Search Results (143)

Background: Smart rings enable continuous physiological monitoring through finger-worn sensors. Despite growing consumer adoption, their clinical utility beyond sleep tracking remains unclear. Objectives: To systematically review evidence for smart ring applications in clinical medicine, assess measurement accuracy, and evaluate clinical outcomes. Methods: We searched PubMed/MEDLINE, Embase, Cochrane Library, and Web of Science through 31 July 2025. Two reviewers independently screened studies and extracted data. Risk of bias was assessed using ROBINS-I and RoB 2.0. Results: From 862 citations, 107 studies met inclusion criteria including approximately 100,000 participants. Studies were equally distributed between sleep (47.7%) and non-sleep applications (52.3%). Smart rings demonstrated high accuracy: heart rate r² = 0.996, heart rate variability r² = 0.980, and sleep detection 93–96% sensitivity. Predictive capabilities included COVID-19 detection 2.75 days pre-symptom (82% sensitivity), inflammatory bowel disease flare prediction 7 weeks early (72% accuracy), and bipolar episode detection 3–7 days early (79% sensitivity). However, 65% of studies had moderate-to-high bias risk. Limitations included small samples, proprietary algorithms (89%), poor diversity reporting (35%), and declining adherence (80% at 3 months to 43% at 12 months). Conclusion: Smart rings have evolved into clinical tools capable of early disease detection. However, algorithmic opacity, population homogeneity, and adherence challenges require attention before widespread implementation. Full article

Multi-Object Tracking (MOT) aims to associate multiple objects across consecutive video sequences and maintain continuous and stable trajectories. Currently, much attention has been paid to data association problems, where many methods filter detection boxes for object matching based on the confidence scores (CS) of the detectors without fully utilizing the detection results. Kalman filter (KF) is a traditional means for sequential frame processing, which has been widely adopted in MOT. It matches and updates a predicted trajectory with a detection box in video. However, under crowded scenes, the noise will create low-confidence detection boxes, causing identity switch (IDS) and tracking failure. In this paper, we thoroughly investigate the limitations of existing trajectory prediction schemes in MOT and prove that KF can still achieve competitive results in video sequence processing if proper care is taken to handle the noise. We propose a confidence-based trajectory prediction scheme (dubbed ConfMOT) based on KF. The CS of the detection results is used to adjust the noise during updating KF and to predict the trajectories of the tracked objects in videos. While a cost matrix (CM) is constructed to measure the cost of successful matching of unreliable objects. Meanwhile, each trajectory is labeled with a unique CS, while the lost trajectories that have not been updated for a long time will be removed. Our tracker is simple yet efficient. Extensive experiments have been conducted on mainstream datasets, where our tracker has exhibited superior performance to other advanced competitors. Full article

While YOLO’s efficiency and accuracy have made it a popular choice for object detection and tracking in real-world applications, models trained on smaller datasets often suffer from intermittent detection failures, where objects remain undetected across multiple consecutive frames, significantly degrading tracking performance in practical scenarios. To address this challenge, we propose PrED (Predictive Enhancement of Detection), a novel framework that enhances object detection and aids in tracking by integrating low-confidence detections with multiple similarity metrics—including Intersection over Union (IoU), spatial distance similarity, and template similarity, and predicts the locations of undetected objects based on a parameter called predictability index. By maintaining object continuity during missed detections, PrED ensures robust tracking performance even when the underlying detection model experiences failures. Extensive evaluations across multiple benchmark datasets demonstrate PrED’s superior performance, achieving over 11% higher DetA with at least 6.9% MOTA improvement in our test scenarios, 17% higher detection accuracy (DetA) and 12.3% higher Multiple Object Tracking Accuracy (MOTA) on the KITTI training dataset, 8% higher DetA and 2.6% higher MOTA on the MOT17 training dataset, compared to ByteTrack, establishing PrED as an effective solution for enhancing tracking robustness in scenarios with suboptimal detection performance. Full article

