Yarn sensors have shown promising application prospects in wearable electronics owing to their shape adaptability, good flexibility, and weavability. However, it is still a critical challenge to develop simultaneously...Yarn sensors have shown promising application prospects in wearable electronics owing to their shape adaptability, good flexibility, and weavability. However, it is still a critical challenge to develop simultaneously structure stable, fast response, body conformal, mechanical robust yarn sensor using full microfibers in an industrial-scalable manner. Herein, a full-fiber auxetic-interlaced yarn sensor(AIYS) with negative Poisson’s ratio is designed and fabricated using a continuous, mass-producible, structure-programmable, and low-cost spinning technology. Based on the unique microfiber interlaced architecture, AIYS simultaneously achieves a Poisson’s ratio of-1.5, a robust mechanical property(0.6 c N/dtex), and a fast train-resistance responsiveness(0.025 s), which enhances conformality with the human body and quickly transduce human joint bending and/or stretching into electrical signals. Moreover, AIYS shows good flexibility, washability, weavability, and high repeatability. Furtherly, with the AIYS array, an ultrafast full-letter sign-language translation glove is developed using artificial neural network. The sign-language translation glove achieves an accuracy of 99.8% for all letters of the English alphabet within a short time of 0.25 s. Furthermore, owing to excellent full letter-recognition ability, real-time translation of daily dialogues and complex sentences is also demonstrated. The smart glove exhibits a remarkable potential in eliminating the communication barriers between signers and non-signers.展开更多
A wearable sensing system that can reconstruct dynamic 3D human body models for virtual cloth fitting is highly important in the era of information and metaverse.However,few research has been conducted regarding confo...A wearable sensing system that can reconstruct dynamic 3D human body models for virtual cloth fitting is highly important in the era of information and metaverse.However,few research has been conducted regarding conformal sen-sors for accurately measuring the human body circumferences for dynamic 3D human body reshaping.Here,we develop a stretchable spring-sheathed yarn sensor(SSYS)as a smart ruler,for precisely measuring the circumference of human bodies and long-term tracking the movement for the dynamic 3D body reconstruction.The SSYS has a robust property,high resilience,high stability(>18000),and ultrafast response(12 ms)to external deformation.It is also washable,wearable,tailorable,and durable for long-time wearing.Moreover,geometric,and mechanical behaviors of the SSYS are systematically investigated both theoretically and experimentally.In addition,a transfer learning algorithm that bridges the discrepancy of real and virtual sensing performance is devel-oped,enabling a small body circumference measurement error of 1.79%,notice-ably lower than that of traditional learning algorithm.Furtherly,3D human bodies that are numerically consistent with the actual bodies are reconstructed.The 3D dynamic human body reconstruction based on the wearing sensing sys-tem and transfer learning algorithm enables excellent virtual fitting and shirt customization in a smart and highly efficient manner.This wearable sensing technology shows great potential in human-computer interaction,intelligent fitting,specialized protection,sports activities,and human physiological health tracking.展开更多
基金supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2020R1A2C3003344 and NRF-2020R1A4A2002728)
文摘Yarn sensors have shown promising application prospects in wearable electronics owing to their shape adaptability, good flexibility, and weavability. However, it is still a critical challenge to develop simultaneously structure stable, fast response, body conformal, mechanical robust yarn sensor using full microfibers in an industrial-scalable manner. Herein, a full-fiber auxetic-interlaced yarn sensor(AIYS) with negative Poisson’s ratio is designed and fabricated using a continuous, mass-producible, structure-programmable, and low-cost spinning technology. Based on the unique microfiber interlaced architecture, AIYS simultaneously achieves a Poisson’s ratio of-1.5, a robust mechanical property(0.6 c N/dtex), and a fast train-resistance responsiveness(0.025 s), which enhances conformality with the human body and quickly transduce human joint bending and/or stretching into electrical signals. Moreover, AIYS shows good flexibility, washability, weavability, and high repeatability. Furtherly, with the AIYS array, an ultrafast full-letter sign-language translation glove is developed using artificial neural network. The sign-language translation glove achieves an accuracy of 99.8% for all letters of the English alphabet within a short time of 0.25 s. Furthermore, owing to excellent full letter-recognition ability, real-time translation of daily dialogues and complex sentences is also demonstrated. The smart glove exhibits a remarkable potential in eliminating the communication barriers between signers and non-signers.
基金National Nature Science Foundation of China(No.12074322,No.62072383)Science and Technology Project of Xiamen City(3502Z20183012)+1 种基金Science and Technology Planning Project of Guangdong Province(2018B030331001)Shenzhen Science and technology plan project(JCYJ20180504170208402)。
文摘A wearable sensing system that can reconstruct dynamic 3D human body models for virtual cloth fitting is highly important in the era of information and metaverse.However,few research has been conducted regarding conformal sen-sors for accurately measuring the human body circumferences for dynamic 3D human body reshaping.Here,we develop a stretchable spring-sheathed yarn sensor(SSYS)as a smart ruler,for precisely measuring the circumference of human bodies and long-term tracking the movement for the dynamic 3D body reconstruction.The SSYS has a robust property,high resilience,high stability(>18000),and ultrafast response(12 ms)to external deformation.It is also washable,wearable,tailorable,and durable for long-time wearing.Moreover,geometric,and mechanical behaviors of the SSYS are systematically investigated both theoretically and experimentally.In addition,a transfer learning algorithm that bridges the discrepancy of real and virtual sensing performance is devel-oped,enabling a small body circumference measurement error of 1.79%,notice-ably lower than that of traditional learning algorithm.Furtherly,3D human bodies that are numerically consistent with the actual bodies are reconstructed.The 3D dynamic human body reconstruction based on the wearing sensing sys-tem and transfer learning algorithm enables excellent virtual fitting and shirt customization in a smart and highly efficient manner.This wearable sensing technology shows great potential in human-computer interaction,intelligent fitting,specialized protection,sports activities,and human physiological health tracking.
