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Smart Steering Wheel: Design of IoMT-Based Non-Invasive Driver Health Monitoring System to Enhance Road Safety

IET INTELLIGENT TRANSPORT systems 2025年第1期19卷

作者： Khan, Muhammad Adil Chen, Mu Nawaz, Tahir Sedky, Mohamed Sheikh, Muhammad Bashir, Ali Kashif Hassan, Sohail Changan Univ Dept Comp Sci & Technol Xian Peoples R China Natl Univ Sci & Technol Dept Mechatron Engn Islamabad Pakistan Natl Ctr Robot & Automat NCRA Islamabad Pakistan Staffordshire Univ Fac Comp Engn & Sci Stoke On Trent England Univ Warwick Warwick Mfg Grp WMG Coventry England Manchester Metropolitan Univ Dept Comp & Math Manchester England Ascens St John Hosp Dept Cardiac Electrophysiol Detroit MI USA

The integration of Internet of Things (IoT) technology and medical devices in healthcare is termed the Internet of Medical Things (IoMT). This advancement holds promise for numerous applications aimed at mitigating the risk of loss of life through physiological signal monitoring. As the number of road accidents is rapidly increasing, a substantial number of car crashes occur due to medical conditions. Therefore, the need remains to develop an effective solution to enable the prevention of such accidents for enhanced road safety. Unlike existing approaches, this paper proposes a holistic IoMT-based non-invasive driver health monitoring system (DHMS) to monitor important vital signs for detecting abnormal health conditions. The proposed system consists of an embedded system, edge computing, cloud computing, and a mobile application with an alert system, to offer an end-to-end unified solution for driver physiological signal monitoring to detect abnormal health conditions that might lead to a road accident. The system is particularly suited to aid (elderly) people with medical conditions and can also be used for public transport to ensure passenger safety. A detailed experimental evaluation of the proposed system has been performed and its performance accuracy compared with standard medical devices, along with quality factors including usability, portability, and effective sensor placement.

关键词： advanced driver assistance systems health care internet of things road safety

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Soft Micropneumatic Touchpad

ADVANCED INTELLIGENT SYSTEMS

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advanced INTELLIGENT systems 2025年第2期7卷

作者： Lampinen, Vilma Pihlajamaki, Mika Koivikko, Anastasia Sariola, Veikko Tampere Univ Fac Med & Hlth Technol Korkeakoulunkatu 3 PL 692 Tampere 33014 Finland

Soft tactile sensors outputting fluidic signals have many potential applications in soft microfluidic devices and soft robots. However, existing systems have been limited to a single or a few sensors in parallel, so they are not comparable to the state-of-the-art electrical resistive and capacitive touchpads, which can detect rich tactile information, including touch location, pressure, area, and even multiple touches simultaneously. This work reports a soft micropneumatic touchpad. The touchpad consists of 32 pneumatic channels inside soft elastomer, with 16 channels aligned row-wise and 16 column-wise. The flow resistance of each channel is measured using a pressure divider. When the pad is touched, the cross-sectional area of the channels close to the contact location deforms, which changes the flow resistance of those channels. With 32 sensing channels, the location, depth, area of the contact, and even two simultaneous touches can be detected. Letters hand-written on the touchpad can be reconstructed from the measured data. With the assumption of sparsity, a tactile pressure map, with a value at each 16 x 16 grid point, can also be reconstructed. This work opens a path to replace electronic tactile sensors in soft devices with all-fluidic alternatives. sensors outputting fluidic signals would allow replacing electrical or optical sensors in soft devices with their fluidic alternatives. This work reports a soft elastomeric touchpad consisting of 32 pneumatic microchannels. When touched, the cross-sectional area and flow resistance of the channels near the contact location change, allowing the touchpad to detect contact location, depth, area, and even multiple simultaneous *** (c) 2024 WILEY-VCH GmbH

关键词： microfluidics pneumatics sensor array soft sensors soft touchpad

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Visualization Detection of Ultralow Temperature Based on Flexible Cross-linked Polymer systems

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advanced FUNCTIONAL MATERIALS 2025年第10期35卷

作者： Guo, Fengling Chen, Yuanjin Li, Chen Wang, xiaojuan Li, Qiankun He, Meiyi Hou, Hui Yang, Chaolong Chongqing Univ Technol Sch Mat Sci & Engn Chongqing 400054 Peoples R China South China Univ Technol Guangdong Prov Key Lab Luminescence Mol Aggregates Guangzhou 510640 Peoples R China

