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Development and In-Field Validation of an Autonomous Soil Mechanical Resistance Sensor
Fluorescence Detection of Dopamine Using o-Phthaldialdehyde and 3-Mercaptopropyltriethoxysilane
Microfluidic Biosensors: Enabling Advanced Disease Detection
Enhanced Readout System for Timepix3-Based Detectors in Large-Scale Scientific Facilities
Respiratory State Estimation in Public-Oriented Powered Air-Purifying Respirators

Journal Description

Sensors

Sensors is an international, peer-reviewed, open access journal on the science and technology of sensors. Sensors is published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE), Japan Society of Photogrammetry and Remote Sensing (JSPRS), Spanish Society of Biomedical Engineering (SEIB), International Society for the Measurement of Physical Behaviour (ISMPB) and Chinese Society of Micro-Nano Technology (CSMNT) and more are affiliated with Sensors and their members receive a discount on the article processing charges.

Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Ei Compendex, Inspec, Astrophysics Data System, and other databases.
Journal Rank: JCR - Q2 (Instruments and Instrumentation) / CiteScore - Q1 (Instrumentation)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.7 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our editors and authors say about Sensors.
Companion journals for Sensors include: Chips, Targets and AI Sensors.
Journal Cluster of Instruments and Instrumentation: Actuators, AI Sensors, Instruments, Micromachines and Sensors.

Impact Factor: 3.5 (2024); 5-Year Impact Factor: 3.7 (2024)

Imprint Information Journal Flyer Open Access ISSN: 1424-8220

Latest Articles

20 pages, 23077 KB

Open AccessArticle

An Integrated Experimental System for Unmanned Underwater Vehicle Swarm Control

by Yutao Chen, Xingwei Zhou, Wenshan Hu and Bo Zhao

Sensors 2025, 25(20), 6413; https://doi.org/10.3390/s25206413 (registering DOI) - 17 Oct 2025

Unmanned Underwater Vehicle (UUV) swarms have become increasingly crucial for underwater exploration and applications, where their coordinated operation offers significant advantages over single-vehicle systems. However, unlike single-vehicle systems, the development of swarm control systems is more complicated, especially because there are limited integrated [...] Read more.

Unmanned Underwater Vehicle (UUV) swarms have become increasingly crucial for underwater exploration and applications, where their coordinated operation offers significant advantages over single-vehicle systems. However, unlike single-vehicle systems, the development of swarm control systems is more complicated, especially because there are limited integrated toolchains that can cover both global scheme design and individual vehicle implementation. Engineers may have to develop a global scheme and then partition it manually for individual vehicle implementation, which can result in substantial efficiency losses. To address this difficulty, an integrated experimental framework is developed to support the complete workflow of UUV swarm control development, from unified algorithm design and system simulation to automated code generation and individual deployment. The architecture of the proposed platform incorporates three principal elements: a global simulation environment that enables virtual validation of swarm collective behavior, a rapid prototyping module that facilitates code generation/partitioning and individual implementation, and a digital twin visualization component that provides real-time monitoring capabilities. A case study demonstrates that the platform can integrate global design with individual implementation. In a comparative experiment where the same engineering team implemented a three-UUV formation control algorithm, the use of our platform reduced the time from algorithm design to successful deployment from an estimated 6 h (using manual coding and integration) to under one hour, representing about an 80% reduction in development time. Full article

(This article belongs to the Section Communications)

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26 pages, 7039 KB

Open AccessArticle

Light Readout of Small Scintillators Using SiPM Photosensors

by Chiara Rita Failla, Simone Amaducci, Gaetano Elio Poma and Paolo Finocchiaro

Sensors 2025, 25(20), 6412; https://doi.org/10.3390/s25206412 (registering DOI) - 17 Oct 2025

During the last two decades, relevant progress has been achieved in silicon photomultiplier (SiPM) technology, such that in an increasing number of radiation detection applications they are proposed as a viable alternative to traditional photomultiplier tubes (PMTs). Applications where the light from tiny [...] Read more.

During the last two decades, relevant progress has been achieved in silicon photomultiplier (SiPM) technology, such that in an increasing number of radiation detection applications they are proposed as a viable alternative to traditional photomultiplier tubes (PMTs). Applications where the light from tiny scintillating crystals is detected by a single SiPM raise the question of the possible non-linearity of the response due to the saturation of the number of microcells involved. In other cases, where larger scintillators subtend arrays of SiPMs, the same question could hold. This work tries to disentangle such a question with a realistic numerical approach and a few tests showing that the possible saturation effects depend on the interplay between the features of the scintillator and of the SiPM (array). The quantitative results of this analysis can likely be used to better plan future radiation detection systems and to highlight their linearity boundaries. Full article

(This article belongs to the Special Issue SPAD-Based Sensors and Techniques for Enhanced Sensing Applications)

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19 pages, 1311 KB

Open AccessArticle

An Interpretable Soft-Sensor Framework for Dissertation Peer Review Using BERT

by Meng Wang, Jincheng Su, Zhide Chen, Wencheng Yang and Xu Yang

Sensors 2025, 25(20), 6411; https://doi.org/10.3390/s25206411 (registering DOI) - 17 Oct 2025

Graduate education has entered the era of big data, and systematic analysis of dissertation evaluations has become crucial for quality monitoring. However, the complexity and subjectivity inherent in peer-review texts pose significant challenges for automated analysis. While natural language processing (NLP) offers potential [...] Read more.

