IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_Data fusion and sensing technology_ISCAS2025_Data

This dataset contains raw tactile signals acquired using an array of eight PVDF-based piezoelectric sensing units integrated into the thumb of the Hannes prosthetic hand during controlled object-grasping experiments. The recorded signals capture the piezoelectric response throughout three distinct phases of the grasping process: Press, Hold, and Release. The dataset was used to investigate machine learning approaches for object hardness and texture recognition, including their implementation on embedded systems. The dataset is associated with the following peer-reviewed publication, which provides a detailed description of the sensing system, experimental methodology, and machine learning classification framework: R. Khalifeh, Y. Abbass, M. Yaacoub, C. Gentile, E. Gruppioni, and M. Valle, "Hardness and Texture Recognition based on PVDF Sensors and Embedded Machine Learning: A Case Study on Hannes Prosthetic Hand," 2025 IEEE International Symposium on Circuits and Systems (ISCAS), 2025, pp. 1–5. DOI: https://doi.org/10.1109/ISCAS56072.2025.11043586 Users of this dataset are kindly requested to cite the above publication when using the data in their research.