Multi-object tracking (MOT) is a critical task in computer vision, with widespread applications in intelligent surveillance, behavior analysis, autonomous navigation, and marine ecological monitoring. In particular, accurate tracking of underwater fish plays a significant role in scientific fishery management, biodiversity assessment, and behavioral analysis of marine species. However, MOT remains particularly challenging due to low visibility, frequent occlusions, and the highly non-linear, burst-like motion of fish. To address these challenges, this paper proposes an improved tracking framework that integrates Interacting Multiple Model Kalman Filtering (IMM-KF) into DeepSORT, forming a self-adaptive multi-object tracking algorithm tailored for underwater fish tracking. First, a lightweight YOLOv8n (You Only Look Once v8 nano) detector is employed for target localization, chosen for its balance between detection accuracy and real-time efficiency in resource-constrained underwater scenarios. The tracking stage incorporates two complementary motion models—Constant Velocity (CV) for regular cruising and Constant Acceleration (CA) for rapid burst swimming. The IMM mechanism dynamically evaluates the posterior probability of each model given the observations, adaptively selecting and fusing predictions to maintain both responsiveness and stability. The proposed method is evaluated on a real-world underwater fish dataset collected from the East China Sea, comprising 19 species of marine fish annotated in YOLO format. Experimental results show that the IMM-DeepSORT framework outperforms the original DeepSORT in terms of MOTA, MOTP, and IDF1. In particular, it significantly reduces false matches and improves tracking continuity, demonstrating the method’s effectiveness and reliability in complex underwater multi-target tracking scenarios. Full article

Remote sensing video (RSV) provides continuous, high spatiotemporal earth observations that are increasingly important for environmental monitoring, disaster response, infrastructure inspection and urban management. Despite this potential, operational use of video streams is hindered by very large data volumes, heterogeneous acquisition platforms, inconsistent preprocessing practices, and the absence of standardized formats that deliver data ready for immediate analysis. These shortcomings force repeated low-level computation, complicate semantic extraction, and limit reproducibility and cross-sensor integration. This manuscript presents a principled multi-level analysis-ready data (ARD) model for remote sensing video, named VideoARD, along with VideoCube, a spatiotemporal management and query infrastructure that implements and operationalizes the model. VideoARD formalizes semantic abstraction at scene, object, and event levels and defines minimum and optimal readiness configurations for each level. The proposed pipeline applies stabilization, georeferencing, key frame selection, object detection, trajectory tracking, event inference, and entity materialization. VideoCube places the resulting entities into a five-dimensional structure indexed by spatial, temporal, product, quality, and semantic dimension, and supports earth observation OLAP-style operations to enable efficient slicing, aggregation, and drill down. Benchmark experiments and three application studies, covering vessel speed monitoring, wildfire detection, and near-real-time three-dimensional reconstruction, quantify system performance and operational utility. Results show that the proposed approach achieves multi-gigabyte-per-second ingestion under parallel feeds, sub-second scene retrieval for typical queries, and second-scale trajectory reconstruction for short tracks. Case studies demonstrate faster alert generation, improved detection consistency, and substantial reductions in preprocessing and manual selection work compared with on-demand baselines. The principal trade-off is an upfront cost for materialization and storage that becomes economical when queries are repeated or entities are reused. The contribution of this work lies in extending the analysis-ready data concept from static imagery to continuous video streams and in delivering a practical, scalable architecture that links semantic abstraction to high-performance spatiotemporal management, thereby improving responsiveness, reproducibility, and cross-sensor analysis for Earth observation. Full article

