Object classification system using temperature variation of smart finger device via machine learning

In this study, we proposed smart finger devices (SFDs) for an object classification system using unimodal temperature sensors. Each SFD comprised a module with a flexible thermoelectric device (TED) and a resistance temperature detector (RTD) sensor embedded in a silicone finger cot mounted on a robot gripper. The stored Peltier heat on the TED of the SFD was transferred to the object when the robot gripper grasped it. The RTD sensor data obtained through a one-dimensional convolutional neural network (1D-CNN) distinguished materials with similar thermal conductivities. Through two preprocessing steps, the sensor data were fed into the designed classifier to identify ten selected objects. Finally, our configured classifier performed real-time recognition using unimodal temperature sensors.