Electrical Tactile Dataset (Piezoelectric and Accelerometer) for textures

<b>Abstract</b>This dataset provides a collection of temporal sensor readings captured by an electrical tactile sensor (PressTip) mounted on a converted CNC machine, aimed at facilitating texture classification research in robotics and manufacturing applications. The dataset comprises low-resolution sensor readings from a piezoelectric sensor and accelerometer, acquired while the sensor was dragged across a diverse range of textures. Each sequence is labelled with corresponding texture categories, encompassing various materials and surface patterns to ensure a broad and representative coverage of textural variations. The dataset is designed to support the development and evaluation of advanced texture classification algorithms, offering insights into the dynamic interactions between tactile sensors and textured surfaces. This dataset aims to enhance the understanding of texture perception and improve the performance of tactile sensing systems in practical applications.<b>About this file</b>This is a dataset made up of a piezoelectric and accelerometer tactile sensors being dragged across a surface at varying pressures. The dataset contains different readings across a number of frames.The labels are as follows and the index corresponds to the number in the y data:**Labels:**+-------+---------------+----------+| Index | Label | Value |+-------+---------------+----------+| 0 | Carpet | - || 1 | LacedMatt | 0.124083 || 2 | wool | 0.198984 || 3 | Cork | 0.344905 || 4 | Felt | 0.116275 || 5 | LongCarpet | 0.128852 || 6 | cotton | 0.129354 || 7 | Plastic | 0.101801 || 8 | Flat | 0.343766 || 9 | Ffoam | 0.396798 || 10 | Gfoam | 0.191812 || 11 | bubble | 0.217116 || 12 | Efoam | 0.073127 || 13 | jeans | - || 14 | Leather | 0.255904 |+-------+---------------+----------+<br>The y data contains numbers corresponding the the index of the above array.Each X item is an image with a set number of frames recording in real time at approximately 10Hz. The y labels is a number corresponding to the index of the labels shown above. For example, 0 would be a carpet.X shape: (Number, frame, sensor index)y shape: (Number,)All files are in compressed numpy format. Python users can load in the dataset using the code provided in the ReadMe.