Background/Objectives: Since its emergence in late 2019, SARS-CoV-2 has demonstrated remarkable genetic diversity driven by mutations and recombination events that shaped the course of the COVID-19 pandemic. Continuous genomic monitoring is essential to track viral evolution, assess the spread of variants of concern (VOCs), and inform public health strategies. The present study aimed to characterize the molecular epidemiology of SARS-CoV-2 in northern Greece from the first national case in February 2020 through early 2025. Methods: A total of 66 respiratory samples collected from hospitalized patients across Northern Greece were subjected to whole-genome sequencing using Oxford Nanopore Technologies’ MinION Mk1C platform and the ARTIC protocol. Sequences were analyzed with PANGO, Nextclade, and GISAID nomenclature systems for lineage and clade assignment, and the WHO nomenclature for VOCs. Results: Across 66 genomes, 34 PANGO lineages were identified. Early introductions included B.1 (2/66), B.1.177 (3/66), and B.1.258 (1/66). Alpha (5/66) and Beta (5/66) circulated in February–June 2021. Delta (AY.43) was detected in early 2022 (2/66; Jan–Feb) but was rapidly displaced by Omicron and reached 100% of the sequences by May 2022. Omicron diversified into BA.1/BA.1.1 (3/66), BA.2 (6/66), BA.4/BA.5 (14/66), BF.5 (1/66), EG.5 (1/66; designated a WHO Variant of Interest in 2023), JN.1 (4/66; globally dominant lineage prompting vaccine updates in 2024–2025), KS.1 (2/66; together with KS.1.1 are recognized PANGO lineages that were tracked internationally but remained less prevalent), KP.3 (5/66; together with KP.3.1.1, prominent “FLiRT” descendants circulating in 2024), and recombinants XDK, XDD, and XEC (5/66), reported by their PANGO names in accordance with the WHO’s current framework, which reserves Greek letters only for newly designated VOCs. Conclusions: This five-year genomic analysis provides an insight into the continuous evolution of SARS-CoV-2 in northern Greece. The findings underscore the importance of sustained genomic surveillance, integrated with epidemiological data, to detect emerging variants, monitor recombination, and strengthen preparedness for future coronavirus threats. Full article

Background: Effective asset tracking and monitoring are critical for modern warehouse management. Methods: In this paper, we present a real-time warehouse monitoring system that leverages ceiling-mounted cameras, computer vision-based object detection, a knowledge-graph based world model. The system is implemented in two architectural configurations: a distributed setup with edge processing and a centralized setup. Results: Experimental results demonstrate the system’s capability to accurately detect and continuously track common warehouse assets such as pallets, boxes, and forklifts. This work provides a detailed methodology, covering aspects from camera placement and neural network training to world model integration and real-world deployment. Conclusions: Our experiments show that the system achieves high detection accuracy and reliable real-time tracking across multiple viewpoints, and it is easily scalable to large-scale logistics and inventory applications. Full article

In low-altitude multi-object tracking (MOT), challenges such as frequent inter-object occlusion and complex non-linear motion disrupt the appearance of individual targets and the continuity of their trajectories, leading to frequent tracking failures. We posit that the relatively stable spatio-temporal relationships within object groups (e.g., pedestrians and vehicles) offer powerful contextual cues to resolve such ambiguities. We present NOWA-MOT (Neighbors Know Who We Are), a novel tracking-by-detection framework designed to systematically exploit this principle through a multi-stage association process. We make three primary contributions. First, we introduce a Low-Confidence Occlusion Recovery (LOR) module that dynamically adjusts detection scores by integrating IoU, a novel Recovery IoU (RIoU) metric, and location similarity to surrounding objects, enabling occluded targets to participate in high-priority matching. Second, for initial data association, we propose a Graph Cross-Attention (GCA) mechanism. In this module, separate graphs are constructed for detections and trajectories, and a cross-attention architecture is employed to propagate rich contextual information between them, yielding highly discriminative feature representations for robust matching. Third, to resolve the remaining ambiguities, we design a cascaded Matched Neighbor Guidance (MNG) module, which uniquely leverages the reliably matched pairs from the first stage as contextual anchors. Through MNG, star-shaped topological features are built for unmatched objects relative to their stable neighbors, enabling accurate association even when intrinsic features are weak. Our comprehensive experimental evaluation on the VisDrone2019 and UAVDT datasets confirms the superiority of our approach, achieving state-of-the-art HOTA scores of 51.34% and 62.69%, respectively, and drastically reducing identity switches compared to previous methods. Full article

This study proposes a behavioral monitoring framework for newborn piglets based on SPMF-YOLO object detection and ByteTrack multi-object tracking, which enables precise quantification of early postnatal activity levels and health assessment. The method enhances small-object detection performance by incorporating the SPDConv module, the MFM module, and the NWD loss function into YOLOv11. When combined with the ByteTrack algorithm, it achieves stable tracking and maintains trajectory continuity for multiple targets. An annotated dataset containing both detection and tracking labels was constructed using video data from 10 piglet pens for evaluation. Experimental results indicate that SPMF-YOLO achieved a recognition accuracy rate of 95.3% for newborn piglets. When integrated with ByteTrack, it achieves 79.1% HOTA, 92.2% MOTA, and 84.7% IDF1 in multi-object tracking tasks, thereby outperforming existing methods. Building upon this foundation, this study further quantified the cumulative movement distance of each newborn piglet within 30 min after birth and proposed a health-assessment method based on statistical thresholds. The results demonstrated an overall consistency rate of 98.2% across pens and an accuracy rate of 92.9% for identifying abnormal individuals. The results validated the effectiveness of this method for quantifying individual behavior and assessing health status in newborn piglets within complex farming environments, providing a feasible technical pathway and scientific basis for health management and early intervention in precision animal husbandry. Full article