Graduate education has entered the era of big data, and systematic analysis of dissertation evaluations has become crucial for quality monitoring. However, the complexity and subjectivity inherent in peer-review texts pose significant challenges for automated analysis. While natural language processing (NLP) offers potential solutions, most existing methods fail to adequately capture nuanced disciplinary criteria or provide interpretable inferences for educators. Inspired by soft-sensor, this study employs a BERT-based model enhanced with additional attention mechanisms to quantify latent evaluation dimensions from dissertation reviews. The framework integrates Shapley Additive exPlanations (SHAP) to ensure the interpretability of model predictions, combining deep semantic modeling with SHAP to quantify characteristic importance in academic evaluation. The experimental results demonstrate that the implemented model outperforms baseline methods in accuracy, precision, recall, and F1-score. Furthermore, its interpretability mechanism reveals key evaluation dimensions experts prioritize during the paper assessment. This analytical framework establishes an interpretable soft-sensor paradigm that bridges NLP with substantive review principles, providing actionable insights for enhancing dissertation improvement strategies. Full article

(This article belongs to the Special Issue Integrating AI and IoT with Sensors in Computer-Based Educational Systems)

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24 pages, 9314 KB

Open AccessArticle

An Intelligent Joint Identification Method and Calculation of Joint Attitudes in Underground Mines Based on Smartphone Image Acquisition

by Guang Li, Jinyao Zhu, Changyu Jin, Xinyang Mao and Qiang Wang

Sensors 2025, 25(20), 6410; https://doi.org/10.3390/s25206410 (registering DOI) - 17 Oct 2025

Acquisition of joint attitudes is vital in mine geology but often constrained by underground conditions, while manual cataloging remains inefficient and subjective. To overcome these issues, we propose a mobile phone photography and deep learning-based method. Rock joint images are collected with smartphones, [...] Read more.

Acquisition of joint attitudes is vital in mine geology but often constrained by underground conditions, while manual cataloging remains inefficient and subjective. To overcome these issues, we propose a mobile phone photography and deep learning-based method. Rock joint images are collected with smartphones, augmented by cutting and rotation, and enhanced using CLAHE. After labeling with Labelme, a dataset is built for training. A ResNet residual module and CBAM attention are integrated into a U-Net architecture, forming the RC-Unet model for accurate semantic segmentation of joints. Post-processing with OpenCV enables contour extraction, and the PCP three-point localization algorithm rapidly calculates joint attitudes. A practical engineering case verifies that intelligent joint identification can replace manual cataloging in relatively simple underground environments. This approach improves efficiency, reduces subjectivity, and provides a rapid, low-cost, and easily storable means for geological information acquisition, highlighting its potential as an effective tool and supplementary method for mine surveys. Full article

(This article belongs to the Section Physical Sensors)

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11 pages, 5142 KB

Open AccessArticle

Enhancing the Output Performance of Fiber-TENG Through Graphite Doping and Its Application in Human Motion Sensing

by You-Jun Huang, Jen-I Chuang and Chen-Kuei Chung

Sensors 2025, 25(20), 6409; https://doi.org/10.3390/s25206409 (registering DOI) - 17 Oct 2025

Triboelectric nanogenerators (TENG) are mechanical energy harvesters characterized by high sensitivity and simple structure and are currently being widely developed for use in human body motion sensing. Among them, fiber-based TENGs (FTENG) are particularly suitable for wearable human motion sensors due to their [...] Read more.

Triboelectric nanogenerators (TENG) are mechanical energy harvesters characterized by high sensitivity and simple structure and are currently being widely developed for use in human body motion sensing. Among them, fiber-based TENGs (FTENG) are particularly suitable for wearable human motion sensors due to their unique structure, which offers flexibility, high durability, and comfort. However, studies involving doping to further modify the electrical output characteristics of FTENGs are very limited. Here, we propose an innovative approach that combines graphite (GP) doping with fiber-based TENG fabrication, successfully developing a graphite-doped polyester fiber-based TENG (GP@PET-TENG). Proper graphite doping can increase the amount of transferred charge and thus improve the output electrical performance of TENG, but this method has rarely been explored in FTENG. With the incorporation of 3%wt graphite, the open-circuit voltage of the GP@PET-TENG increased from 103.3 V to 202.1 V, and the short-circuit current increased from 60.7 μA to 105.1 μA, compared to the pure polyester fiber based TENG (PET-TENG). The device achieved a maximum output power of 4.15 mW (2.59 W/m²), demonstrates the capability to charge various capacitors, and successfully lit up 200 LEDs. By attaching the GP@PET tribo-layer to human skin, a single-electrode mode TENG can be formed, which captures the subject’s motion signals through skin contact and separation, converting them into voltage outputs. In fist-clenching and wrist-bending tests, motion-induced voltage signals up to 0.6 V were recorded, demonstrating the potential applications in rehabilitation assistance and mechanical control. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Advanced Integrated Sensing Enabling Smart Structures)

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25 pages, 2961 KB

Open AccessArticle

Ultrasound and Unsupervised Segmentation-Based Gesture Recognition for Smart Device Unlocking

by Xiaojuan Wang and Mengqiao Li

Sensors 2025, 25(20), 6408; https://doi.org/10.3390/s25206408 (registering DOI) - 17 Oct 2025

A smart device unlocking scheme based on ultrasonic gesture recognition is proposed, allowing users to unlock their devices by customizing the unlock code through gesture movements. This method utilizes ultrasound to detect multiple consecutive gestures, identifying micro-features within these gestures for authentication. To [...] Read more.

