Deep-Learning-Based Analysis of Electronic Skin Sensing Data DOI Creative Commons

Yu-Chen Guo,

Xidi Sun,

Lulu Li

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(5), P. 1615 - 1615

Published: March 6, 2025

E-skin is an integrated electronic system that can mimic the perceptual ability of human skin. Traditional analysis methods struggle to handle complex e-skin data, which include time series and multiple patterns, especially when dealing with intricate signals real-time responses. Recently, deep learning techniques, such as convolutional neural network, recurrent transformer methods, provide effective solutions automatically extract data features recognize significantly improving data. Deep not only capable handling multimodal but also response personalized predictions in dynamic environments. Nevertheless, problems insufficient annotation high demand for computational resources still limit application e-skin. Optimizing algorithms, efficiency, exploring hardware-algorithm co-designing will be key future development. This review aims present techniques applied inspiration subsequent researchers. We first summarize sources characteristics models applicable their applications analysis. Additionally, we discuss use e-skin, particularly health monitoring human-machine interactions, explore current challenges development directions.

Language: Английский

Layer-by-Layer Self-Assembled Honeycomb Structure Flexible Pressure Sensor Array for Gait Analysis and Motion Posture Recognition with the Assistance of the ResNet-50 Neural Network DOI
Hao Zhang,

Chunqing Yang,

Hui Xia

et al.

ACS Sensors, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

With the rapid emergence of flexible electronics, pressure sensors are importance in various fields. In this study, a dopamine-modified melamine sponge (MS) was used to prepare honeycomb structure carbon black (CB)/MXene-silicone rubber (SR)@MS sensor (CMSM) through layer-by-layer self-assembly technology. Using SR as binder construct not only improves mechanical properties but also provides more attachment sites for CB/MXene, enhancing stability conductive network. The CMSM exhibits high sensitivity (7.44 kPa-1), wide detection range (0-240 kPa), short response/recovery times (150 ms/180 ms), and excellent stability. addition, smart insole has been developed based on 6-unit array, achieving plantar detection. By combination ResNet-50 neural network algorithm with data under different postures, recognition 16 types human motion postures achieved, an accuracy rate 90.63%. This study proposes performance sensing capabilities, providing new ideas references design wearable devices.

Language: Английский

Citations

1
Multidimensional Nanochannel Regulation for High‐Performance Flexible Hydrovoltaic Sensing Devices DOI Open Access
Yongfeng Wang,

Changlei Ge,

Mingxu Wang

et al.

Advanced Functional Materials, Journal Year: 2025, Volume and Issue: unknown

Published: March 6, 2025

Abstract The evaporation‐induced hydrovoltaic effect represents a promising avenue for green energy harvesting and self‐powered ion sensing. However, the intricacies of designing solid‐liquid interface insufficient systematic research on influence parameters performance hinder advancement high‐performance devices. Herein, governing principles nanochannel size, material conductivity, surface properties, water evaporation based multidimensional regulation nanochannels by dip‐coating carbonization processes are systematically elucidated. Guided obtained mechanisms, flexible sensing device with photothermal conversion capability is prepared, exhibiting an open‐circuit voltage exceeding 3.5 V wide univalent range 10 −7 –10 −1 m . Ultimately, fabricated successfully serves electrolyte monitoring. These results elucidate correlation between controllable design solid–liquid interfaces (on structure, environmental factors) devices, paving way practical applications.

Language: Английский

Citations

0
Sustainable Materials Enabled Terahertz Functional Devices DOI Creative Commons
Baoning Wang,

Haolan Wang,

Ying Bao

et al.

Nano-Micro Letters, Journal Year: 2025, Volume and Issue: 17(1)

Published: April 11, 2025

Abstract Terahertz (THz) devices, owing to their distinctive optical properties, have achieved myriad applications in diverse domains including wireless communication, medical imaging therapy, hazardous substance detection, and environmental governance. Concurrently, mitigate the impact of electronic waste generated by traditional materials, sustainable materials-based THz functional devices are being explored for further research taking advantages eco-friendliness, cost-effective, enhanced safety, robust biodegradability biocompatibility. This review focuses on origins biological structures materials as well succinctly elucidates latest device fabrication, communication macromolecule detection sensors, environment monitoring biomedical therapeutic devices. We highlight recent protein-based monitoring. Besides, this explores developmental prospects integrating with presenting potential future.

Language: Английский

Citations

0
Preparation, Integration, and piezoresistive performance of MXene materials for multifunctional pressure sensor design DOI
Yingying Jin, Yuhua Wang

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 163035 - 163035

Published: April 1, 2025

Language: Английский

Citations

0
Developing an applicable electrochemical micro-sensor for highly specific detecting chlortetracycline in water with o-phenylenediamine-based molecularly imprinted polymer DOI
Yi Xing, Haifeng Xu, Min Zhang

et al.

Microchemical Journal, Journal Year: 2025, Volume and Issue: unknown, P. 113846 - 113846

Published: May 1, 2025

Language: Английский

Citations

0
Deep-Learning-Based Analysis of Electronic Skin Sensing Data DOI Creative Commons

Yu-Chen Guo,

Xidi Sun,

Lulu Li

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(5), P. 1615 - 1615

Published: March 6, 2025

E-skin is an integrated electronic system that can mimic the perceptual ability of human skin. Traditional analysis methods struggle to handle complex e-skin data, which include time series and multiple patterns, especially when dealing with intricate signals real-time responses. Recently, deep learning techniques, such as convolutional neural network, recurrent transformer methods, provide effective solutions automatically extract data features recognize significantly improving data. Deep not only capable handling multimodal but also response personalized predictions in dynamic environments. Nevertheless, problems insufficient annotation high demand for computational resources still limit application e-skin. Optimizing algorithms, efficiency, exploring hardware-algorithm co-designing will be key future development. This review aims present techniques applied inspiration subsequent researchers. We first summarize sources characteristics models applicable their applications analysis. Additionally, we discuss use e-skin, particularly health monitoring human-machine interactions, explore current challenges development directions.

Language: Английский

Citations

0