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"IEEE Sensors Alert" is a pilot project of the IEEE Sensors Council. Started as one of its new initiatives, this weekly digest publishes teasers and condensed versions of our journal papers in layperson's language.
Articles Posted in the Month (July 2025)
Research on Intelligent Diagnosis Method of Swallowing Signal Based on Complex Electrical Impedance Myography
Author: Yu Shaoshuai, Chu Xu, Fu Letian, Fu Zhang, Liu Qi, Yang Yuxiang
Published in: IEEE Sensors Journal (Volume: 25, Issue: 4, February 2025)
Summary Contributed by: Payal Savani
Dysphagia or swallowing disorders occur when the muscles or nerves involved in swallowing malfunction. Early and accurate detection will reduce risks to health and life. This study presents an innovative diagnostic framework that integrates Complex Electrical Impedance Myography (C-EIM) with advanced machine learning algorithms that analyze both amplitude and phase data. The smart system achieves an accuracy of 95.2%, demonstrating strong potential in early diagnosis and clinical decision-making for managing dysphagia.
Microneedle Uric Acid Biosensor With Graphite Ink and Electrodeposited MWCNT
Author: Kameoka Jun, Kawahira Hiroshi, Nohgi Toru, Tu Yifan
Published in: IEEE Sensors Journal (Volume: 25, Issue: 3, February 2025)
Summary Contributed by: Jun Kameoka (Author)
Uric acid is a well-established biomarker for gout. Recent studies have suggested its prospective as an indicator of visceral fat accumulation, particularly in monitoring metabolic syndrome. This paper introduces a microneedle-based uric acid biosensor using graphite ink and electrodeposited multi-walled carbon nanotubes (MWCNTs). The device supports non-invasive, real-time monitoring of uric acid concentrations in interstitial skin fluid (ISF). It has excellent potential for point-of-care applications and integration into wearable health technologies.
Parallel Detection of Mixed Pesticides Based on Dual Quantum Dot/Porous Silicon Optical Biosensors
Author: Jia Zhenhong, Cao Jianghong, Huang Xiaohui, Lv Xiaoyi, Wang Jiajia, Yang Jie, Yue Haitao
Published in: IEEE Sensors Journal (Volume: 24, Issue: 23, December 2024)
Summary Contributed by: Saurabh Dubey
Pesticide detection is vital for protecting human health and the environment. The proposed optical biosensor uses dual quantum dots and porous silicon technology to provide a cost-effective and real-time solution for detecting mixed pesticides. With high sensitivity, stability, and low cross-reactivity, this biosensor can efficiently monitor pesticides, thus improving food and environmental safety. Its application in resource-limited areas helps reduce costs and facilitates automated pesticide monitoring and detection.
Liquid Crystal Microlens Arrays Based on Aluminum-Doped Zinc Oxide Oriented Microstructure Facilitate Light Field Image Resolution Enhancement
Author: Li Hui, Li Zikang, Qiao Chuan, Wu Yuntao
Published in: IEEE Sensors Journal (Volume: 25, Issue: 4, February 2025)
Summary Contributed by: Hui Li (Author)
Light-field cameras are excellent at capturing comprehensive scene information; however, they often face issues due to unstable liquid crystal (LC) alignment in their liquid crystal microlens arrays (LC-MLAs). This study introduces an LC-MLA imaging system based on an aluminum-doped zinc oxide (AZO) alignment layer, achieving ordered LC alignment. The system enhances image resolution by integrating full variational denoising and convex optimization, contributing significantly to the advancements in light-field imaging technology.
Design and Test of a High-Sensitivity MEMS Capacitive Resonator for Photoacoustic Gas Detection
Author: Shi Junhui, Gao Da, Ren Danyang, Wang Yuqi, Yin Yonggang
Published in: IEEE Sensors Journal (Volume: 24, Issue: 24, December 2024)
Summary Contributed by: Anupama
Photoacoustic spectroscopy (PAS) enables precise trace gas detection by converting absorbed laser energy into acoustic signals. This study introduces a high-sensitivity micro-electromechanical system (MEMS) capacitive resonator for photoacoustic gas detection designed by altering the overlapping areas to minimize gas damping. Experimental results demonstrate increased sensitivity and stability of the resonator and improved trace gas detection methods. The advancement paves the way for more sensitive and accurate environmental monitoring and other applications.
