Sensors

22 pages, 1096 KiB

Open AccessSystematic Review

Continuous Movement Monitoring at Home Through Wearable Devices: A Systematic Review

by Gianmatteo Farabolini, Nicolò Baldini, Alessandro Pagano, Elisa Andrenelli, Lucia Pepa, Giovanni Morone, Maria Gabriella Ceravolo and Marianna Capecci

Sensors 2025, 25(16), 4889; https://doi.org/10.3390/s25164889 (registering DOI) - 8 Aug 2025

Abstract

Background: Wearable sensors are a promising tool for the remote, continuous monitoring of motor symptoms and physical activity, especially in individuals with neurological or chronic conditions. Despite many experimental trials, clinical adoption remains limited. A major barrier is the lack of awareness and [...] Read more.

Background: Wearable sensors are a promising tool for the remote, continuous monitoring of motor symptoms and physical activity, especially in individuals with neurological or chronic conditions. Despite many experimental trials, clinical adoption remains limited. A major barrier is the lack of awareness and confidence among healthcare professionals in these technologies. Methods: This systematic review analyzed the use of wearable sensors for continuous motor monitoring at home, focusing on their purpose, type, feasibility, and effectiveness in neurological, musculoskeletal, or rheumatologic conditions. This review followed PRISMA guidelines and included studies from PubMed, Scopus, and Web of Science. Results: Seventy-two studies with 7949 participants met inclusion criteria. Neurological disorders, particularly Parkinson’s disease, were the most frequently studied. Common sensors included inertial measurement units (IMUs), accelerometers, and gyroscopes, often integrated into medical devices, smartwatches, or smartphones. Monitoring periods ranged from 24 h to over two years. Feasibility studies showed high patient compliance (≥70%) and good acceptance, with strong agreement with clinical assessments. However, only half of the studies were controlled trials, and just 5.6% were randomized. Conclusions: Wearable sensors offer strong potential for real-world motor function monitoring. Yet, challenges persist, including ethical issues, data privacy, standardization, and healthcare access. Artificial intelligence integration may boost predictive accuracy and personalized care. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Health Monitoring in Clinical and Ecologic Scenarios)

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26 pages, 19304 KiB

Open AccessArticle

FreqDyn-YOLO: A High-Performance Multi-Scale Feature Fusion Algorithm for Detecting Plastic Film Residues in Farmland

by Mingyang Zhang, Jianjie Zhang, Yihang Peng and Yi Wang

Sensors 2025, 25(16), 4888; https://doi.org/10.3390/s25164888 (registering DOI) - 8 Aug 2025

Abstract

Plastic mulch technology plays an important role in increasing agricultural productivity and economic returns. However, residual mulch remaining in agricultural fields poses significant challenges to both crop production and environmental sustainability. Effective recovery and recycling of residual plastic mulch requires accurate detection and [...] Read more.

Plastic mulch technology plays an important role in increasing agricultural productivity and economic returns. However, residual mulch remaining in agricultural fields poses significant challenges to both crop production and environmental sustainability. Effective recovery and recycling of residual plastic mulch requires accurate detection and identification of mulch fragments, which presents a substantial technical challenge. The detection of residual plastic film is complicated by several factors: the visual similarity between residual film fragments and soil in terms of color and texture, as well as the irregular shapes and variable sizes of the target objects. To address these challenges, this study develops FreqDyn-YOLO, a detection model for residual film identification in agricultural environments based on the YOLO11 architecture. The proposed methodology introduces three main technical contributions. First, a Frequency-C3k2 (FreqC3) feature extraction module is implemented, which employs a Frequency Feature Transposed Attention (FreqFTA) mechanism to improve discrimination between residual film and soil backgrounds. Second, a High-Performance Multi-Scale Feature Pyramid Network (HPMSFPN) is developed to enable effective cross-layer feature fusion, enhancing detection performance across different target scales. Third, a Dynamic Detection Head With DCNv4 (DWD4) is introduced to improve the model’s ability to adapt to varying film morphologies while maintaining computational efficiency. Experimental findings on a self-developed agricultural field residual film dataset confirm that FreqDyn-YOLO outperforms the baseline approach, achieving improvements of 5.37%, 1.97%, and 2.96% in mAP50, precision, and recall, respectively. The model also demonstrates superior performance compared to other recent detection methods. This work provides a technical foundation for precise residual film identification in agricultural applications and shows promise for integration into automated recovery systems. Full article

