Augmented-tactility wearable devices have attracted significant attention for their potential to expand the boundaries of human tactile capabilities and their broad applications in medical rehabilitation.Nonetheless,t...Augmented-tactility wearable devices have attracted significant attention for their potential to expand the boundaries of human tactile capabilities and their broad applications in medical rehabilitation.Nonetheless,these devices face challenges in practical applications,including high susceptibility to the operating environments,such as variations in pressure,humidity,and touch speed,as well as concerns regarding wearability and comfort.In this work,we developed an augmented-tactility superskin,termed AtSkin,which integrates a skin-compatible nanofiber sensor array and deep learning algorithms to enhance material recognition regardless of the ambient environment.We fabricated a lightweight and breathable triboelectric sensor array with multilayer nanofiber architectures through electrospinning and hot pressing.The carefully selected combination of sensing layers can capture the electrical characteristics of different materials,thus enabling their distinction.Combined with deep learning algorithms,AtSkin achieved an accuracy of 97.9%in distinguishing visually similar resin and fabric materials,even under varying environmental pressures and humidities.As a proof of concept,we constructed an intelligent augmented-tactility system capable of identifying fabrics with similar textures and hand feel,demonstrating the potential of the superskin to expand human tactile capabilities,enhance augmented reality experiences,and revolutionize intelligent healthcare solutions.展开更多
基金funding has been received from Beijing Municipal Science and Technology with Grant no.Z221100002722015National Natural Science Foundation of China with Grant no.52125201+1 种基金RGC Senior Research Fellowship Scheme with Grant no.SRFS2122-5S04PolyU RI-IWEAR with Grant no.1-CD44.
文摘Augmented-tactility wearable devices have attracted significant attention for their potential to expand the boundaries of human tactile capabilities and their broad applications in medical rehabilitation.Nonetheless,these devices face challenges in practical applications,including high susceptibility to the operating environments,such as variations in pressure,humidity,and touch speed,as well as concerns regarding wearability and comfort.In this work,we developed an augmented-tactility superskin,termed AtSkin,which integrates a skin-compatible nanofiber sensor array and deep learning algorithms to enhance material recognition regardless of the ambient environment.We fabricated a lightweight and breathable triboelectric sensor array with multilayer nanofiber architectures through electrospinning and hot pressing.The carefully selected combination of sensing layers can capture the electrical characteristics of different materials,thus enabling their distinction.Combined with deep learning algorithms,AtSkin achieved an accuracy of 97.9%in distinguishing visually similar resin and fabric materials,even under varying environmental pressures and humidities.As a proof of concept,we constructed an intelligent augmented-tactility system capable of identifying fabrics with similar textures and hand feel,demonstrating the potential of the superskin to expand human tactile capabilities,enhance augmented reality experiences,and revolutionize intelligent healthcare solutions.
