Hardness and Texture Recognition based on PVDF Sensors and Embedded Machine Learning: A case study on Hannes prosthetic hand

This study presents a sensing system composed of PVDF-based sensing arrays to recognize an object’s hardness and texture. The system is mounted on the fingertip of the Hannes prosthetic hand, which obtains the texture and hardness features by grasping daily-life objects. Time-domain features are extracted from the sensor’s response and evaluated through the Support Vector Machine (SVM) algorithm. Additionally, two deep learning (DL) algorithms including One-Dimensional Convolutional Neural Network (1-D CNN) and Long-Short Term Memory (LSTM) were implemented. Results demonstrated that the 1-D CNN attained the highest recognition accuracy 90% ≈ for both hardness and texture. Moreover, it demonstrated that the hardness information is integrated into the full grasp action, while the texture information can be extracted using the first contact with the object. Deploying the 1-D CNN into a microcontroller proved that the system is energy efficient and capable of extracting tactile information in real-time. This study demonstrated the effectiveness of the proposed system in recognizing object properties highlighting its robustness and suitability for developing prosthetics.