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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.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 10, May 2024)
Summary Contributed by: Alexander Schossmann (Author)
Strain measurement is central to mechanical engineering, structural health monitoring, and biomedical sensing. This paper presents a telemetric millimeter-wave strain sensor based on elastic metamaterial structures, consisting of a single-layer copper element embedded between two sheets of thermoplastic polyurethane (TPU). It detects mechanical strain by measuring frequency shifts due to structural deformation. The study also introduces a semi-analytical model of the underlying sensor effect, laying the groundwork for further optimization.
Published in: IEEE Sensors Journal (Volume: 26, Issue: 10, May 2026)
Summary Contributed by: Cheng-Ming Huang (Author)
Recognizing human activities is challenging due to the similarity in many movements. This paper presents a multimodal action recognition framework that combines dense and long-range joint relations of the human skeleton with dynamically selected visual information. The Sparse Joints Graph Convolutional Network connects important non-adjacent body joints, efficiently capturing global movement patterns. The approach improves recognition accuracy for confusing actions and has applications in healthcare, smart environments, and human-machine interaction.
Novel Label-Free Detection of Cancer Biomarkers Using T-Shape Channel and L-Shape Nanocavity in Si:HfO2 Ferroelectric DM-JLTFET With Gate Staggering Biosensor
Author: Karumuri Srinivasa Rao, Anil Kumar Rapolu, I Hend Alkhammash, K Girija Sravani, M Ramesh, Shaikh Mohd Rizwan Uddin, Wadhwa Girish
Published in: IEEE Sensors Journal (Volume: 26, Issue: 10, May 2026)
Cancer diagnosis requires tools that are fast, affordable, highly precise, and capable of detecting the disease at an early stage. This research introduces a breakthrough label-free biosensor that utilizes an innovative T-shaped channel and L-shaped nano-cavities embedded with a silicon-hafnium-dioxide (Si: HfO2) ferroelectric material. This ultra-small device successfully isolates and identifies colorectal, kidney, and bone cancer cells by detecting specific electrical changes, and it outperforms conventional point-of-care screening methods.
In Situ Temperature-Compensated Puncture Force Sensing for Brain Stereotactic Puncture Needle Using Fiber Bragg Grating
Author: Fang Ge, Wang Hongpeng, Huang Yanqi, Qin Yanding, Wang Longxin, Wen Zikang, Yi Liang, Zhe Guo
Published in: IEEE Sensors Journal (Volume: 26, Issue: 10, May 2026)
Summary Contributed by: Ge Fang (Author)
Brain stereotactic surgery demands accurate, real-time measurement of the interaction force between the puncture needle and brain tissue for surgical precision and safety. The proposed puncture needle integrates a co-planar Fiber Bragg Grating, enabling direct measurement of the tip puncture force. With in-situ temperature compensation, it maintains exceptional linearity across the entire measurement range. Its non-metallic, MRI-compatible design further supports image-guided neurosurgical intervention, as validated by in-vivo animal trials.
Published in: IEEE Sensors Journal (Volume: 26, Issue: 9, May 2026)
Summary Contributed by: Michihiko Nakano (Author)
Hydroquinone (HQ) is a harmful chemical found in industrial products and tobacco smoke, and poses various health risks, potentially damages DNA, and causes cancer. This paper presents a simple, rapid method for the electrical detection of HQ by measuring the electrical changes resulting from HQ-induced DNA damage. By accelerating the reaction with copper ions, detection can be completed within one hour, providing an affordable tool for environmental and health monitoring.
Can spontaneous electrical activity in nerve fibers be detected wirelessly using minimally invasive sensors? This study introduces magnetoelectric nanoparticles (MENPs) as futuristic wireless sensors for detecting electrical signals from nerve fibers, demonstrating varying sensitivities across different geometries and configurations. Using computational methods, the research provides a detailed quantification of the interaction between MENPs and nerve fibers, paving the way to the next generation of neural interfaces and neuroprostheses.
Wearable hand-control systems usually rely on electromyography (EMG) signals from muscles. This work introduces a wearable displacement myography (DMG) armband that simultaneously detects hand gestures, force levels, and hand aperture with substantial accuracy by measuring forearm muscle shape rather than muscle electrical activity. By combining advanced machine learning with compact sensing hardware, this approach enables real-time decoding of multiple hand movements, offering promising applications in assistive technologies and human-machine interfaces.
Olfactory, visual, auditory, and haptic interfaces collectively advance the human–computer interaction (HCI) experience. This paper introduces an eight-channel olfactory system mounted beneath a virtual reality (VR) headset. Wearable olfactory displays can significantly improve immersion in VR. Unlike conventional olfactory displays, this innovative system expands the range of reproducible odors while enabling low-latency delivery and precise intensity control, improving the vividness and realism of diverse interactive and multisensory VR experiences.
Published in: IEEE Sensors Journal (Volume: 26, Issue: 10, May 2026)
Summary Contributed by: Praveen Chopra (Author)
Industrial machines can develop fault conditions not encountered during model training, causing unreliable predictions from conventional diagnostic models. The proposed neuron-activation similarity-based approach compares the internal deep neural network responses with known fault patterns to distinguish known faults from previously unseen ones. This open-set framework improves the reliability of unseen faults without retraining or modifying existing diagnostic models, making industrial monitoring systems more robust, adaptable, and dependable in real-world operating environments.
Published in: IEEE Sensors Journal (Volume: 26, Issue: 8, April 2026)
Summary Contributed by: Razan Khalifeh (Author)
How can a robot recognize an object within hundreds of milliseconds while grasping it? This paper offers a solution through a flexible piezoelectric tactile-sensing electronic skin that enables robots to identify everyday objects from the earliest stages of contact. When combined with embedded artificial intelligence, this system provides fast, accurate, real-time recognition during manipulation. The advancements pave the way for more responsive, adaptive, and human-like robotic grasping in practical environments.
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.
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.
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