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.

Multifunctional biomimetic tactile system via a stick-slip sensing strategy for human–machine interactions

A tactile sensor system enables natural interaction between humans and machines;this interaction is crucial for dexterous robotic hands,interactive entertainment,and other smart scenarios.However,the lack of sliding friction detection significantly limits the accuracy and scope of interactions due to the absence of sophisticated information,such as slippage,material and roughness of held objects.Here,inspired by the stick-slip phenomena in the sliding process,we have developed a multifunctional biomimetic tactile system based on the stick-slip sensing strategy,which is a universal method to detect slippage and estimate the surface properties of objects by sliding.This system consists of a flexible fingertip-inspired tactile sensor,a read-out circuit and a machinelearning module.Based on the stick-slip sensing strategy,our system was endowed with high recognition rates for slippage detection(100.0%),material classification(93.3%)and roughness discrimination(92.8%).Moreover,robotic hand manipulation,interactive games and object classification are demonstrated with this multifunctional system for comprehensive and promising human-machine interactions.