Innovations in Tactile Sensing: Microstructural Designs for Superior Flexible Sensor Performance

Advanced Functional Materials, Год журнала: 2024, Номер 34(44)

Опубликована: Июнь 22, 2024

Abstract Tactile sensors have garnered considerable interest for their capacity to detect and quantify tactile information. The incorporation of microstructural designs into flexible has emerged as a potent strategy augment sensitivity pressure variations, thereby enhancing linearity, response spectrum, mechanical robustness. This review underscores the imperative progress in microstructured sensors. Subsequently, discourse transitions prevalent materials employed fabrication sensor electrodes, encapsulation layers, active sensing mediums, elucidating merits limitations. In‐depth discussions are devoted adorned with microstructures, including but not limited to, micropyramids, microhemispheres, micropillars, microporous configurations, microcracks, topological interconnections, multilevel constructs, random roughness, biomimetic microstructures inspired by flora fauna, accompanied exemplar studies from each category. Moreover, utility within realm intelligent environments is explicated, highlighting application monitoring physiological signals, detection sliding motions, discernment surface textures. culminates critical examination paramount challenges predicaments that must be surmounted further development enhance functional performance sensors, paving way integration advanced sensory systems.

Язык: Английский

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Advances in Machine Learning for Wearable Sensors

ACS Nano, Год журнала: 2024, Номер 18(34), С. 22734 - 22751

Опубликована: Авг. 15, 2024

Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role real-time sensing data analysis to provide clinical-grade information personalized healthcare. To this end, supervised unsupervised algorithms emerged as powerful tools, allowing the detection of complex patterns relationships large, high-dimensional sets. In Review, we aim delineate latest advancements sensors, focusing on key developments algorithmic techniques, applications, challenges intrinsic evolving landscape. Additionally, highlight potential machine-learning approaches enhance accuracy, reliability, interpretability sensor discuss opportunities limitations emerging field. Ultimately, our work aims a roadmap future research endeavors exciting rapidly area.

Язык: Английский

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A Programmable Electronic Skin with Event‐Driven In‐Sensor Touch Differential and Decision‐Making

Advanced Functional Materials, Год журнала: 2024, Номер unknown

Опубликована: Сен. 12, 2024

Abstract High‐precise, crosstalk‐free tactile perception offers an intuitive way for informative human‐machine interactions. However, the differentiation and labeling of touch position strength require substantial computational space due to cumbersome post‐processing parallel data. Herein, a programmable robust electronic skin (PR e‐skin) with event‐driven in‐sensor differential perception, solving inherent defects in von Neumann framework is introduced. The PR e‐skin realizes feature simplification reduction data transmission by integrating computing into sensing terminals. Furthermore, functional mode further greatly compresses untriggered redundant Benefiting from minimal concise dataset, can directly differentiate pressure swift response time (<0.3 ms). Robust carbon film ensures long‐term stable implementation (>10 000 cycles) architectural feature. In designable, continuous detection extensive range (210 kPa), which improvement 5.5 times, ultra‐sensitive extract trajectory sliding or rapping actions. Moreover, combined customized neural network, dual‐encryption recognition system constructed based on slide action, reaching high accuracy ≈98%, reveals great potential intelligent interaction security.

Язык: Английский

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Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review

Journal of Personalized Medicine, Год журнала: 2024, Номер 14(2), С. 203 - 203

Опубликована: Фев. 13, 2024

This review investigates the convergence of artificial intelligence (AI) and personalized health monitoring through wearable devices, classifying them into three distinct categories: bio-electrical, bio-impedance electro-chemical, electro-mechanical. Wearable devices have emerged as promising tools for monitoring, utilizing machine learning to distill meaningful insights from expansive datasets they capture. Within bio-electrical category, these employ biosignal data, such electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), etc., monitor assess health. The electro-chemical category focuses on measuring physiological signals, including glucose levels electrolytes, offering a holistic understanding wearer’s state. Lastly, electro-mechanical encompasses designed capture motion physical activity providing valuable an individual’s behavior. critically evaluates integration algorithms within illuminating their potential revolutionize healthcare. Emphasizing early detection, timely intervention, provision lifestyle recommendations, paper outlines how amalgamation advanced techniques with can pave way more effective individualized healthcare solutions. exploration this intersection promises paradigm shift, heralding new era in innovation well-being.