(This article belongs to the Section Smart Agriculture)

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21 pages, 5425 KiB

Open AccessArticle

Designing a Capacitive Sensor to Detect Series Arcs in Aircraft HVDC Electrical Systems

by Gema Salinero and Guillermo Robles

Sensors 2025, 25(16), 4886; https://doi.org/10.3390/s25164886 (registering DOI) - 8 Aug 2025

Abstract

The transition toward more electric aircraft (MEA) and all-electric aircraft (AEA) has driven the adoption of high-voltage DC (HVDC) electrical architectures to meet increasing power demands while reducing weight and enhancing overall efficiency. However, HVDC systems introduce new challenges, particularly concerning insulation reliability [...] Read more.

The transition toward more electric aircraft (MEA) and all-electric aircraft (AEA) has driven the adoption of high-voltage DC (HVDC) electrical architectures to meet increasing power demands while reducing weight and enhancing overall efficiency. However, HVDC systems introduce new challenges, particularly concerning insulation reliability and the detection of in-flight series arc faults. This paper presents the design and evaluation of a capacitive sensor specifically developed to detect series arc faults in HVDC electrical systems for aerospace applications. A model of the sensor is proposed and validated through both simulations and experimental measurements using a step response test. The results show excellent agreement between the model and the physical setup. After validating the capacitive coupling value and its response to high-frequency signals, series arcs were generated in the laboratory to evaluate the sensor’s performance under realistic operating conditions, which involve different signal dynamics. The results are highly satisfactory and confirm the feasibility of using capacitive sensing for early arc detection, particularly aligned with the stringent requirements of more electric aircraft (MEA) and all-electric aircraft (AEA). The proposed sensor thus enables non-intrusive detection of series arc faults in compact, lightweight, and safety-critical environments. Full article

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

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26 pages, 571 KiB

Open AccessArticle

SHARP: Blockchain-Powered WSNs for Real-Time Student Health Monitoring and Personalized Learning

by Zeqiang Xie, Zijian Li and Xinbing Liu

Sensors 2025, 25(16), 4885; https://doi.org/10.3390/s25164885 (registering DOI) - 8 Aug 2025

Abstract

With the rapid advancement of the Internet of Things (IoT), artificial intelligence (AI), and blockchain technologies, educational research has increasingly explored smart and personalized learning systems. However, current approaches often suffer from fragmented integration of health monitoring and instructional adaptation, insufficient prediction accuracy [...] Read more.

With the rapid advancement of the Internet of Things (IoT), artificial intelligence (AI), and blockchain technologies, educational research has increasingly explored smart and personalized learning systems. However, current approaches often suffer from fragmented integration of health monitoring and instructional adaptation, insufficient prediction accuracy of physiological states, and unresolved concerns regarding data privacy and security. To address these challenges, this study introduces SHARP, a novel blockchain-enhanced wireless sensor networks (WSNs) framework designed for real-time student health monitoring and personalized learning in smart educational environments. Wearable sensors enable continuous collection of physiological data, including heart rate variability, body temperature, and stress indicators. A deep neural network (DNN) processes these inputs to detect students’ physical and affective states, while a reinforcement learning (RL) algorithm dynamically generates individualised educational recommendations. A Proof-of-Authority (PoA) blockchain ensures secure, immutable, and transparent data management. Preliminary evaluations in simulated smart classrooms demonstrate significant improvements: the DNN achieves a 94.2% F1-score in state recognition, the RL module reduces critical event response latency, and energy efficiency improves by 23.5% compared to conventional baselines. Notably, intervention groups exhibit a 156% improvement in quiz scores over control groups. Compared to existing solutions, SHARP uniquely integrates multi-sensor physiological monitoring, real-time AI-based personalization, and blockchain-secured data governance in a unified framework. This results in superior accuracy, higher energy efficiency, and enhanced data integrity compared to prior IoT-based educational platforms. By combining intelligent sensing, adaptive analytics, and secure storage, SHARP offers a scalable and privacy-preserving solution for next-generation smart education. Full article

