A machine learning-assisted multifunctional tactile sensor for smart prosthetics

The absence of tactile perception limits the dexterity of a prosthetic hand and its acceptance by amputees.Recreating the sensing properties of the skin using a flexible tactile sensor could have profound implications for prosthetics,whereas existing tactile sensors often have limited functionality with cross-interference.In this study,we propose a machine-learning-assisted multifunctional tactile sensor for smart prosthetics,providing a human-like tactile sensing approach for amputations.This flexible sensor is based on a poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(PEDOT:PSS)-melamine sponge,which enables the detection of force and temperature with low cross-coupling owing to two separate sensing mechanisms:the open-circuit voltage of the sensor as a force-insensitive intrinsic variable to measure the absolute temperature and the resistance as a temperature-insensitive extrinsic variable to measure force.Furthermore,by analyzing the unsteady heat conduction and characterizing it using real-time thermal imaging,we demonstrated that the process of open-circuit voltage variation resulting from the unsteady heat conduction is closely correlated with the heatconducting capabilities of materials,which can be utilized to discriminate between substances.Assisted by the decision tree algorithm,the device is endowed with thermal conductivity sensing ability,which allows it to identify 10 types of substances with an accuracy of 94.7%.Furthermore,an individual wearing an advanced myoelectric prosthesis equipped with the above sensor can sense pressure,temperature,and recognize different materials.We demonstrated that our multifunctional tactile sensor provides a new strategy to help amputees feel force,temperature and identify the material of objects without the aid of vision.