A smart device unlocking scheme based on ultrasonic gesture recognition is proposed, allowing users to unlock their devices by customizing the unlock code through gesture movements. This method utilizes ultrasound to detect multiple consecutive gestures, identifying micro-features within these gestures for authentication. To enhance recognition accuracy, an unsupervised segmentation algorithm is employed to accurately segment the gesture feature region and extract the time-frequency domain data of the gestures. Additionally, two-stage data enhancement techniques are applied to generate user-specific data based on a small sample size. Finally, the user-specific model is deployed to mobile devices via transfer learning for on-device, real-time inference. Experimental validation on a commercial smartphone (Redmi K50) demonstrates that the entire authentication pipeline, from signal acquisition to decision, processes 8 types of gestures in a sequence in sequence in approximately 1.2 s, with the core model inference taking less than 50 milliseconds. This ensures that the raw biometric data (ultrasonic echoes) and the recognition results never leave the user’s device during authentication, thereby safeguarding privacy. It is important to note that while model training is performed offline on a server to leverage greater computational resources for personalization, the deployed system operates fully in real time on the edge device. Experimental results demonstrate that our system achieves accurate and robust identity verification, with an average five-fold cross-validation accuracy rate of up to

93.56 %

, and it shows robustness across different environments. Full article

(This article belongs to the Section Intelligent Sensors)

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29 pages, 6302 KB

Open AccessArticle

Measurement of Strain and Vibration, at Ambient Conditions, on a Dynamically Pressurised Aircraft Fuel Pump Using Optical Fibre Sensors

by Edmond Chehura, Stephen W. James, Jarryd Braithwaite, James H. Barrington, Stephen Staines, Andrew Keil, Martin Yates, Nicholas John Lawson and Ralph P. Tatam

Sensors 2025, 25(20), 6407; https://doi.org/10.3390/s25206407 (registering DOI) - 17 Oct 2025

Ever-increasing demands to improve fuel burn efficiency of aero gas turbines lead to rises in fuel system pressures and temperatures, posing challenges for the structural integrity of the pump housing and creating internal deflections that can adversely affect volumetric efficiency. Non-invasive strain and [...] Read more.

Ever-increasing demands to improve fuel burn efficiency of aero gas turbines lead to rises in fuel system pressures and temperatures, posing challenges for the structural integrity of the pump housing and creating internal deflections that can adversely affect volumetric efficiency. Non-invasive strain and vibration measurements could allow transient effects to be quantified and considered during the design process, leading to more robust fuel pumps. Fuel pumps used on a high bypass turbofan engine were instrumented with optical fibre Bragg grating (FBG) sensors, strain gauges and thermocouples. A hydraulic hand pump was used to facilitate measurements under static conditions, while dynamic measurements were performed on a dedicated fuel pump test rig. The experimental data were compared with the outputs from a finite element (FE) model and, in general, good agreement was observed. Where differences were observed, it was concluded that they arose from the sensitivity of the model to the selection of nodes that best matched the sensor location. Strain and vibration measurements were performed over the frequency range of 0 to 2.5 kHz and demonstrated the ability of surface-mounted FBGs to characterise vibrations originating within the internal sub-components of the pump, offering potential for condition monitoring. Full article

(This article belongs to the Special Issue Feature Papers in Optical Sensors 2025)

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21 pages, 13386 KB

Open AccessArticle

Enhanced Gas Sensitivity Characteristics of NO₂ Sensor Based on a Silicon Micropillar Design Strategy at Room Temperature

by Zhiyuan Zhang, An Ning, Jian-Jun Zhu, Yi-Yu Yue, Zhi-Qiang Fan and Sai Chen

Sensors 2025, 25(20), 6406; https://doi.org/10.3390/s25206406 (registering DOI) - 17 Oct 2025

In this study, two types of gas sensors—silicone-based interdigital electrode and silicon micropillar sensors based on rGO and rGO/SnO₂—were fabricated. Their gas-sensing performance was investigated at room temperature. First, interdigital electrodes of different channel widths were fabricated to investigate the impact [...] Read more.

