploration conditions. For each texture, 50 trials were recorded per participant, resulting in a total of 2100 trials, corresponding to 50 trials × 6 textures × 7 subjects. The tactile signals were acquired from eight PVDF sensors located on the index finger at a sampling frequency of 2 kSamples/s. Each trial lasted 5 s, producing 10,000 samples per sensor. Since each original recording was too large to be directly processed by resource-constrained embedded devices, the signals were segmented into shorter temporal windows. This windowing procedure produced the dataset D_T1000, in which each trial segment contains 1000 samples per sensor.