Ultralow temperature storage has sparked considerable attention with the development of the economy, showing promising applications ranging from biomedical to national defense and other fields. However, the development of ultralow temperature detection is constrained by the brittleness of current materials at low temperatures and the complexity of detection techniques. Consequently, the challenge exists in finding efficient solutions to material tolerance issues and achieving rapid detection of ultralow temperature. Herein, a novel flexible cross-linked polymer TPTA@PU film with long afterglow, high phosphorescence quantum efficiency, and excellent mechanical properties are successfully fabricated. Interestingly, the obtained TPTA@PU films demonstrate a notable thermoresponsive behavior, with the afterglow color shifting rapidly from blue to green within the temperature ranges from 80 to 280 K. Additionally, there is a positive linear correlation between the RGB values of the afterglow color and the corresponding temperature. Based on these prominent features, an ultralow temperature sensor is realized by utilizing TPTA@PU films as thermoresponsive elements. This work can be expected to provide more inspiration and possibilities for using RTP materials in a more cutting-edge field.

关键词： flexible materials room temperature phosphorescence temperature monitoring thermoresponsive ultralow temperature

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In-depth development of temperature insensitive refractive index Fabry-Perot optical sensor 2

In-depth development of temperature insensitive refractive i...

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2nd International conference on Optical Communication and Optical Information Processing, OCOIP 2024

作者： Gao, Yuda Luo, Yanhua Institute of Fiber Optics Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China

ISBN: (数字)9781510688995

ISBN: (纸本)9781510688988

sensors based on optical fiber interference structures offer benefits such as high sensitivity, simple fabrication and good compatibility, and are widely used in fields like biological monitoring, chemical engineering, and environmental monitoring. However, these sensors often face unavoidable cross-sensitivity issues between temperature and refractive index. Therefore, developing a new optical fiber refractive index sensor that is insensitive to temperature and has a broad refractive index measurement range remains a significant research issue. In this work, proposing a temperature-insensitive refractive index sensor featuring a two-stage Fabry-Perot (F-P) cavity made from single-mode fiber (SMF), capillary fiber (CF), and no core fiber (NCF) based on the principle of mode interference. The research experimentally investigates the sensor's sensitivity to temperature and refractive index. Results indicate that the sensor's amplitude correlates linearly with the refractive index in both optical and Fourier domains, covering a refractive index range of 1.3164 to 1.4215. In the Fourier domains, sensitivities of - 0.7184/refractive index unit (RIU) and - 0.2672/RIU are recorded at two distinct positions, respectively. A direct relationship between the amplitude and the refractive index is established. Additionally, the proposed sensor is almost insensitive to temperature in both the wavelength and Fourier domains. At the same time, experimental validation using water, ethanol, isopropanol, and glycerin solutions with varying refractive indexes, yielding results that align well with the established refractive index relationship. © 2025 SPIE.

关键词： Fabry-Perot interferometers

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Sustainable and Tunable Synaptic Electrolyte-Gated Organic Field-Effect Transistors (EGOFETs) for Light Adaptive Visual Perceptive systems

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advanced FUNCTIONAL MATERIALS 2025年第11期35卷

作者： Serghiou, Theodoros Fernandes, Jose Diego Karthikeyan, Vaithinathan Assi, Dani S. Vieira, Douglas Henrique Alves, Neri Kettle, Jeff Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Scotland Sao Paulo State Univ UNESP Sch Technol & Appl Sci Dept Phys BR-19060900 Presidente Prudente SP Brazil Hong Kong Metropolitan Univ Sch Sci & Technol Ho Man Tin 11-F Hong Kong Peoples R China

The recent advances in optic neuromorphic devices have led to a subsequent rise in the development of energy-efficient artificial-vision systems. While the energy consumption of such devices is known to be much lower than conventional vision systems, it is known that manufacturing accounts for the largest share of the climate impact in microelectronics, dominating over the product use phase. Thus, there is a need to develop sustainable manufacturing processes and to adopt low-impact materials for hardware solutions of the future. In this study, an Electrolyte-Gated Organic Field-effect Transistor (EGOFET) is experimentally demonstrated for the implementation of a high-performing synaptic optical sensor using sustainable materials that degrade to benign products at the End of Life (EoL). The device shows remarkable light response with maximum Paired-Pulse Facilitation (PPF) Index of up to 151% at a light power density of 38 mu W cm-2, which enables artificial synaptic applications with an average power consumption as low as 2.4 pJ for each training process, representing one of the best among the reported results. To demonstrate the tunability of the vision system, an ensemble decision tree is used to enable the EGOFET to distinguish and remember different primary colors at different power densities with 95.6% accuracy.

关键词： electro-gated transistors neuromorphic imaging system optical synaptic devices organic phototransistors sustainable materials

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A Comprehensive Survey on Traffic Light Detection sensors for Vehicle Safety and Automatic Emergency Braking systems Using Deep Learning Techniques 3rd

A Comprehensive Survey on Traffic Light Detection Sensors fo...