In this study, two types of gas sensors—silicone-based interdigital electrode and silicon micropillar sensors based on rGO and rGO/SnO₂—were fabricated. Their gas-sensing performance was investigated at room temperature. First, interdigital electrodes of different channel widths were fabricated to investigate the impact of the channel width parameter. Subsequently, the rGO/SnO₂ doping ratio in the composite material was varied to identify the optimal composition for gas sensitivity. Additionally, triangular and square-arrayed silicon micropillar substrates were fabricated via photolithography and inductively coupled plasma etching. The rGO/SnO₂-based gas sensor on a silicon micropillar substrate exhibited an ultra-high specific surface area. The triangular micropillar arrangement of rGO/SnO₂-160 demonstrates the best performance, showing approximately 14% higher response and a 106 s reduction in response time compared with interdigital electrode sensors spray-coated with the same concentration of rGO/SnO₂ when tested at room temperature under 250 ppm NO₂. The optimized sensor achieves a detection limit as low as 5 ppm and maintains high responsiveness, even in conditions of 60% relative humidity (RH). Additionally, the repeatability, selectivity, and stability of the sensor were evaluated. Finally, structural and morphological characterization was conducted using XRD, SEM, TEM, and Raman spectroscopy, which confirmed the successful modification of rGO with SnO₂. Full article

(This article belongs to the Special Issue Recent Advances in Gas Sensors)

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16 pages, 2231 KB

Open AccessArticle

Robotic Ankle Assessment Post-Stroke: Reliability, Comparison to Therapists, and Benchmark Dataset Development

by Christopher A. Johnson, Andria J. Farrens, Piyashi Biswas, Luis Garcia-Fernandez, Jill See, Lucy Dodakian, Vicky Chan, Po T. Wang, Steven C. Cramer, Zoran Nenadic, An H. Do and David J. Reinkensmeyer

Sensors 2025, 25(20), 6405; https://doi.org/10.3390/s25206405 (registering DOI) - 17 Oct 2025

In rehabilitation research, coarse clinical outcome measures may limit the sensitivity of trials by failing to detect meaningful intervention-induced changes. Sensorized robotic platforms can potentially improve the evaluation of motor function, yet their reliability compared to skilled therapists remains unclear. This study utilized [...] Read more.

In rehabilitation research, coarse clinical outcome measures may limit the sensitivity of trials by failing to detect meaningful intervention-induced changes. Sensorized robotic platforms can potentially improve the evaluation of motor function, yet their reliability compared to skilled therapists remains unclear. This study utilized a robotic device to measure two fundamental impairments that are critical to ankle function: range of motion (ROM, active and passive) and dorsiflexion maximum voluntary contraction (MVC). In 34 chronic hemiparetic post-stroke individuals, we assessed test–retest reliability over two days for the robot and experienced therapists, who used a goniometer and manual muscle testing (MMT). We also evaluated robotic test–retest reliability in 36 young and 26 older unimpaired adults. Reliability for robotic and therapist-based AROM and MVC measures was high (ICC > 0.86), consistent across all groups. Robotic AROM and PROM measurements correlated strongly with therapist assessments (r > 0.60, p < 0.001) but were 120% and 37% larger than therapist assessments (p < 0.001), respectively. For the MVC measurement, the therapist assigned 85% of participants a score of 1 on the MMT, but their MVC torque was distributed from 0 to ~20 Nm. Measurement differences between methods likely arose from the robot’s constrained setup, allowing for compensatory muscle activation. The increased granularity provided by robotic MVC measurements could enable more precise tracking of motor recovery and facilitate tailored rehabilitation strategies. These results support the clinical utility of robotic platforms for ankle assessment, offering detailed, objective measurements that can augment traditional evaluations. Full article

(This article belongs to the Section Sensors and Robotics)

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18 pages, 3754 KB

Open AccessArticle

Hardware Implementation of Improved Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping Version 2

by Ji-Long He, Ying-Hua Chen, Wenny Ramadha Putri, Chung-I. Huang, Ming-Hsiang Su, Kuo-Chen Li, Jian-Hong Wang, Shih-Lun Chen, Yung-Hui Li and Jia-Ching Wang

Sensors 2025, 25(20), 6404; https://doi.org/10.3390/s25206404 (registering DOI) - 17 Oct 2025

The field of autonomous driving has seen continuous advances, yet achieving higher levels of automation in real-world applications remains challenging. A critical requirement for autonomous navigation is accurate map construction, particularly in novel and unstructured environments. In recent years, Simultaneous Localization and Mapping [...] Read more.

The field of autonomous driving has seen continuous advances, yet achieving higher levels of automation in real-world applications remains challenging. A critical requirement for autonomous navigation is accurate map construction, particularly in novel and unstructured environments. In recent years, Simultaneous Localization and Mapping (SLAM) has evolved to support diverse sensor modalities, with some implementations incorporating machine learning to improve performance. However, these approaches often demand substantial computational resources. The key challenge lies in achieving efficiency within resource-constrained environments while minimizing errors that could degrade downstream tasks. This paper presents an enhanced ORB-SLAM2 (Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping, version 2) algorithm implemented on a Raspberry Pi 3 (ARM A53 CPU) to improve mapping performance under limited computational resources. ORB-SLAM2 comprises four main stages: Tracking, Local Mapping, Loop Closing, and Full Bundle Adjustment (BA). The proposed improvements include employing a more efficient feature descriptor to increase stereo feature-matching rates and optimizing loop-closing parameters to reduce accumulated errors. Experimental results demonstrate that the proposed system achieves notable improvements on the Raspberry Pi 3 platform. For monocular SLAM, RMSE is reduced by 18.11%, mean error by 22.97%, median error by 29.41%, and maximum error by 17.18%. For stereo SLAM, RMSE decreases by 0.30% and mean error by 0.38%. Furthermore, the ROS topic frequency stabilizes at 10 Hz, with quad-core CPU utilization averaging approximately 90%. These results indicate that the system satisfies real-time requirements while maintaining a balanced trade-off between accuracy and computational efficiency under resource constraints. Full article

