Biosensors-Basel
·
2024
·Vol. 14
·期 5
·227
·
IF 4.9
Ya Jiang,Shujing Zhao,Fengyuan Wang,Xiaoyuan Zhang,Zhiqiang Su
At the heart of the non-implantable electronic revolution lies ionogels, which are remarkably conductive, thermally stable, and even antimicrobial materials. Yet, their potential has been hindered by poor mechanical properties. Herein, a double network (DN) ionogel crafted from 1-Ethyl-3-methylimidazolium chloride ([Emim]Cl), acrylamide (AM), and polyvinyl alcohol (PVA) was constructed. Tensile strength, fracture elongation, and conductivity can be adjusted across a wide range, enabling researchers to fabricate the material to meet specific needs. With adjustable mechanical properties, such as tensile strength (0.06–5.30 MPa) and fracture elongation (363–1373%), this ionogel possesses both robustness and flexibility. This ionogel exhibits a bi-modal response to temperature and strain, making it an ideal candidate for strain sensor applications. It also functions as a flexible strain sensor that can detect physiological signals in real time, opening doors to personalized health monitoring and disease management. Moreover, these gels’ ability to decode the intricate movements of sign language paves the way for improved communication accessibility for the deaf and hard-of-hearing community. This DN ionogel lays the foundation for a future in which e-skins and wearable sensors will seamlessly integrate into our lives, revolutionizing healthcare, human–machine interaction, and beyond.
研究领域:
材料科学
·
生物医学工程
·
电子皮肤研究
✓ 已验证
产品关联
DOI
PubMed
高影响因子
COMPOSITES SCIENCE AND TECHNOLOGY
·
2019
·
IF 6.309
Qiuping Qian,Yan Wang,Min Zhang,Limin Chen,Jie Feng,Yi Wang,Yunlong Zhou
We report a facile, low cost and readily scalable method for the fabrication of a flexible paper-based strain sensor. Approaches of pen-writing, paper-transferring and elastomer-packaging were exploited to build a strain sensor with layered structures consisting of the graphene mesh and conductive polyaniline . The normalized conductivity change ( Δ G / G 0 ) and gauge factor could reach as high as 747 and 1.1 × 10 4 , respectively. The mechanism of ultra-high conductivity change was attributed to the formation of micro-disconnection and micro-cracking in the layered structure deduced by Raman imaging and a simulation based on equivalent resistor networks . As a proof of the concept, the strain devices were finally exploited in sign language recognition. Our work demonstrated a new strategy to create ultra-high sensitive communication devices for people with speech and hearing impairment.
研究领域:
材料科学
·
生物医学工程
✓ 已验证
产品关联
DOI
高影响因子
CHEMICAL ENGINEERING JOURNAL
·
2025
·
IF 13.2
Jingwen Sun,Zhenyuan Xu,Xingyan Shao,Mingyu Qi,Jiahui Shao,Pengfei Zhang,Dongzhi Zhang
With the continuous development of flexible electronics and human-computer interaction, the demand for flexible sensors in sign language recognition has further expanded. Hydrogels have emerged as promising candidate materials for flexible sensors due to their ability to efficiently convert mechanical deformation into electrical signals. To address the limitations of conventional hydrogels that suffer from insufficient service life caused by water evaporation or low-temperature freezing, this study prepared a composite hydrogel using a one-step polymerization method, in which acrylamide (AM) monomers were polymerized in situ within a system containing ionic liquid ([EMIM]Cl), polydopamine (PDA), and carbon nanotubes (CNTs), forming a polyacrylamide (PAM) network. Benefiting from multiple interaction mechanisms among PDA, [EMIM]Cl, and PAM, including hydrogen bonding, π-π stacking, and covalent bonds, the fabricated hydrogel exhibits exceptional tensile properties (elongation exceeding 2400 %), excellent water retention, antifreeze characteristics, and strong adhesion. As a strain sensor, it exhibits high sensitivity (GF = 2.38 within 0–400 % strain), fast response (< 80 ms), and durability (12,000 stretch-release cycles). By integrating the hydrogel strain sensor with peripheral circuits and a 1D-CNN algorithm, it can accurately capture hand movements, achieving a detection accuracy of 98.61 % for six complex sign language gestures. This provides an effective solution for high-performance sign language recognition.