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3rd International conference on Intelligent systems and Sustainable Computing, ICISSC 2023

作者： Gowri, B. Shyamala Priya, G. Vishnu Anjana, S. Arjun, V. Kodal, Devendraa Department of Computer Science and Engineering Easwari Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819783540

The integration of Traffic Light Detection (TLD) systems with advanced Emergency Braking systems (AEBS) marks a critical milestone in enhancing road safety and paving the way for advanced autonomous driving. This survey paper provides a panoramic and extensive overview of the state-of-the-art TLD solutions leveraging sensors and deep learning techniques. With an increasing emphasis on accident prevention and traffic management, the intersection of TLD and AEBS has become a focal point of research and development. This survey begins by elucidating the fundamental challenges associated with TLD, including varying environmental conditions, occlusions, and complex traffic scenarios. We explore the pivotal role of sensors such as cameras, LiDAR, and radar in providing the requisite data for TLD, and delve into the intricacies of sensor fusion techniques for enhanced perception. Deep Learning has emerged as a cornerstone technology in TLD, enabling robust object detection, classification, and real-time decision-making. We meticulously analyze a spectrum of deep learning architectures including Single-Shot Detectors (SSD), Faster R-CNN, YOLO, and custom-designed networks tailored for TLD applications. Furthermore, the survey examines critical components of the TLD pipeline, encompassing data collection, preprocessing, model training, real-time inference, and integration with AEBS. Emphasis is placed on real-time constraints, multi-modal sensor fusion, and adaptability to diverse traffic light configurations. The paper also delves into the significance of accurate traffic light state prediction, going beyond mere detection to anticipate traffic light changes and optimize vehicle control actions. Human-centric interaction and privacy concerns are addressed, encompassing driver warnings, user interfaces, and data anonymization strategies. Moreover, the survey discusses the importance of safety, validation, and collaboration within the TLD and AEBS ecosystem, emphasizing compl

关键词： Road and street markings

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An Antifreeze Gel as Strain sensors and Machine Learning Assisted Intelligent Motion Monitoring of Triboelectric Nanogenerators in Extreme Environments

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advanced FUNCTIONAL MATERIALS 2025年

作者： Han, Delong Cai, Yuting Wang, xinze Zhang, Weining Li, xusheng Hou, Zhaoru Liu, Jiahui Song, Dengke xu, Wenlong Ludong Univ Sch Chem & Mat Sci Yantai 264025 Peoples R China

Traditional hydrogels tend to freeze and lose performance at low temperatures, limiting their applications. Additionally, hydrogels need to exhibit low hysteresis, excellent cycling stability, and self-adhesion to ensure high-quality signal acquisition in complex environments. To address this challenge, this study designed a dual-network gel in a glycerol (Gly)/H2O solvent system. Due to the combination of chemical and physical crosslinking (hydrogen bonding and electrostatic interactions), the resulting gel exhibits skin-adaptive modulus, high cycling stability, anti-freezing ability, body temperature-induced adhesion, and excellent electrical performance, making it suitable for wearable sensors at low temperatures. Based on this gel, a single-electrode triboelectric nanogenerator (gel-TENG) is developed, achieving efficient conversion of mechanical energy into electrical energy. Further applied to a smart insole, it successfully enabled real-time visualization of plantar pressure distribution and skiing motion recognition. Using a random forest machine learning algorithm, the system accurately classified 11 basic skiing motions, achieving a classification accuracy of 97.1%. This study advances flexible sensors and self-powered systems, supporting intelligent materials research in extreme environments.

关键词： antifreeze gel machine learning self-power sensor

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The impact of sustainable management systems on the sustainable development of water resources in agriculture and the reduction of agricultural carbon footprint 2

The impact of sustainable management systems on the sustaina...

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2nd International conference on Big Data, Computational Intelligence, and applications, BDCIA 2024

作者： Jiang, Zihao University of Illinois Urbana - Champaign Champaign United States

ISBN: (纸本)9781510689053

Environmental and economic Sustainability increasingly demands effective water resource management and reduction of the carbon footprint in agriculture. This meta-analysis synthesizes the results of 18 studies published in 2015-2024 to assess the performance of sustainable management systems in optimizing agricultural water resources and reducing carbon emissions. The review specifically focuses on integrating the Internet of Things and Artificial Intelligence into agricultural water management, employing advanced computational methods, including text clustering algorithms, support vector machines (SVM), and deep learning models for analysis. Results show that IoT-driven water management systems achieve, on average, a water economy of 27.3% (95% CI: 24.1-30.5%) against conventional approaches and reduce agricultural emissions by approximately 31.2% (95% CI: 28.4-34.0%). AI-driven optimization algorithms demonstrate remarkable effectiveness;machine learning algorithms provide an average increase in water use efficiency by 35.6% in precision irrigation applications, while deep learning models achieve 42.3% accuracy in crop water requirement predictions. The computational analysis revealed that integrated IoT-AI systems show the highest potential for agricultural Sustainability, with neural network models achieving 89.5% accuracy in resource optimization predictions. The results, therefore, show that large-scale diffusion of these technologies can make major contributions to agricultural sustainability goals while sustaining or improving crop yields. © 2025 SPIE.