(This article belongs to the Section Intelligent Sensors)

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17 pages, 3288 KB

Open AccessArticle

Autonomous Vision-Based Object Detection and Tracking System for Quadrotor Unmanned Aerial Vehicles

by Oumaima Gharsa, Mostefa Mohamed Touba, Mohamed Boumehraz and Nacira Agram

Sensors 2025, 25(20), 6403; https://doi.org/10.3390/s25206403 (registering DOI) - 16 Oct 2025

This paper introduces an autonomous vision-based tracking system for a quadrotor unmanned aerial vehicle (UAV) equipped with an onboard camera, designed to track a maneuvering target without external localization sensors or GPS. Accurate capture of dynamic aerial targets is essential to ensure real-time [...] Read more.

This paper introduces an autonomous vision-based tracking system for a quadrotor unmanned aerial vehicle (UAV) equipped with an onboard camera, designed to track a maneuvering target without external localization sensors or GPS. Accurate capture of dynamic aerial targets is essential to ensure real-time tracking and effective management. The system employs a robust and computationally efficient visual tracking method that combines HSV filter detection with a shape detection algorithm. Target states are estimated using an enhanced extended Kalman filter (EKF), providing precise state predictions. Furthermore, a closed-loop Proportional-Integral-Derivative (PID) controller, based on the estimated states, is implemented to enable the UAV to autonomously follow the moving target. Extensive simulation and experimental results validate the system’s ability to efficiently and reliably track a dynamic target, demonstrating robustness against noise, light reflections, or illumination interference, and ensure stable and rapid tracking using low-cost components. Full article

(This article belongs to the Section Sensors and Robotics)

32 pages, 12557 KB

Open AccessArticle

Controlling an Industrial Robot Using Stereo 3D Vision Systems with AI Elements

by Jarosław Panasiuk

Sensors 2025, 25(20), 6402; https://doi.org/10.3390/s25206402 (registering DOI) - 16 Oct 2025

Robotization of production processes and the use of 3D vision systems are currently becoming more and more popular. It allows for more flexibility in the robotic process as well as expands the possibilities of process control, depending on changes in the parameters of [...] Read more.

Robotization of production processes and the use of 3D vision systems are currently becoming more and more popular. It allows for more flexibility in the robotic process as well as expands the possibilities of process control, depending on changes in the parameters of the object, its pose, and changes in the process itself. Unfortunately, the use of standard solutions is limited to a relatively small space in which the robot’s vision system operates. The use of the latest solutions in the field of Artificial Intelligence (AI) and external vision systems, in combination with the closed structures of industrial robot control systems, provides advantages by enhancing the digital awareness of the environment of robotic systems. This article presents an example of solving the problem of low digital awareness of the environment of robotic systems resulting from the limited field of view of vision systems used in industrial robots, while maintaining high precision of the systems consisting of the combination of a 3D vision system using a stereovision camera and software with AI elements with the control system of an industrial robot from FANUC and an integrated Robot Vision (iRVision) system to maintain the positioning accuracy of the robot tool. Full article

(This article belongs to the Special Issue Advanced Techniques in Control and Path Planning for Autonomous and Collaborative Robots in Dynamic Environments)

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19 pages, 4118 KB

Open AccessArticle

Evaluating Reticulorumen Temperature, Rumination, Activity and pH Measured by Rumen Sensors as Indicators of Heat Load in Fattening Bulls

by Kay Fromm, Christian Ammon, Thomas Amon and Gundula Hoffmann

Sensors 2025, 25(20), 6401; https://doi.org/10.3390/s25206401 (registering DOI) - 16 Oct 2025

The aim of this experiment was to determine whether reticulorumen temperature (ReT), rumination, activity or pH captured by a rumen sensor bolus system (smaXtec animal care GmbH, Graz, Austria) can be used as an early indicator of heat load (HL) and to assess [...] Read more.

The aim of this experiment was to determine whether reticulorumen temperature (ReT), rumination, activity or pH captured by a rumen sensor bolus system (smaXtec animal care GmbH, Graz, Austria) can be used as an early indicator of heat load (HL) and to assess how its daily patterns are influenced by diurnal effects. Physiological and behavioral data from 70 male feedlot cattle (Uckermärker, Hereford, Simmentaler) housed in a closed barn were investigated using the calculated temperature-humidity index (THI) from remote HOBO Onset climate sensors over a period of 210 days. Using time series analysis and seasonal ARIMA modeling, it was found that ReT followed the same patterns throughout days with a THI < 74 as well as days under heat load conditions. Time series and correlation analyses were also performed for the rumen pH, rumination index and activity index. The collective mean ReT over the winter days assessed (n = 14,971) was 39.48 °C, with a minimum mean of 38.31 °C and a maximum mean of 40.69 °C. In comparison, the collective mean ReT over the summer days assessed (n = 14,030) was 39.53 °C, with a minimum mean of 38.39 °C and a maximum mean of 42.02 °C. Pearson’s correlation did not reveal a relationship between THI and ReT (r = −0.06; p < 0.001) and only minimally for rumination (r = −0.11; p < 0.001). Rumination clearly decreased with increasing ambient temperature in comparison to days with a THI < 74. A long-term effect is also visible when the monthly mean rumination from all bulls tends to decrease slightly from February to May and then increases beginning in June. The mean pH values decreased throughout the summer months. Nevertheless, the comparison between daily fluctuations in pH values under HL failed to yield significant deviations from those captured on days of winter. The Pearson correlation for rumen pH showed a weak negative linear relationship with THI (r = −0.3; p < 0.001). The monthly means of the motion activity index could also not verify that HL led to increasing activity (Pearson correlation for motion activity and THI: r = 0.04; p < 0.001). The heat load had no visible short-term effects on the ReT or rumen pH, but rumination and peak motion activity were reduced on days with high ambient temperatures. Full article