Язык: Английский

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3D Heterogeneous Sensing System for Multimode Parrallel Signal No‐Spatiotemporal Misalignment Recognition

Advanced Materials, Год журнала: 2024, Номер unknown

Опубликована: Дек. 11, 2024

The spatiotemporal error caused by planar tiled structure design and the waste of communication resources brought on transmission a single channel are two challenges facing development multifunctional intelligent sensors with high-density integration. A homo-spatiotemporal multisensory parallel system (HMPTs) is expanded to realize multisignal no-spatiotemporal misalignment recognition efficient transmission. First, this optimizes distribution sensors, completes 3D vertical heterogeneous layout four achieves material multi-information detection at place deviation. Additionally, couples transmittes multiple sensory signals, delivering fourfold increase in efficiency one-third power consumption compared single-channel system. Finally, used for mixed materials, human-computer interaction assignment materials VR, demonstrating great accuracy HMPTs as well its feasibility practical application. This an priori effort enhance machine perception accuracy, improve signal effectiveness, advance human-machine-object triadic

Язык: Английский

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A deep learning–enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life

Proceedings of the National Academy of Sciences, Год журнала: 2025, Номер 122(7)

Опубликована: Фев. 11, 2025

In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality preventing chronic conditions. However, the requirements device–skin coupling in electrophysiological systems hinder comfort reliability night wearing. Here, we report a washable, skin-compatible garment system that captures local skin strain signals under weak without positioning or preparation requirements. A printed textile-based sensor array responds to from 0.1 10% with gauge factor as high 100 shows independence extrinsic motion artifacts via strain-isolating pattern design. Through reversible starching treatment, ink penetration depth during direct printing on garments is controlled achieve batch-to-batch performance variation <10%. Coupled deep learning, explainable AI, transfer learning data processing, capable classifying six states an accuracy 98.6%, maintaining excellent explainability (classification low bias) generalization (95% new users few-shot less than 15 samples per class) practical applications, paving way next-generation daily healthcare management.

Язык: Английский

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Recent Advances in Smart Fabric-Type Wearable Electronics toward Comfortable Wearing

Energies, Год журнала: 2024, Номер 17(11), С. 2627 - 2627

Опубликована: Май 29, 2024

With the improvement of energy density and sensing accuracy wearable devices, there is increasing interest in applying electronics daily life. However, traditional rigid plate-structured devices cannot meet human body’s wearing habits make users may feel uncomfortable after them for a long time. Fabric-type can be conformably coated on skin without discomfort from mismatches mechanical properties between body electronics. Although state-of-the-art textile-based have shown unique advantages field e-textiles, real-world scenarios often involve stretching, bending, wetting. Further efforts should made to achieve “comfortable wearing” due great challenge achieving both promising electrical comfort single device. This review presents comprehensive overview advances smart fabric-based toward comfortable wearing, emphasizing their stretchability, hydrophobicity, air permeability, stability, color-change abilities. Through addressing challenges that persist fabric-type electronics, we are optimistic these will soon ubiquitous our lives, offering exceptionally experiences health monitoring, sports performance tracking, even fashion, paving way more technologically advanced future.

Язык: Английский

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Triboelectric‐Inertial Sensing Glove Enhanced by Charge‐Retained Strategy for Human‐Machine Interaction

Advanced Science, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 22, 2024

Abstract As technology advances, human‐machine interaction (HMI) demands more intuitive and natural methods. To meet this demand, smart gloves, capable of capturing intricate hand movements, are emerging as vital HMI tools. Moreover, triboelectric‐based sensors, with their self‐powered, cost‐effective, material various characteristics, can offer promising solutions for enhancing existing glove systems. However, a key limitation these sensors is that charge leakage in the measurement circuit results only transient signals, rather than continuous changes. address issue, charge‐retained effectively prevents triboelectric signal attenuation developed, enabling accurate finger movements. This innovation forms foundation highly integrated system, functionality by combining signals inertial sensor data. The system showcases diverse range applications, including complex robotic control, virtual reality interaction, home lighting adjustments, interface operations. Furthermore, leveraging artificial intelligence (AI) techniques, achieves recognition sign language an impressive 99.38% accuracy. work presents approach sensing offering valuable insights developing future multifunctional proposed its dual‐mode AI integration, holds great potential revolutionizing domains user experiences.

Язык: Английский

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Biomaterials for Reliable Wearable Health Monitoring: Applications in Skin and Eye Integration

Biomaterials, Год журнала: 2024, Номер 314, С. 122862 - 122862

Опубликована: Сен. 30, 2024

Язык: Английский

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Ultrasensitive and Breathable Hydrogel Fiber‐Based Strain Sensors Enabled by Customized Crack Design for Wireless Sign Language Recognition

Advanced Functional Materials, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 26, 2024

Abstract Wearable strain sensors, capable of continuously detecting human movements, hold great application prospects in sign language gesture recognition to alleviate the daily communication barriers deaf and mute community. However, unsatisfactory sensing performance (such as low sensitivity, narrow detection range, etc.) wearing discomfort severely hinder their practical application. Here, high‐performance breathable hydrogel sensors are proposed by introducing an adjustable localized crack a closed‐loop connected fiber encapsulated porous elastomer films. Upon loading/unloading external strain, dynamic opening/closing pre‐cut causes rapid switching conductive path, resulting sharp changes resistance high sensitivity. Consequently, hydrogel‐based crack‐effect sensor exhibits superb sensitivity (GF up 3930), broad range (from 0.02% 80%), fast response/recovery time (78/52 ms), repeatability, structural stability. Based on capability accurately detect various strains across full wireless system is developed achieve accuracy 98.1% encoding decoding gestures with assistance machine learning, providing robust platform for efficient intelligibility barrier‐free communication.

Язык: Английский

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