关键词： Carbon capture and utilization

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Grasp Stability Assessment Through Spatio-Temporal Attention Mechanism and Adaptive Gate Fusion

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IEEE sensorS JOURNAL 2025年第1期25卷 1872-1884页

作者： Li, Song Sun, Wei Liang, Qiaokang Sun, Jian Liu, Chongpei Hunan Univ Coll Elect & Informat Engn Natl Engn Res Ctr Robot Visual Percept & Control T Changsha 410000 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410000 Peoples R China Hunan Univ Coll Robot Changsha 410000 Peoples R China

In the field of robotic grasping and manipulation, accurately assessing the stability of handheld objects plays a critical role in achieving proficient manipulation. Methods relying solely on visual or tactile information for slip detection often have limited applicability across different scenarios. Relevant studies have shown that combining visual and tactile sensing can significantly improve grasping performance. This study proposes a novel deep neural network architecture, specifically adopting a spatiotemporal attention mechanism to fuse multilevel spatiotemporal features, effectively integrating deep high-level features with shallow low-level features. It extracts important slip features across temporal and spatial dimensions from both visual RGB image sequences and tactile image sequences, thereby facilitating stability prediction. The gating mechanism builds a resilient network architecture that adaptively fuses features with appropriate weights, maintaining highly accurate and robust predictive performance even when sensor signal quality degrades. Validation results from both public and custom datasets demonstrate that the proposed model is highly effective in accurately predicting grasp stability, even in the presence of missing, occluded, noisy, or corrupted visual or tactile sensor signals. The practicality of this approach extends to various downstream applications in robotics, including grasp force control, generation of grasping strategies, and proficient manipulation in challenging scenarios.

关键词： Visualization Adaptation models Attention mechanisms Grasping Logic gates Robot sensing systems Feature extraction Stability analysis Spatiotemporal phenomena sensors Grasp outcomes assessment multimodal fusion slip detection tactile sensing visual-tactile perception

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A Flexible Hybrid Resource Allocation Model for Enhanced Efficiency using AI in Wireless sensor Networks 3

A Flexible Hybrid Resource Allocation Model for Enhanced Eff...

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3rd International conference on Electronics and Renewable systems, ICEARS 2025

作者： Endla, Purushotham Mohandas, R. Saikia, Achinta Lakshmi, C. Karthikeyan, G. Moharekar, Tejashree Tejpal Sr University School of Sciences and Humanities Department of Physics Telangana Warangal506371 India Chennai Institute of Technology Department of Ece Chennai India Mariani College Department of Statistics Mariani Assam Jorhat785634 India Saveetha Engineering College Chennai India Karpagam College of Engineering Department of Artificial Intelligence & Data Science Coimbatore India Shivaji University Yashwantrao Chavan School of Rural Development Maharashtra Kolhapur India

ISBN: (纸本)9798331509675

Wireless sensor Networks (WSNs) have become a cornerstone technology in various fields, including environmental monitoring, industrial automation, and healthcare. This paper presents a novel hybrid resource allocation model designed to enhance efficiency in Wireless sensor Networks (WSNs) using advanced Artificial Intelligence (AI) techniques. The proposed model integrates static and dynamic resource allocation strategies with machine learning and reinforcement learning to optimize key network resources such as energy, bandwidth, and processing power. Comprehensive evaluations demonstrate that the proposed model outperforms nine existing models across multiple metrics, including energy efficiency, network lifetime, throughput, delay, latency, and accuracy of resource allocation. The proposed model achieved an energy efficiency of 0.98 J/bit, extended network lifetime to 620 days, and maintained a high throughput of 620 Kbps. Notably, the model demonstrated the lowest delay (90 ms) and latency (180 ms) while achieving the highest resource allocation accuracy of 97.8%. These results highlight the model's capability to dynamically adjust resource allocation in response to real-time network conditions, ensuring optimal performance and resource utilization. The proposed model's success in efficiently managing WSN resources marks a significant advancement in the field, offering a robust solution for various WSN applications requiring high efficiency and reliability. © 2025 IEEE.

关键词： Artificial Intelligence Energy Efficiency Machine Learning Network Lifetime Reinforcement Learning Resource Allocation Wireless sensor Networks

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