Sensors MDPI

04/09/2026

🏆 Most Influential Papers Published in 2001–2026 🏆

📄 𝗕𝗶𝗼𝗺𝗮𝗿𝗸𝗲𝗿𝘀 𝗶𝗻 𝗖𝗮𝗻𝗰𝗲𝗿 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻, 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗶𝘀, 𝗮𝗻𝗱 𝗣𝗿𝗼𝗴𝗻𝗼𝘀𝗶𝘀
👤 Sreyashi Das, Mohan Kumar Dey, Ram V. Devireddy, Manas Ranjan Gartia
🏛️ Louisiana State University

This review examines key biomarkers for cancer detection and management, spanning imaging techniques, circulating proteins and nucleic acids, circulating tumor cells, immunohistochemistry, and modern genetic profiling tools such as RNAseq and spatial transcriptomics. It highlights their roles in enabling earlier diagnosis, better prognosis assessment, and more tailored treatment strategies.

Read the full paper here: https://doi.org/10.3390/s24010037

Enjoyed the insights? Like, share with your network, or tag friends in cancer research and sensor technologies! 💙

04/09/2026

🏆 Most Influential Papers Published in 2001–2026 🏆

📄 Title: Wearable Sensors for Remote Health Monitoring

👤 Authors: Sumit Majumder, Tapas Mondal, M. Jamal Deen

🏛 McMaster University

This highly cited review paper, published in Sensors, provides a comprehensive overview of wearable sensor technologies for remote health monitoring. The authors systematically compare low-cost, non-invasive health and activity monitoring systems, survey textile-based flexible sensors for wearable applications, and discuss the compatibility of various communication technologies alongside future research challenges in the field. With over 1,200 citations, this paper remains a foundational reference for researchers working at the intersection of wearable sensors, IoT, and remote healthcare.

🔗 Read the full paper here: https://doi.org/10.3390/s17010130

👉 Love this? Give it a like, share it with your friends, and tag colleagues who are into wearable tech or remote healthcare!

04/09/2026

📢 Exciting Special Issue Open for Submissions!

𝘚𝘦𝘯𝘴𝘰𝘳𝘴 invites researchers to contribute to:

𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝘀 𝗶𝗻 𝗟𝗮𝘀𝗲𝗿 𝗦𝗲𝗻𝘀𝗼𝗿 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗵𝗲𝗶𝗿 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀

Guest Editor: 𝗗𝗿. 𝗩𝗶𝗼𝗹𝗲𝘁𝗮 𝗟𝗮𝘇𝗶𝗰 (ENEA - Agenzia nazionale, Italy)

From industrial process control and structural health monitoring to environmental analysis and novel applications — laser sensors are delivering non-contact, high-resolution solutions that are changing the way we measure and diagnose.

Topics of interest include industrial sensing, fault diagnostics, laser spectroscopy for environmental monitoring, system development, and emerging applications.

📅 Deadline: 𝟮 𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆 𝟮𝟬𝟮𝟳

Publish in a focused, high-impact collection with continuous online publication and strong visibility.

Learn more and submit:
https://www.mdpi.com/journal/sensors/special_issues/8R271Y8V08

Share with colleagues working on laser-based sensing — let’s advance the field together! 💡🔬

03/09/2026

📢 Meet our General Chair for AIS 2027: Prof. Chengkuo Lee

🌟 We are honored to welcome Prof. Chengkuo Lee (National University of Singapore) as the general chair at the 4th International Conference on AI Sensors and Transducers (AIS 2027).

Prof. Lee is the director of the Center for Intelligent Sensors and MEMS at the National University of Singapore, with remarkable contributions to research, innovation, and education.

🏆 Fellow of OPTICA
🔬 570+ journal publications | 10 US patents | 54,000+ Google Scholar citations
🎓 Trained 40+ Ph.D. students at NUS ECE
🌍 Clarivate Highly Cited Researcher (2023, 2024 & 2025)
🚀 Co-founder of Asia Pacific Microsystems (APM) and former VP of R&D

He also serves as Editor-in-Chief and Associate Editor-in-Chief for several leading journals, including AI Sensors, IEEE Transactions on Nanotechnology, Sensors International, and npj Self-powered Electronics.

Present Your Research to Leading Experts:
🎓 We warmly welcome academics, researchers, engineers, students and practitioners to submit abstracts, present posters or talks, and join us for academic exchange in Kunming, Yunnan, China!