研究领域:
生物医学工程
·
柔性电子学
·
人机交互
·
传感器材料
·
高分子材料科学
✓ 已验证
产品关联
DOI
高影响因子
ACS Applied Materials & Interfaces
·
2025
·
IF 8.2
Chunqing Yang,Dongzhi Zhang,Weiwei Wang,Jun Wang,Yukun Liu,Lina Zhou,Yihong Guo,Jiahui Shao
In recent years, smart textiles and flexible wearable products have garnered significant attention in fields such as human-computer interaction, medical rehabilitation training, and motion monitoring. Flexible pressure sensors have attracted significant attention due to their excellent flexibility, stability, and multifunctional integration. Herein, a multifunctional wearable MXene/polydopamine (PDA)@cotton fabric pressure sensor was developed by modifying weft-knitted cotton fabric based on a dual hydrogen bond self-assembly strategy. The MXene/PDA@cotton fabric pressure sensor demonstrates wide linear detection range (0–146 kPa), high sensitivity (0.95 kPa–1), fast response/recovery times (16.434 and 11.952 ms), and outstanding stability after over 5000 cyclic tests. This sensor can achieve the monitoring of physiological parameters for human state detection, such as facial expression signals, abdominal respiratory signals, and joint bending signals. Furthermore, by integrating the MXene/PDA sensors into the interphalangeal and metacarpophalangeal joints of a cotton glove, combined with intelligent algorithms and the human-computer interaction system, static gesture recognition and dynamic sign language translation were successfully realized based on the smart glove. This work demonstrates the potential application of flexible pressure sensors in intelligent human-computer interaction, providing new insights for developing next-generation sign language recognition systems.
研究领域:
材料科学
·
纳米技术
·
可穿戴电子器件
·
人机交互
·
人工智能
·
生物医学工程
✓ 已验证
产品关联
DOI
PubMed
高影响因子
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
·
2023
·
IF 8.2
Yue Xin,Wenshuo Gao,Guang Zeng,Shousen Chen,Jijin Shi,Wenquan Wang,Ke Ma,Baoliu Qu,Jun Fu,Xin He
Conductive hydrogel which belongs to a type of soft materials has recently become promising candidate for flexible electronics application. However, it remains difficult for conductive hydrogel-based strain sensors to achieve the organic unity of large stretchability, high conductivity, self-healing, anti-freezing, anti-drying and transparency. Herein, a multifunctional conductive organohydrogel with all of the above superiorities is prepared by crosslinking polyacrylamide (PAM) with dialdehyde starch (DAS) in glycerol-water binary solvent. Attributing to the synergy of abundant hydrogen bonding and Schiff base interactions caused by introducing glycerol and dialdehyde starch, respectively, the organohydrogel achieved balanced mechanical and electrical properties. Besides, the addition of glycerol promoted the water-locking effects, making the organohydrogel retain the superior mechanical properties and conductivity even at extreme conditions. The resultant organohydrogel strain sensor exhibits desirable sensing performance with high sensitivity (GF = 6.07) over a wide strain range (0–697 %), enabling the accurate monitoring of subtle body motions even at −30 °C. On the basis, a hand gesture monitor system based on the organohydrogel sensors arrays is constructed using machine learning method, achieving a considerable sign language recognition rate of 100 %, and thus providing convenience for communications between the hearing or speaking-impaired and general person.