(This article belongs to the Section Biomedical Sensors)

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17 pages, 2475 KB

Open AccessArticle

YOLO-LMTB: A Lightweight Detection Model for Multi-Scale Tea Buds in Agriculture

by Guofeng Xia, Yanchuan Guo, Qihang Wei, Yiwen Cen, Loujing Feng and Yang Yu

Sensors 2025, 25(20), 6400; https://doi.org/10.3390/s25206400 (registering DOI) - 16 Oct 2025

Tea bud targets are typically located in complex environments characterized by multi-scale variations, high density, and strong color resemblance to the background, which pose significant challenges for rapid and accurate detection. To address these issues, this study presents YOLO-LMTB, a lightweight multi-scale detection [...] Read more.

Tea bud targets are typically located in complex environments characterized by multi-scale variations, high density, and strong color resemblance to the background, which pose significant challenges for rapid and accurate detection. To address these issues, this study presents YOLO-LMTB, a lightweight multi-scale detection model based on the YOLOv11n architecture. First, a Multi-scale Edge-Refinement Context Aggregator (MERCA) module is proposed to replace the original C3k2 block in the backbone. MERCA captures multi-scale contextual features through hierarchical receptive field collaboration and refines edge details, thereby significantly improving the perception of fine structures in tea buds. Furthermore, a Dynamic Hyperbolic Token Statistics Transformer (DHTST) module is developed to replace the original PSA block. This module dynamically adjusts feature responses and statistical measures through attention weighting using learnable threshold parameters, effectively enhancing discriminative features while suppressing background interference. Additionally, a Bidirectional Feature Pyramid Network (BiFPN) is introduced to replace the original network structure, enabling the adaptive fusion of semantically rich and spatially precise features via bidirectional cross-scale connections while reducing computational complexity. In the self-built tea bud dataset, experimental results demonstrate that compared to the original model, the YO-LO-LMTB model achieves a 2.9% improvement in precision (P), along with increases of 1.6% and 2.0% in mAP50 and mAP50-95, respectively. Simultaneously, the number of parameters decreased by 28.3%, and the model size reduced by 22.6%. To further validate the effectiveness of the improvement scheme, experiments were also conducted using public datasets. The results demonstrate that each enhancement module can boost the model’s detection performance and exhibits strong generalization capabilities. The model not only excels in multi-scale tea bud detection but also offers a valuable reference for reducing computational complexity, thereby providing a technical foundation for the practical application of intelligent tea-picking systems. Full article

(This article belongs to the Section Smart Agriculture)

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33 pages, 5047 KB

Open AccessReview

Review of Advances in Fire Extinguishing Based on Computer Vision Applications: Methods, Challenges, and Future Directions

by Valentyna Loboichenko, Grzegorz Wilk-Jakubowski, Lukasz Pawlik, Jacek Lukasz Wilk-Jakubowski, Roman Shevchenko, Olha Shevchenko, Radoslaw Harabin, Artur Kuchcinski, Valentyna Fedorchuk-Moroz, Anastasiia Khmyrova and Ivan Rushchak

Sensors 2025, 25(20), 6399; https://doi.org/10.3390/s25206399 (registering DOI) - 16 Oct 2025

This paper examines the state-of-the-art in fire suppression technologies based on computer vision applications in the subject areas of computer science and engineering. The study involves a two-stage analysis of publications using keywords. This paper presents a bibliographic analysis of scientific literature from [...] Read more.

This paper examines the state-of-the-art in fire suppression technologies based on computer vision applications in the subject areas of computer science and engineering. The study involves a two-stage analysis of publications using keywords. This paper presents a bibliographic analysis of scientific literature from the Scopus database using VOSviewer software and the author’s methodological approach. General keywords were used for the initial analysis of the dataset, followed by a more detailed study with additional criteria and specific keywords. The categories considered in the article are as follows: Firefighting Robots, Fire Detection, Fire Suppression, Aerial Vehicles, and Computer Vision. It is shown that the research includes technical aspects of fire robots and systems, as well as the improvement of their software and hardware. The subsequent review highlights the important role of computer vision in improving the efficiency and effectiveness of fire suppression systems. It is noted that key advances include the development of sophisticated fire detection algorithms and the implementation of automated fire suppression systems. The study also discusses the challenges and future directions in this field, emphasizing the need for continuous innovation and interdisciplinary collaboration. This review provides valuable information for researchers, engineers, and practitioners in the field of fire safety by offering a comprehensive overview of state-of-the-art technologies and their applications in fire suppression. Full article