Natural latex harvest is pushing the boundaries of unmanned agricultural production in rubber milk collection via integrated robots in hilly and mountainous regions, such as the fixed and mobile tapping robots widely deployed in forests. As there are bad working conditions and complex natural environments surrounding rubber trees, the real-time and precision assessment of rubber milk yield status has emerged as a key requirement for improving the efficiency and autonomous management of these kinds of large-scale automatic tapping robots. However, traditional manual rubber milk yield status detection methods are limited in their ability to operate effectively under conditions involving complex terrain, dense forest backgrounds, irregular surface geometries of rubber milk, and the frequent occlusion of rubber milk bowls (RMBs) by vegetation. To address this issue, this study presents an unmanned aerial vehicle (UAV) imagery rubber milk yield state detection method, termed YOLOv8n-RMB, in unstructured field environments instead of manual watching. The proposed method improved the original YOLOv8n by integrating structural enhancements across the backbone, neck, and head components of the network. First, a receptive field attention convolution (RFACONV) module is embedded within the backbone to improve the model’s ability to extract target-relevant features in visually complex environments. Second, within the neck structure, a bidirectional feature pyramid network (BiFPN) is applied to strengthen the fusion of features across multiple spatial scales. Third, in the head, a content-aware dynamic upsampling module of DySample is adopted to enhance the reconstruction of spatial details and the preservation of object boundaries. Finally, the detection framework is integrated with the BoT-SORT tracking algorithm to achieve continuous multi-object association and dynamic state monitoring based on the filling status of RMBs. Experimental evaluation shows that the proposed YOLOv8n-RMB model achieves an AP@0.5 of 94.9%, an AP@0.5:0.95 of 89.7%, a precision of 91.3%, and a recall of 91.9%. Moreover, the performance improves by 2.7%, 2.9%, 3.9%, and 9.7%, compared with the original YOLOv8n. Plus, the total number of parameters is kept within 3.0 million, and the computational cost is limited to 8.3 GFLOPs. This model meets the requirements of yield assessment tasks by conducting computations in resource-limited environments for both fixed and mobile tapping robots in rubber plantations. Full article

This paper presents the final implemented design and performance evaluation of the ground-based C-band Cheia radar system, developed to enhance Romania’s contribution to the EU Space Surveillance and Tracking (EU SST) network. All data used for performance analysis are real-time, real-life measurements of true spatial test objects orbiting Earth. The radar is based on two decommissioned 32 m satellite communication antennas already present at the Cheia Satellite Communication Center, that were retrofitted for radar operation in a quasi-monostatic architecture. A Linear Frequency Modulated Continuous Wave (LFMCW) Radar design was implemented, using low transmitted power (2.5 kW) and advanced software-defined signal processing for detection and tracking of Low Earth Orbit (LEO) targets. System validation involved dry-run acceptance tests and calibration campaigns with known reference satellites. The radar demonstrated accurate measurements of range, Doppler velocity, and angular coordinates, with the capability to detect objects with radar cross-sections as low as 0.03 m² at slant ranges up to 1200 km. Tracking of medium and large Radar Cross Section (RCS) targets remained robust under both fair and adverse weather conditions. This work highlights the feasibility of re-purposing legacy satellite infrastructure for SST applications. The Cheia radar provides a cost-effective, EUSST-compliant performance solution using primarily commercial off-the-shelf components. The system strengthens the EU SST network while demonstrating the advantages of LFMCW radar architectures in electromagnetically congested environments. Full article