研究领域:
材料科学
·
柔性电子
·
传感器
·
生物医学工程
✓ 已验证
产品关联
DOI
PubMed
高影响因子
ADVANCED FUNCTIONAL MATERIALS
·
2025
·Vol. 35
·期 29
·2425586
·
IF 19
Yue Zhou,Shuai Guo,Yun Zhou,Liupeng Zhao,Tianshuang Wang,Xu Yan,Fangmeng Liu,Sai Kishore Ravi,Peng Sun,Swee Ching Tan,Geyu Lu
The escalating population affected by deafness and hearing loss demands solutions to revolutionize traditional sign language recognition based on interpreters. The emergence of wearable sensors could provide a promising alternative but suffer from poor mechanical stability, external signal inferences, less sensitivity, and signal hysteresis. Herein, an ultrasensitive and anti-interference flexible ionic composite nanofiber membranes (ICNM) based pressure sensor is developed through precisely manipulating polymer-blending interactions, where ionic liquid and silver nanowire additives are well anchored on thermoplastic polyurethane polymer scaffolds without leakage via unique hydrogen bond networks, leading to a substantial areal capacitance of 20 µF cm −2 , and effectively mitigating external noise. The ICNM-based sensor showcases high sensitivity (57.2 kPa −1 ), ultralow detection limit (≈1.2 Pa), fast response time (15 ms), expansive detection range (1.2 Pa –220 kPa), and exceptional stability for over 10 000 continuous compression and recovery cycles, showing great promise for capturing subtle facial expressions, large joint movements, and high-frequency (≈25.5 Hz) pressure sensing in a high accuracy and resolution. Together with advanced machine learning algorithms, an intelligent sign language recognition glove achieves 96.8% accuracy for 24 letters within 0.1 s, ushering in a new era for ultrasensitive pressure sensors and significantly contributing to next-generation intelligent sign language recognition systems.
✓ 已验证
产品关联
DOI
高影响因子
COMPOSITES PART B-ENGINEERING
·
2025
·
IF 14.2
Weiwei He,Fangxin Wan,Yunlong Liu,Guanyang Wu,Puye Zhang,Yingshuo Xiong,Xinyue Zhang,Tianyi Hang,Wei Chen,Kejie Chen,Boce Xue,Runsheng Li,Guofang Hu,Zihao Li,Yuyao Wu,Jianhao Zhu,Teng Xiang,Jiajia Zheng,Yanzheng Zhang
The development of wearable sensor-based sign language recognition systems has become a solution to facilitate effective communication among hearing-impaired groups, but achieving high integration, sign language standardization, and anti-environmental interference remains challenging. Here, we design a smart glove system for real-time sign language interpretation based on a composite foam with a cross-dimensional conductive network, integrating flexible switches, pressure sensors, custom miniaturized circuits, and deep learning modules. The sensor exhibits electromagnetic shielding, thermal management, and antibacterial capabilities, enhancing the smart glove's adaptability to the external environment. The elastic conductive framework of the foam allows the system to realize the start/stop function and fast response to gestures. In addition, a deep learning model of multi-component collaboration and mechanism fusion is constructed, along with the establishment of a comprehensive set of sign language rules, which realizes 99.4 % accurate recognition of 26 letters through only three pressure sensors. Overall, our proposed strategy provides a new way for smart gloves to work stably in harsh environments, and is expected to eliminate communication barriers among hearing-impaired groups due to sign language diversity and cultural differences.
研究领域:
人工智能
·
材料科学
·
可穿戴技术
·
人机交互
·
信号处理
·
生物医学工程
✓ 已验证
产品关联
DOI
高影响因子
Sensors & Diagnostics
·
2023
·
IF 0
Jionghong Liang,Ke Ma,Wenshuo Gao,Yue Xin,Shousen Chen,Weicheng Qiu,Gengzhe Shen,Xin He
Traditional starch-based hydrogels often lack the mechanical robustness and electrical conductivity required for strain sensing applications. In this study, a conductive organohydrogel was developed by blending starch and MXene with polyacrylic acid (PAA). The resulting PAA/starch/MXene organohydrogel exhibits exceptional mechanical strength, high electrical conductivity, robust adhesion, and resilience to low temperatures. Strain sensors based on this innovative material demonstrate remarkable characteristics, including high sensitivity (maximum GF = 14.19), rapid response time (approximately 160 ms), a wide sensing range (exceeding 800%), and excellent cycling stability. Notably, these sensors remain efficient even at frigid temperatures as low as −30 °C. Furthermore, these sensors find practical application in sign language translation, achieving an impressive recognition rate of up to 100% for complex sentences. When integrated into a sensor array, they enable precise assessment of load magnitude and distribution. Consequently, this research introduces an innovative strategy for fabricating highly efficient conductive hydrogels, holding significant promise for diverse applications in the realm of flexible electronic devices, and promoting sustainable advancements in the field of wearable electronics.