(This article belongs to the Section Sensing and Imaging)

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32 pages, 25136 KB

Open AccessArticle

Efficiency Evaluation of Sampling Density for Indoor Building LiDAR Point-Cloud Segmentation

by Yiquan Zou, Wenxuan Chen, Tianxiang Liang and Biao Xiong

Sensors 2025, 25(20), 6398; https://doi.org/10.3390/s25206398 (registering DOI) - 16 Oct 2025

Prior studies on indoor LiDAR point-cloud semantic segmentation consistently report that sampling density strongly affects segmentation accuracy as well as runtime and memory, establishing an accuracy–efficiency trade-off. Nevertheless, in practice, the density is often chosen heuristically and reported under heterogeneous protocols, which limits [...] Read more.

Prior studies on indoor LiDAR point-cloud semantic segmentation consistently report that sampling density strongly affects segmentation accuracy as well as runtime and memory, establishing an accuracy–efficiency trade-off. Nevertheless, in practice, the density is often chosen heuristically and reported under heterogeneous protocols, which limits quantitative guidance. We present a unified evaluation framework that treats density as the sole independent variable. To control architectural variability, three representative backbones—PointNet, PointNet++, and DGCNN—are each augmented with an identical Point Transformer module, yielding PointNet-Trans, PointNet++-Trans, and DGCNN-Trans trained and tested under one standardized protocol. The framework couples isotropic voxel-guided uniform down-sampling with a decision rule integrating three signals: (i) accuracy sufficiency, (ii) the onset of diminishing efficiency, and (iii) the knee of the accuracy–density curve. Experiments on scan-derived indoor point clouds (with BIM-derived counterparts for contrast) quantify the accuracy–runtime trade-off and identify an engineering-feasible operating band of 1600–2900 points/m², with a robust setting near 2400 points/m². Planar components saturate at moderate densities, whereas beams are more sensitive to down-sampling. By isolating density effects and enforcing one protocol, the study provides reproducible, model-agnostic guidance for scan planning and compute budgeting in indoor mapping and Scan-to-BIM workflows. Full article

(This article belongs to the Special Issue Application of LiDAR Remote Sensing and Mapping)

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20 pages, 2565 KB

Open AccessArticle

GBV-Net: Hierarchical Fusion of Facial Expressions and Physiological Signals for Multimodal Emotion Recognition

by Jiling Yu, Yandong Ru, Bangjun Lei and Hongming Chen

Sensors 2025, 25(20), 6397; https://doi.org/10.3390/s25206397 (registering DOI) - 16 Oct 2025

A core challenge in multimodal emotion recognition lies in the precise capture of the inherent multimodal interactive nature of human emotions. Addressing the limitation of existing methods, which often process visual signals (facial expressions) and physiological signals (EEG, ECG, EOG, and GSR) in [...] Read more.

A core challenge in multimodal emotion recognition lies in the precise capture of the inherent multimodal interactive nature of human emotions. Addressing the limitation of existing methods, which often process visual signals (facial expressions) and physiological signals (EEG, ECG, EOG, and GSR) in isolation and thus fail to exploit their complementary strengths effectively, this paper presents a new multimodal emotion recognition framework called the Gated Biological Visual Network (GBV-Net). This framework enhances emotion recognition accuracy through deep synergistic fusion of facial expressions and physiological signals. GBV-Net integrates three core modules: (1) a facial feature extractor based on a modified ConvNeXt V2 architecture incorporating lightweight Transformers, specifically designed to capture subtle spatio-temporal dynamics in facial expressions; (2) a hybrid physiological feature extractor combining 1D convolutions, Temporal Convolutional Networks (TCNs), and convolutional self-attention mechanisms, adept at modeling local patterns and long-range temporal dependencies in physiological signals; and (3) an enhanced gated attention fusion module capable of adaptively learning inter-modal weights to achieve dynamic, synergistic integration at the feature level. A thorough investigation of the publicly accessible DEAP and MAHNOB-HCI datasets reveals that GBV-Net surpasses contemporary methods. Specifically, on the DEAP dataset, the model attained classification accuracies of 95.10% for Valence and 95.65% for Arousal, with F1-scores of 95.52% and 96.35%, respectively. On MAHNOB-HCI, the accuracies achieved were 97.28% for Valence and 97.73% for Arousal, with F1-scores of 97.50% and 97.74%, respectively. These experimental findings substantiate that GBV-Net effectively captures deep-level interactive information between multimodal signals, thereby improving emotion recognition accuracy. Full article

(This article belongs to the Section Biomedical Sensors)

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23 pages, 2529 KB

Open AccessArticle

Image Representation-Driven Knowledge Distillation for Improved Time-Series Interpretation on Wearable Sensor Data

by Jae Chan Jeong, Matthew P. Buman, Pavan Turaga and Eun Som Jeon

Sensors 2025, 25(20), 6396; https://doi.org/10.3390/s25206396 (registering DOI) - 16 Oct 2025

With the increased demand for wearable sensors, image representations—such as persistence images and Gramian angular fields—transformed from time-series data have been investigated to address challenges in wearables arising from physiological variations, sensor noise, and limitations in capturing contextual information. To preserve the lightweight [...] Read more.