Small-scale poultry farms often lack access to advanced monitoring tools and rely heavily on manual observation, which is time-consuming, inconsistent, and insufficient for precise flock management. Feeding and drinking behaviors are critical, as they serve as early indicators of health and environmental issues. With global poultry production expanding, raising over 70 billion hens annually, there is an urgent need for intelligent, low-cost systems that can continuously and accurately monitor bird behavior in resource-limited farm settings. This paper presents the development of a computer vision-based chicken behavior monitoring system, specifically designed for small barn environments where at most 10–15 chickens are housed at any time. The developed system consists of an object detection model, created on top of the YOLOv8 model, trained with an imagery dataset of laying hen, feeder, and waterer objects. Although chickens are visually indistinguishable, the system processes each detection per frame using bounding boxes and movement-based approximation identification rather than continuous identity tracking. The approach simplifies the tracking process without losing valuable behavior insights. Over 700 frames were annotated manually for high-quality labeled data, with different lighting, hen positions, and interaction angles with dispensers. The images were annotated in YOLO format and used for training the detection model for 100 epochs, resulting in a model having an average mean average precision (mAP@0.5) metric value of 91.5% and a detection accuracy of over 92%. The proposed system offers an efficient, low-cost solution for monitoring chicken feeding and drinking behaviors in small-scale farms, supporting improved management and early health detection. Full article

Wearable technology is rapidly evolving, with increasing efforts to integrate a wide range of sensors capable of capturing real-time physiological and behavioral health data from users. These devices have shown significant promise in supporting health monitoring and promoting well-being by providing continuous, objective feedback based on data analytics. Importantly, they enable early detection of potential health issues, allowing for timely intervention and more personalized healthcare. While a wide variety of commercially available wearable devices are designed for adults—tracking metrics such as physical activity, heart rate, body temperature, electrocardiograms (ECG), and oxygen saturation—there remains a notable gap in the availability and development of wearable technologies specifically tailored to the pediatric population. This narrative review paper focuses on non-invasive wearable technologies developed for individuals under the age of 18, with an emphasis on health-related applications. We examine the current landscape of pediatric wearable research, including devices aimed at monitoring developmental progress and chronic health conditions. Particular attention is given to the limited research on wearables for younger children, where physiological and developmental differences pose additional challenges. Furthermore, we explore emerging applications, identify key barriers to adoption, and discuss opportunities for future development, including improvements in design, data privacy, and age-appropriate functionality. Full article

Multi Small-Object Tracking (MSOT) is crucial for drone inspection and intelligent monitoring, yet traditional Multiple-object Tracking (MOT) methods perform poorly in such scenarios. The reasons include the following: small targets have low resolution and sparse features, leading to high missed detection rates; frequent occlusion and motion blur in dense scenes cause trajectory interruption and identity switches. To address these issues, an MSOT method combining dual motion modeling and dynamic Region of Interest (ROI) detection is proposed. The dual motion framework integrates Kalman filtering and optical flow through dynamic weighting to optimize target state estimation. The Kalman filter-guided dynamic ROI mechanism, combined with multi-feature fusion, enables trajectory recovery when targets are lost. Experiments on the VisDrone-MOT and UAVDT datasets show that this method outperforms mainstream algorithms in core metrics such as MOTA and HOTA, with better trajectory continuity and identity consistency while maintaining good real-time performance. Full article

Avocado orchards are among the most profitable and fastest-growing commodity crops in Mexico, especially in the area known as the “Avocado Belt”. Several efforts have been made to monitor their expansion; however, there is currently no method that can be easily updated to track this expansion. The main objective of this study was to monitor the expansion of avocado orchards from 1993 to 2024, using the Continuous Change Detection and Classification (CCDC) algorithm and Landsat 5, 7, 8, and 9 imagery. Presence/absence maps of avocado orchards corresponding to 1 January of each year were used to perform a trajectory analysis, identifying eight possible change trajectories. Finally, maps from 2020 to 2023 were verified using reference data and very-high-resolution images. The maps showed a level of agreement = 0.97, while the intersection over union for the avocado orchard class was 0.62. The main results indicate that the area occupied by avocado orchards more than tripled from 1993 to 2024, from 64,304.28 ha to 200,938.32 ha, with the highest expansion occurring between 2014 and 2024. The trajectory analysis confirmed that land conversion to avocado orchards is generally permanent and happens only once (i.e., gain without alternation). The method proved to be a robust approach for monitoring avocado orchard expansion and could be an attractive alternative for regularly updating this information. Full article