研究领域:
材料科学
·
电子学
·
可穿戴技术
·
生物医学工程
✓ 已验证
DOI
Nano Energy
·
2022
·
IF 19.069
Hongsen Niu,Hao Li,Yang Li,Wenjing Yue,Song Gao,Xiao Wei,Guozhen Shen
Traditional electronic skin (e-skin) with single micro-nano structures has a limited capacity to optimize the comprehensive sensing properties, which greatly hinders the intellectualization process. Herein, cocklebur-inspired bionic e-skin consisting of the “branch-seed-spininess” three-dimensional hierarchical structure, namely the P(VDF-TrFE) fiber-TiO 2 pillar-TiO 2 thorn structure (FPTS), is proposed. Benefiting from the hierarchical characteristics of the FPTS, the e-skin exhibits a high sensitivity of 0.163 kPa −1 (<20 kPa), wide pressure sensing range of 0–200 kPa, and ultrafast response/relaxation time of < 5.6 ms. In light of these salient features , versatile intelligent perception applications are explored. First, robot fingers are equipped with the FPTS e-skins and realize the traditional Chinese medicine pulse diagnosis of the radial artery in different positions and pressing degrees. Second, a flexible perception array with 100 FPTS e-skin units enables the spatial pressure mapping of flat/curved surfaces and the perception of different Braille characters. Most importantly, by making full use of the FPTS e-skin with a five-layer deep neural network , an intelligent gesture perception system is developed for accurate recognition (average accuracy: 98.33%) of sign language and precise control of the robot hand, which is of great significance for the development of intelligent sign language translation in artificial intelligence.
研究领域:
材料科学
·
机器人技术
·
人工智能
·
生物医学工程
✓ 已验证
产品关联
DOI
高影响因子
ADVANCED FUNCTIONAL MATERIALS
·
2025
·
IF 19
Dongze Lv,Ziwen Tang,Yingzhi Wang,Jiaquan Xu,YeJia Wu,Guoqiang Wu,Jin Xie
3D hand reconstruction is an advanced technology in human-machine interaction (HMI), robotic control, and medical rehabilitation applications. However, methods based on optical cameras and data gloves suffer from high equipment costs, complex algorithms, susceptibility to ambient light interference, and high drift of inertial measurement units (IMUs). Here, a soft electronic skin (E-skin) for 3D hand reconstruction, inspired by human biology is proposed, which integrates a multi-sensor fusion of stretchable carbon nanotubes (CNTs) strain sensors and microelectromechanical system (MEMS) ultrasonic transducers. A straightforward screen-printing process is introduced- to fabricate a multi-layer stacked structure of the E-skin. The substrates of both the E-skin and strain sensors use the same material, a polyethylene glycol (PEG) mixed polydimethylsiloxane (PDMS) adhesive (PPA), which strengthens the bonding between the layers. The optimized PPA exhibits a low modulus (186 kPa), high elongation (>220%), and strong adhesion (1.2 N cm −1 ), while the PPA-CNT composite strain sensor demonstrates excellent sensitivity linearity (0.99) and minimal resistance drift over 500 stretching cycles. The PPA material combines the waterproof and biocompatible properties of PDMS, while also achieving high adhesion and softness, allowing it to maintain conformal contact during finger bending (strain >70%) without glue or bandage. A Quantile Regression Neural Network (QRNN) algorithm is introduced to improve dynamic accuracy and robustness in finger joint angle detection. The system's application in gesture recognition and VR interaction is demonstrated, achieving high accuracy in sign language recognition and robust hand tracking.
研究领域:
生物医学工程
·
柔性电子学
·
人机交互
·
机器人技术
·
传感技术
·
人工智能
·
虚拟现实
·
信号处理
·
材料科学
·
纳米技术
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）