(This article belongs to the Special Issue Sensor-Based Recommender System for Smart Education and Smart Living)

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17 pages, 918 KiB

Open AccessArticle

LTGS-Net: Local Temporal and Global Spatial Network for Weakly Supervised Video Anomaly Detection

by Minghao Li, Xiaohan Wang, Haofei Wang and Min Yang

Sensors 2025, 25(16), 4884; https://doi.org/10.3390/s25164884 (registering DOI) - 8 Aug 2025

Abstract

Video anomaly detection has an important application value in the field of intelligent surveillance; however, due to the problems of sparse anomaly events and expensive labeling, it has made weakly supervised methods a research hotspot. Most of the current methods still adopt the [...] Read more.

Video anomaly detection has an important application value in the field of intelligent surveillance; however, due to the problems of sparse anomaly events and expensive labeling, it has made weakly supervised methods a research hotspot. Most of the current methods still adopt the strategy of processing temporal and spatial features independently, which makes it difficult to fully capture their temporal and spatial complex dependencies, affecting the accuracy and robustness of detection. Existing studies predominantly process temporal and spatial information independently, which limits the ability to effectively capture their interdependencies. To address this, we propose the Local Temporal and Global Spatial Network (LTGS) for weakly supervised video anomaly detection. The LTGS architecture incorporates a clip-level temporal feature relation module and a video-level spatial feature module, which collaboratively enhance discriminative representations. Through joint training of these modules, we develop a feature encoder specifically tailored for video anomaly detection. To further refine clip-level annotations and better align them with actual events, we employ a dynamic label updating strategy. These updated labels are utilized to optimize the model and enhance its robustness. Extensive experiments on two widely used public datasets, ShanghaiTech and UCF-Crime, validate the effectiveness of the proposed LTGS method. Experimental results demonstrate that the LTGS achieves an AUC of 96.69% on the ShanghaiTech dataset and 82.33% on the UCF dataset, outperforming various state-of-the-art algorithms in anomaly detection tasks. Full article

(This article belongs to the Section Sensor Networks)

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28 pages, 11876 KiB

Open AccessArticle

Improved WTCN-Informer Model Based on Frequency-Enhanced Channel Attention Mechanism and Wavelet Convolution: Prediction of Remaining Service Life of Ion Etcher Cooling System

by Tingyu Ma, Jiaqi Liu, Panfeng Xu, Yan Song and Xiaoping Bai

Sensors 2025, 25(16), 4883; https://doi.org/10.3390/s25164883 (registering DOI) - 8 Aug 2025

Abstract

Etching has become a critical step in semiconductor wafer fabrication, and its importance in semiconductor manufacturing highlights the fact that it directly determines the ability of the fab to produce high-process products, as well as the application performance of the chip. While the [...] Read more.

Etching has become a critical step in semiconductor wafer fabrication, and its importance in semiconductor manufacturing highlights the fact that it directly determines the ability of the fab to produce high-process products, as well as the application performance of the chip. While the health of the etcher is a concern, especially for the cooling system, accurately predicting the remaining useful life (RUL) of the etcher cooling system is a critical task. Predictive maintenance (PDM) can be used to monitor the basic condition of the equipment by learning from historical data, and it can help solve the task of RUL prediction. In this paper, we propose the FECAM-WTCN-Informer model, which first obtains a new WTCN structure by inserting wavelet convolution into the TCN, and then combines the discrete cosine transform (DCT) and channel attention mechanism into the temporal neural network (TCN). Multidimensional feature extraction of time series data can be realized, and the processed features are input into the Informer network for prediction. Experimental results show that the method is significantly more accurate in terms of overall prediction performance (MSE, RMSE, and MAE), compared with other state-of-the-art methods, and is suitable for solving the problem of predictive maintenance of etching machine cooling systems. Full article