With the increased demand for wearable sensors, image representations—such as persistence images and Gramian angular fields—transformed from time-series data have been investigated to address challenges in wearables arising from physiological variations, sensor noise, and limitations in capturing contextual information. To preserve the lightweight structural design of models, knowledge distillation (KD) has also been employed alongside image representations during training to distill smaller and more efficient models. Although image representations play a key role in providing richer and more informative features in training a model, their effectiveness within the KD framework has not been thoroughly explored. In this paper, we focus on image representation-driven KD to investigate whether these representations can provide useful knowledge leading to improved time-series interpretation in activity classification tasks. We explore the benefits of integrating image representations into KD, and we analyze the interplay between representation richness and model compactness with different combinations of teacher and student networks. We also introduce diverse KD strategies to utilize image representations, and we demonstrate the strategies with various perspectives, such as analysis of noises, generalizability, and compatibility, across datasets of varying scales to obtain comprehensive and insightful observations. These offer valuable insights for designing efficient and high-performance wearable sensor-based systems. Full article

(This article belongs to the Special Issue Wearable Sensors for Continuous Health Monitoring and Analysis)

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19 pages, 5533 KB

Open AccessArticle

Application of Wireless EMG Sensors for Assessing Agonist–Antagonist Muscle Activity During 50-m Sprinting in Athletes

by Kanta Yokota and Hiroyuki Tamaki

Sensors 2025, 25(20), 6395; https://doi.org/10.3390/s25206395 (registering DOI) - 16 Oct 2025

Background: Wireless surface electromyography (sEMG) enables the investigation of neuromuscular control in realistic sports settings; however, ensuring reliable signal acquisition during sprinting remains challenging. This study examined the feasibility of continuous wireless EMG recording in sprinting athletes and evaluated their agonist–antagonist coordination patterns. [...] Read more.

Background: Wireless surface electromyography (sEMG) enables the investigation of neuromuscular control in realistic sports settings; however, ensuring reliable signal acquisition during sprinting remains challenging. This study examined the feasibility of continuous wireless EMG recording in sprinting athletes and evaluated their agonist–antagonist coordination patterns. Methods: Ten trained sprinters performed four maximal 50-m sprints on a force plate–equipped track. sEMG was recorded from the biceps femoris (BF), rectus femoris (RF), soleus (Sol), and tibialis anterior (TA) under two receiver configurations: fixed-receiver condition (FRC) and mobile-receiver condition (MRC). Integrated EMG, kinematics, and cross-correlation analyses were performed on a stride-by-stride basis. Results: Continuous high-quality EMG was feasible under MRC, highlighting the practical importance of maintaining receiver proximity in sprint experiments. BF activity during the late swing phase correlated positively with sprint velocity, supporting the performance relevance of pawing. BF/RF interactions varied substantially across individuals, whereas Sol/TA were consistently coactivated, indicating ankle stabilization. Conclusions: Wireless EMG enables reliable in-field monitoring of sprinting athletes, revealing both individualized and shared coordination strategies relevant to performance and injury prevention in athletes. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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51 pages, 4751 KB

Open AccessReview

Large Language Models and 3D Vision for Intelligent Robotic Perception and Autonomy

by Vinit Mehta, Charu Sharma and Karthick Thiyagarajan

Sensors 2025, 25(20), 6394; https://doi.org/10.3390/s25206394 (registering DOI) - 16 Oct 2025

With the rapid advancement of artificial intelligence and robotics, the integration of Large Language Models (LLMs) with 3D vision is emerging as a transformative approach to enhancing robotic sensing technologies. This convergence enables machines to perceive, reason, and interact with complex environments through [...] Read more.

With the rapid advancement of artificial intelligence and robotics, the integration of Large Language Models (LLMs) with 3D vision is emerging as a transformative approach to enhancing robotic sensing technologies. This convergence enables machines to perceive, reason, and interact with complex environments through natural language and spatial understanding, bridging the gap between linguistic intelligence and spatial perception. This review provides a comprehensive analysis of state-of-the-art methodologies, applications, and challenges at the intersection of LLMs and 3D vision, with a focus on next-generation robotic sensing technologies. We first introduce the foundational principles of LLMs and 3D data representations, followed by an in-depth examination of 3D sensing technologies critical for robotics. The review then explores key advancements in scene understanding, text-to-3D generation, object grounding, and embodied agents, highlighting cutting-edge techniques such as zero-shot 3D segmentation, dynamic scene synthesis, and language-guided manipulation. Furthermore, we discuss multimodal LLMs that integrate 3D data with touch, auditory, and thermal inputs, enhancing environmental comprehension and robotic decision-making. To support future research, we catalog benchmark datasets and evaluation metrics tailored for 3D-language and vision tasks. Finally, we identify key challenges and future research directions, including adaptive model architectures, enhanced cross-modal alignment, and real-time processing capabilities, which pave the way for more intelligent, context-aware, and autonomous robotic sensing systems. Full article

(This article belongs to the Special Issue Advanced Sensors and AI Integration for Human–Robot Teaming)

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