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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 (June 2026)
CMUT-Based Force Sensing
Author: Kupnik Mario, Bretthauer Christian, Dorsam Jan Helge, Haugwitz Christoph, Herbst Felix, Kim Yoojeong, Lee Hyunjoo J., Merbeler Fabian, Rutsch Matthias, Soennecken Soren, Suppelt Sven, Wiemer Elena, Wismath Sonja Katharina
Published in: IEEE Sensors Journal (Volume: 26, Issue: 2, January 2026)
Summary Contributed by: Mario Kupnik (Author)
Force-sensing technology plays a vital role in measuring mechanical force using sensors and transducers. This work introduces capacitive micromachined ultrasonic transducers (CMUTs) as a groundbreaking approach to force sensing, showcasing their advantages over conventional strain gauges. The sensing principle relies on detecting resonance frequency shifts caused by force-induced structural deformation, enabling precise frequency readout. Despite slightly lower uncertainty metrics, CMUTs offer superior scalability and integration potential for highly miniaturized applications
Distributed Stress Sensing in Polarization-Maintaining Fiber Under Circumferential Stress
Author: Zhang Hongxia, Jia Dagong, Li Tianyue, Li Zhihong, Liu Tiegen, Liu Yan, Song Rencheng, Zhang Bozhi
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Zhang Hongxia (Author)
Real-world engineering applications require continuous monitoring of structural stress. This study presents a distributed sensing method using Polarization-maintaining fiber (PMF) for circumferential stress monitoring via white-light interferometry, detecting subtle pressure changes around the fiber. Validated by 0–26 MPa pressure tank tests and 55 m downhole experiments, it demonstrated a linear response and 2.5 dB/MPa sensitivity, making it a robust solution for circumferential pressure sensing in extreme environments.
3D printing is revolutionizing the production of a wide range of sensors, allowing customization at a lower cost than conventional manufacturing techniques. This paper presents an artificial hair-like sensor (AHS) inspired by the flat hair-like mechanoreceptor of the adult Buthus occitanus scorpion. The sensor has been 3D-printed for airflow velocity measurements. Potential applications include meteorology, robotics and autonomous drones, biomedical engineering, flow cameras for multi-point measurements, and more.
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Mavrogiannis (Author)
Gait analysis is crucial in diagnosing and managing mobility disorders. This study utilizes low-cost smart insoles with embroidered textile capacitive sensors and an inertial measurement unit (IMU) to recognize the four basic phases of the human gait cycle. It uses a long short-term memory (LSTM)-based neural network trained on collected data, achieving 90% accuracy and 87.1% F1 macro score across exercises with variable gait speeds, offering an affordable gait analysis solution.
Deep Learning-Integrated Agarose-Coated Micro-Loop Fiber Resonator for Relative Humidity Measurement
Author: Zain Huda, Qureshi Khurram Karim
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Huda Adnan Zain (Author)
Fiber-optic humidity sensors, especially those with resonators, produce rich spectral data that leads to expensive, slow processing. This work integrates deep learning with an agarose-coated resonator sensor to address this challenge. The approach achieved a sensitivity of 0.2037 dB/%RH and a convolutional neural network (CNN) accuracy of ~98%. It highlights the potential of combining optical sensing with AI, offering a promising solution for environmental monitoring, healthcare, and real-time humidity tracking.
Unified Neural Network-Based Shape Sensing for Continuum Robots Using Fiber Bragg Gratings
Author: Dong Xin, Axinte Dragos, Elsayed Belal, Jones Simon D., Mohammad Abdelkhalick, Shi Hongshen, Wild Samuel, Zeng Tianyi
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Samuel Wild (Author)
Continuum robots (CRs) or miniaturised robotic snakes need to know their own shape to navigate accurately in confined spaces. Fibre Bragg grating (FBG) sensors offer a path, but deployable straight-core versions often suffer from twist-induced inaccuracies. This paper presents a shape-sensing method using FBG technology combined with a neural network to correct these errors. Validated across two robots of different sizes, it enables real-time, camera-free shape sensing.
Analysing past patterns in machine data within industrial settings can enhance predictive maintenance and improve overall equipment effectiveness (OEE). This study presents a lightweight late-fusion soft sensor model that benchmarks a deep learning forecasting approach using real manufacturing data. It compares various popular techniques and combines multiple models to increase accuracy. This method provides more reliable predictions, enhances performance, minimizes downtime, and boosts efficiency in complex, real-world factory environments.
Foreground Attention Loss and Attention-Guided Convolution for Remote Sensing Object Detection
Author: Tian Hongxian, Li Yunyue, Liu Jueting, Wang Zehua, Wang Zilong, Xu Tingting, Xu Zishan, Yang Wei, chen wei
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Zilong Wang (Author)
Object interpretation in satellite or aerial images is challenging due to small size, complex backgrounds, indistinguishable features, and varying orientations. Enhancing remote-sensing images can significantly improve their applications in fields like agriculture, urban planning, military surveillance, and disaster response. This paper introduces a lightweight discover→focus approach: Foreground Attention Loss learns an object-density attention map, and Attention-Guided Convolution shifts sampling toward foreground without extra offset branches, substantially improving accuracy with minimal overhead.
Anomaly Detection in Industrial Networks: Current State, Classification, and Key Challenges
Author: Kuchar Karel, Fujdiak Radek
Published in: IEEE Sensors Journal (Volume: 25, Issue: 3, February 2025)
Summary Contributed by: Fynn Foerger (Author)
Magnetic field measurements play a crucial role in medical diagnostics, industrial monitoring, electronic testing, and scientific research. However, traditional volumetric magnetic field measurements can be challenging and time-consuming. This research introduces a 3D magnetic field camera concept featuring surface-mounted sensors that captures detailed magnetic field patterns in under one second. This device can reconstruct magnetic fields at 10 Hz using physics-based methods, opening new opportunities in field validation and generation.
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Fynn Foerger (Author)
Magnetic field measurements play a crucial role in medical diagnostics, industrial monitoring, electronic testing, and scientific research. However, traditional volumetric magnetic field measurements can be challenging and time-consuming. This research introduces a 3D magnetic field camera concept featuring surface-mounted sensors that captures detailed magnetic field patterns in under one second. This device can reconstruct magnetic fields at 10 Hz using physics-based methods, opening new opportunities in field validation and generation.
The recent COVID outbreaks highlighted the need for breathing rate monitoring and increased the demand for hospitalized patients. Monitoring breathing rate is vital for diagnosing diseases and observing patients with pulmonary conditions. The pros and cons of different techniques are studied and categorized under contact and remote modes of respiratory monitoring systems. Various Radar-based methods found to be more suitable for respiration monitoring are discussed.
Radar detection of smaller targets requires lowering the radar cross-section and velocity thresholds. With it, an abundance of target signatures gets generated, making it necessary to classify only relevant targets. Micro-motions of targets are significant characteristics. Micro-Doppler signatures have emerged as an effective method of classifying such targets. The study presents a systematic review of various micro-Doppler-based radar target signature analysis and classification techniques.
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