📩 Abstract submission are now open.
- Submit here: https://sciforum.net/submissions/AIS2027/1
- Website: https://sciforum.net/event/AIS2027

03/09/2026

📣📣📣 Call for Reading
Interpolation and Imputation Strategies for Missing Segments in Continuous Pressure-Flow Cerebral Bio-Signals: A Systematic Scoping Review
👨‍🏫 Isuru Sachitha Herath, Frederick A. Zeiler; et al.
🏫 University of Manitoba, Karolinska Institutet, University of Cambridge, Pan Clinic Foundation
💥 Objective: Continuous pressure-flow cerebral bio-signals are critical for monitoring cerebrovascular dynamics but are often disrupted by missing data segments caused by artifacts from a variety of sources. These missing segments refer to segments of the signal that do not contain any valid physiological data. Such interruptions fragment the signals, resulting in discontinuities that compromise their overall integrity. Therefore, reconstructing missing values and preserving signal continuity are essential for ensuring the stable computation of signal trajectories and the accuracy of derived cerebrovascular indices. Methods: To address this issue, this systematic scoping review aimed to identify and synthesize existing interpolation and imputation strategies for handling missing segments in continuous pressure-flow cerebral bio-signals. Following the Cochrane and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a comprehensive search of five electronic databases was conducted from their inception to 24 September 2024, and updated on 16 June 2025, using a detailed search string. Results: The initial searches yielded 19,403 results, and 8 studies were filtered and included in the review. All included studies employed interpolation techniques, such as linear and spline interpolation algorithms, to correct distorted signal segments. However, none of the included studies directly utilized interpolation or imputation strategies to reconstruct or completely fill missing data segments. Conclusions: This reveals a critical knowledge gap, as no study has systematically addressed the utilization of interpolation or imputation strategies for missing segments in pressure-flow cerebral bio-signals. Therefore, this systematic review emphasizes the need for specialized methodologies and standardized frameworks to enable reliable recovery of missing data segments in pressure-flow cerebral bio-signals, which is critical for advancing real-time neurocritical care monitoring and experimental neuroscience/psychological research. Significance: This systematic review lays the groundwork for future research into physiologically informed interpolation and imputation strategies for pressure-flow cerebral bio-signals in clinical and research applications.
🔗 https://brnw.ch/21x5pcb

Objective: Continuous pressure-flow cerebral bio-signals are critical for monitoring cerebrovascular dynamics but are often disrupted by missing data segments caused by artifacts from a variety of sources. These missing segments refer to segments of the signal that do not contain any valid physiolog...

03/09/2026

📣📣📣 Call for Reading
Near-Infrared Spectroscopy Used During Cardiopulmonary Resuscitation: Instrumentation, Signal Metrics, Clinical Context, and Feasibility: A Scoping Review
👨‍🏫 Zahra Askari, Babak Shadgan; et al.
🏫 University of British Columbia
💥 Conventional cardiopulmonary resuscitation (CPR) is guided primarily by process metrics that do not directly quantify cerebral hemodynamics or perfusion. Near-infrared spectroscopy (NIRS) provides continuous, non-invasive monitoring of regional tissue oxygenation and has emerged as a candidate modality for physiologic feedback during low-flow states. However, CPR applications vary across devices and signal processing. This scoping review maps how NIRS has been implemented during conventional CPR in humans and porcine models, with emphasis on instrumentation characteristics, signal processing, acquisition bandwidth, artifact handling, physiologic associations, and feasibility constraints. From 1048 records, 39 studies met the inclusion criteria. Most used forehead-based cerebral rSO₂ monitoring (30/39). Rising cerebral oxygenation trajectories were consistently associated with return of spontaneous circulation (ROSC). In contrast, persistently low or non-increasing patterns were associated with non-ROSC, and absolute thresholds varied substantially across devices and studies. A minority of investigations derived compression-rate or waveform features from hemoglobin signals. Feasibility findings emphasized rapid probe placement without interrupting compressions but highlighted motion artifact, workflow constraints, and incomplete acquisition reporting. Overall, during conventional CPR, NIRS primarily serves as a dynamic monitor of oxygenation trends rather than a validated prognostic tool. Emerging waveform-based and hemodynamic analyses suggest the potential to evaluate CPR efficiency using perfusion-responsive optical features.
🔗 https://brnw.ch/21x5p8U

Conventional cardiopulmonary resuscitation (CPR) is guided primarily by process metrics that do not directly quantify cerebral hemodynamics or perfusion. Near-infrared spectroscopy (NIRS) provides continuous, non-invasive monitoring of regional tissue oxygenation and has emerged as a candidate modal...

