These datasets report data of 64 Force Sensing Resistors at multiple voltages. It was foun that the input voltage can be used to trim sensors' sensitivity and ultimately to reduce dispersion. The DMAIC cycle was used to reduce process variability on the basis of the Six Sigma Methodology. The zip folder contains:

1) a Matlab file for loading the data

2) four .txt files with the experimental data of Force Sensing resistors


Tactile perception of the material properties in real-time using tiny embedded systems is a challenging task and of grave importance for dexterous object manipulation such as robotics, prosthetics and augmented reality [1-4] . As the psychophysical dimensions of the material properties cover a wide range of percepts, embedded tactile perception systems require efficient signal feature extraction and classification techniques to process signals collected by tactile sensors in real-time.


There are four CSV files (X, Y, Z, and S) in the dataset corresponding to the sensor recordings. The 3-dimensional accelerometer sensor recordings are denoted by X, Y, and Z, respectively. The sound recordings from the electret condenser microphone are denoted by S. As there are 12 classes in the dataset, there is one line for each class in the CSV files. For each texture, 20 seconds of recordings are collected. Therefore, each line in the X, Y, Z files has 4,000 samples (20 sec x 200Hz sampling rate) and each line in the S file has 160,000 samples (20 sec x 8 kHz). The training and test sets for the machine learning classifiers can be created by snipping short frames out of these recordings and applying signal feature extraction. For example, the first 400 columns of the 12th row of X.csv and the first 16,000 columns of the 12th row of S.csv both correspond to the first 2 seconds of the recordings for texture class 12. The Python programs we have developed will be made available upon request.


Please cite the dataset and accompanying paper if you use this dataset:

  • Kursun, O. and Patooghy, A. (2020) "An Embedded System for Collection and Real-time Classification of a Tactile Dataset", IEEE Access (accepted for publication).
  • Kursun, O. and Patooghy, A. (2020) "Texture Dataset Collected by Tactile Sensors", IEEE Dataport, 2020.