Tactile Time-Series Dataset for Artificial Tactile Perception with Reservoir Computing

This dataset contains one-dimensional tactile time-series data used in the study “Artificial Tactile Perception with Reservoir Computing.” The data were acquired using a MEMS tactile sensor during controlled tactile scanning. The measurement system consisted of a three-axis robot stage and a reference load cell, and the sensor was scanned over target surfaces under a controlled normal load.

The dataset was constructed for three tactile recognition tasks: bump detection, shape classification, and material classification. For bump detection and shape classification, 3D-printed periodic surface structures were used. The prepared shapes included semicircle, curve, and square structures with spacing conditions of 400 μm, 600 μm, and 800 μm. For material classification, flat material samples were used so that the task focused on material-dependent tactile responses.

The MEMS tactile sensor output was acquired through an oscillator circuit and converted into a count-based time-series signal. The data were preprocessed using smoothing and normalization procedures described in the accompanying paper. The dataset includes tactile time-series data, task labels, and metadata required for reproducing the experimental evaluation.

This dataset is intended to support research on tactile sensing, tactile time-series analysis, reservoir computing, and machine-learning-based tactile recognition.