03/09/2026

📣📣📣 Call for Reading
Quantum-Enhanced Edge Intelligence Leveraging Large Language Models for Immersive Space–Aerial–Ground Communications: Survey, Challenges, and Open Issues
👨‍🏫 Abhishek Gupta, Ajmery Sultana; et al.
🏫 Algoma University
💥 The integration of unmanned aerial vehicles (UAVs), autonomous vehicles, and advanced satellite systems in sixth-generation (6G) networks is poised to redefine next-generation communications as well as next-generation intelligent transportation systems. This paper examines the convergence of UAVs, CubeSats, and terrestrial infrastructures that comprise the framework of Space–Aerial–Ground Integrated Networks (SAGINs) as vital enablers of the International Mobile Telecommunications (IMT)-2030 standards. This paper examines the role of UAVs in providing flexible and quickly deployable airborne connectivity. It also discusses how CubeSats enhance global coverage through low-latency relaying and resilient backhaul links from low Earth orbit (LEO). Additionally, the paper highlights how terrestrial systems contribute high-capacity, densely concentrated communication layers that support various end-user applications. By examining their interoperability and coordinated resource allocation, the paper underscores that the seamless interaction of SAGIN nodes is essential for achieving the ultra-reliable, intelligent, and pervasive communication capabilities envisioned by IMT-2030. As 6G aims for ultra-low latency, high reliability, and massive connectivity, UAVs and CubeSats emerge as key enablers for extending coverage and capacity, particularly in remote and dense urban regions. Furthermore, the role of large language models (LLMs) is explored for intelligent network management and real-time data optimization, while quantum communication is analyzed for ensuring security and minimizing latency. The integration of LLMs into quantum-enhanced edge intelligence for SAGINs represents an emerging research frontier for adaptive, high-throughput, and context-aware decision-making. By exploiting quantum-assisted parallelism and entanglement-based optimization, LLMs enhance the processing efficiency of multimodal data across space, aerial, and terrestrial nodes. This paper further investigates distributed quantum inference and multimodal sensor data fusion to enable resilient, self-optimizing communication systems comprising a high volume of data traffic, which is a critical bottleneck in the global connectivity transition. LLMs are envisioned as cognitive control centers capable of generating semantic representations for mission-critical communications that enhance energy efficiency, reliability, and adaptive learning at the edge. The findings of the survey reveal that quantum-enhanced LLMs overcome challenges pertaining to bandwidth allocation, dynamic routing, and interoperability in existing classical communication systems. Overall, quantum-empowered LLMs significantly assist intelligent, autonomous, and immersive communications in SAGIN, while enabling secure, privacy-preserving communication.
🔗 https://brnw.ch/21x5p8S

The integration of unmanned aerial vehicles (UAVs), autonomous vehicles, and advanced satellite systems in sixth-generation (6G) networks is poised to redefine next-generation communications as well as next-generation intelligent transportation systems. This paper examines the convergence of UAVs, C...

03/09/2026

📣📣📣 Call for Reading
Transoral Robotic Cleft Palate Surgery: Communication-Related Outcomes and Feasibility
👨‍🏫 Tim Frederik Peter Ritzen, Rutger M. Schols; et al.
🏫 Maastricht University Medical Centre, Maastricht University, KidzHealthCastle Children's Hospital, Free University Brussels, Antwerp University Hospital, University of Antwerp
💥 Treatment of cleft lip alveolus and/or palate includes surgical repair to treat communication-related outcomes such as velopharyngeal insufficiency and otological dysfunction. Robot-assisted surgery has recently evolved into a promising adjunct to conventional surgery, particularly for complex procedures such as transoral (reconstructive) surgery. This structured literature review aims to investigate whether robot-assisted transoral cleft palate repair enhances communication (i.e., speech and otological) outcomes compared to conventional manual cleft palate surgery. A literature search was performed using PubMed, Embase, the Cochrane Library and Google Scholar. Primary outcomes were the change in cleft speech characteristics and otological disease after robot-assisted cleft palate surgery versus manual cleft palate surgery. The available evidence on communication-related outcomes remains sparse. Six relevant articles were included. In only one study, transoral robotic cleft surgery (TORCS) significantly reduced otitis media with effusion (OME), need for ventilation tubes and hearing threshold within 2 years post-surgery. No postoperative speech or velopharyngeal outcomes were reported. In conclusion, transoral robotic cleft surgery (TORCS) appears safe and feasible for repair of a cleft palate. It provides superior intraoral view and improved surgeon ergonomics. Current drawbacks are the costs and the available tools, the extended surgical duration and the lack of haptic feedback, which limit the current clinical applicability of TORCS. Based on limited clinical evidence, TORCS may support faster recovery of Eustachian tube function and hearing, but no conclusions on speech outcomes can yet be drawn.
🔗 https://brnw.ch/21x5p7x