HOT Watch: IoT-Based Wearable Health Monitoring System
Author: Jhansi Bharathi Madavarapu, S. Nachiyappan, S. Rajarajeswari, N. Anusha, Nirmala Venkatachalam, Rahul Charan Bose Madavarapu, A. Ahilan
Published in: IEEE Sensors Journal (Volume: 24, Issue: 15, October 2024)
Summary Contributed by: Saurabh Dubey
Heart rate oxygen rate temperature watch (HOT Watch) transforms IoT-based health monitoring by providing real-time tracking of vital signs with an accuracy exceeding 99.4%. It outperforms other leading devices available. Powered by IoT and the Pan-Tompkins algorithm(PTA), it efficiently processes health data and transmits it via Bluetooth for instant notification. GPS tracking and real-time alerts facilitate prompt medical responses, making it a dependable solution for personal and remote healthcare.
Discriminating Between Indoor and Outdoor Environments During Daily Living Activities Using Local Magnetic Field Characteristics and Machine Learning Techniques
Author: Vincenzo Marcianò, Andrea Cereatti, Stefano Bertuletti, Tecla Bonci, Lisa Alcock, Eran Gazit, Neil Ireson, Antonio Bevilacqua, Claudia Mazzà, Fabio Ciravegna, Silvia Del Din, Jeffrey M. Hausdorff, Georgiana Ifrim, Brian Caulfield, Marco Caruso
Published in: IEEE Sensors Journal (Volume: 25, Issue: 1, January 2025)
Summary Contributed by: Marco Caruso (Author)
Wearable sensors are essential for context-aware applications. But can they distinguish between indoor and outdoor environments? This study presents a novel method for distinguishing indoor and outdoor environments by analyzing magnetometer data and deep learning models to improve traditional inertial-based methods. It offers more reliable and high classification accuracy. The approach opens new possibilities for enhanced navigation and supports environmental detection in wearable and mobile health monitoring systems.
A Microwave Sensor Based on Grounded Coplanar Waveguide for Solid Material Measurement
Author: Liu Guohua, Gong YuXiang, Jiang Shuren, Zhang Jiaqi, Zhang Rui
Published in: IEEE Sensors Journal (Volume: 25, Issue: 3, February 2025)
Summary Contributed by: Liu Guohua (Author)
Accurate measurement of permittivity is crucial for characterizing the electromagnetic properties of materials. This study presents a compact, high-sensitivity microwave sensor utilizing a grounded coplanar waveguide (GCPW) integrated with a parallel interdigital capacitor (P-IDC) and split-ring resonator (SRR). This design enhances electric field concentration and signal isolation, enabling precise permittivity detection. The optimized GCPW and interdigital capacitor (IDC) parameters show significant potential in advanced communication and electronic applications.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 24, December 2024)
Summary Contributed by: Saurabh Dubey
Urinary tract infections (UTIs) are among the most common infections worldwide. This paper introduces an innovative gas-phase detection technology for UTI diagnostics that uses metal-oxide sensors and change-point detection (CPD) to efficiently identify bacterial growth through volatile organic compounds (VOCs) emissions. The approach showed promising results for rapid, non-invasive UTI detection, enabling real-time monitoring, reducing diagnostic delays, minimizing unnecessary antibiotic intake, and providing cost-effective point-of-care diagnostics with minimal equipment.
Nanoelectromechanical resonators are some of the most sensitive devices to external perturbations that can be built. However, reading nanoscale vibrational motion with the help of electrical signals in the background of noise from a profusion of sources can be very challenging. The method in this paper utilizes the materials’ intrinsic piezoresistivity to transduce the mechanical vibrations to electrical signals, effectively leveraging a simple circuit configuration compared to existing methods.
The researchers present a novel target classification technique incorporating mmWave radar and deep learning models to classify moving objects. The system provides a wide field of view by orienting the antenna in elevation and rotating it in the horizontal field. With 97 – 99 % accuracy, the proposed classification technique is a cost-effective and dependable system for a wide range of autonomous applications.
Vision-based tactile sensors (VBTS) are attracting attention for their application in robotics. As an innovative optical sensor, VBTS leverages tactile sensing to enhance the interpretation and utilization tactile information. The paper presents an overview of the hardware aspects of VBTS, including their technology, capabilities, challenges, and potential solutions. It provides insightful guidelines for optimizing the design and fabrication processes of VBTS to improve their performance.
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