Treatment of cleft lip alveolus and/or palate includes surgical repair to treat communication-related outcomes such as velopharyngeal insufficiency and otological dysfunction.

03/09/2026

📣📣📣 Call for Reading
Non-Standardized Methods of Assessing Tibial Loads During Different Gait Speeds Obscures Load-Management Recommendations in Healthy Adults
👨‍🏫 Jack D. Hart, Elizabeth J. Bradshaw; et al.
🏫 Deakin University, AUT - Auckland University of Technology
💥 This scoping review aimed to investigate (i) the methods used to measure tibial load during human gait, and (ii) the effect of gait mode and velocity on tibial load in healthy adults. This review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). Two databases (EMBASE, MEDLINE) were searched, revealing twelve studies that met the inclusion criteria from the previous 24 years. Tibia load was measured using indirect (in vivo strain gauges) and direct (accelerometers, inertial measurement units [IMUs]) sensor methods, and three-dimensional motion analysis systems. A range of surfaces were used, including treadmill (motorized, curved non-motorized) and overground conditions. Velocity was a key determinant of tibia load, with surface type and gait modifications further influencing the loads. The non-standardized measurement methods resulted in varied tibial load results, particularly from varied anatomical positions used in indirect sensor methods. The findings suggest that, based on the current literature, prescribing load management recommendations for healthy adults is not currently possible, given the variability in results. Future research should aim to develop standardized measurement protocols to improve injury risk reduction strategies and to inform rehabilitation programs to support individuals in resuming participation in gait-related activities.
🔗 https://brnw.ch/21x5p6H

This scoping review aimed to investigate (i) the methods used to measure tibial load during human gait, and (ii) the effect of gait mode and velocity on tibial load in healthy adults. This review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extens...

03/09/2026

📣📣📣 Call for Reading
Time-Aware and Power-Law Retention Gating Mechanisms in LSTMs for Irregularly Sampled Sensor Data: A Survey
👨‍🏫 Diba Das, Abbas Z. Kouzani; et al.
🏫 Deakin University
💥 Sensor data streams from Internet of Things devices, wearables, and monitoring systems are often irregularly sampled due to variable transmission intervals, intermittent connectivity, and event-driven reporting, producing heavy-tailed temporal gaps between observations. Long Short-Term Memory (LSTM) networks, widely used for sequential sensor data analysis, assume a synchronous, equidistant timeline and regulate memory through a forget gate whose dynamics are input- and state-dependent but carry no explicit dependence on elapsed real time. Under stationary inputs, the standard LSTM architecture additionally exhibits a structural bias toward exponential memory decay. However, in real-world sensing contexts, uneven sampling and long-range dependencies are common, violating both assumptions simultaneously. To address this, we survey and taxonomise two independently proposed strategies: time-aware mechanisms that rescale memory using elapsed time, and power-law retention schemes that model slower, heavy-tailed forgetting. Through comparative analysis, we demonstrate that both strategies converge on the same underlying failure point, the inability of the forget gate to jointly adapt its attenuation rate and decay profile to temporal context and sensor input, and frequently employ functionally redundant mechanisms. We subsequently articulate a unified design framework that defines the essential properties that temporal forget gates must satisfy to enable robust learning from irregular sensor streams. These findings indicate that future sequence models for sensing applications should jointly integrate temporal-gap awareness with adaptive retention dynamics to better capture complex temporal dependencies in real-world environments.
🔗 https://brnw.ch/21x5p6v

Sensor data streams from Internet of Things devices, wearables, and monitoring systems are often irregularly sampled due to variable transmission intervals, intermittent connectivity, and event-driven reporting, producing heavy-tailed temporal gaps between observations. Long Short-Term Memory (LSTM)...