(This article belongs to the Section Industrial Sensors)

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25 pages, 3724 KiB

Open AccessArticle

Research on Trajectory Tracking Control Method for Wheeled Robots Based on Seabed Soft Slopes on GPSO-MPC

by Dewei Li, Zizhong Zheng, Zhongjun Ding, Jichao Yang and Lei Yang

Sensors 2025, 25(16), 4882; https://doi.org/10.3390/s25164882 (registering DOI) - 8 Aug 2025

Abstract

With advances in underwater exploration and intelligent ocean technologies, wheeled underwater mobile robots are increasingly used for seabed surveying, engineering, and environmental monitoring. However, complex terrains centered on seabed soft slopes—characterized by wheel slippage due to soil deformability and force imbalance arising from [...] Read more.

With advances in underwater exploration and intelligent ocean technologies, wheeled underwater mobile robots are increasingly used for seabed surveying, engineering, and environmental monitoring. However, complex terrains centered on seabed soft slopes—characterized by wheel slippage due to soil deformability and force imbalance arising from slope variations—pose challenges to the accuracy and robustness of trajectory tracking control systems. Model predictive control (MPC), known for predictive optimization and constraint handling, is commonly used in such tasks. Yet, its performance relies on manually tuned parameters and lacks adaptability to dynamic changes. This study introduces a hybrid grey wolf-particle swarm optimization (GPSO) algorithm, combining the exploratory ability of a grey wolf optimizer with the rapid convergence of particle swarm optimization. The GPSO algorithm adaptively tunes MPC parameters online to improve control. A kinematic model of a four-wheeled differential-drive robot is developed, and an MPC controller using error-state linearization is implemented. GPSO integrates hierarchical leadership and chaotic disturbance strategies to enhance global search and local convergence. Simulation experiments on circular and double-lane-change trajectories show that GPSO-MPC outperforms standard MPC and PSO-MPC in tracking accuracy, heading stability, and control smoothness. The results confirm the improved adaptability and robustness of the proposed method, supporting its effectiveness in dynamic underwater environments. Full article

(This article belongs to the Section Sensors and Robotics)

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31 pages, 5417 KiB

Open AccessArticle

Design and Analysis of an Autonomous Active Ankle–Foot Prosthesis with 2-DoF

by Sayat Akhmejanov, Nursultan Zhetenbayev, Aidos Sultan, Algazy Zhauyt, Yerkebulan Nurgizat, Kassymbek Ozhikenov, Abu-Alim Ayazbay and Arman Uzbekbayev

Sensors 2025, 25(16), 4881; https://doi.org/10.3390/s25164881 (registering DOI) - 8 Aug 2025

Abstract

This paper presents the development, modeling, and analysis of an autonomous active ankle prosthesis with two degrees of freedom (2-DoF), designed to reproduce movements in the sagittal (dorsiflexion/plantarflexion) and frontal (inversion/eversion) planes in order to enhance the stability and naturalness of the user’s [...] Read more.

This paper presents the development, modeling, and analysis of an autonomous active ankle prosthesis with two degrees of freedom (2-DoF), designed to reproduce movements in the sagittal (dorsiflexion/plantarflexion) and frontal (inversion/eversion) planes in order to enhance the stability and naturalness of the user’s gait. Unlike most commercial prostheses, which typically feature only one active degree of freedom, the proposed device combines a lightweight mechanical design, a screw drive with a stepper motor, and a microcontroller-based control system. The prototype was developed using CAD modeling in SolidWorks 2024, followed by dynamic modeling and finite element analysis (FEA). The simulation results confirmed the achievement of physiological angular ranges of ±20–22 deg. in both planes, with stable kinematic behavior and minimal vertical displacements. According to the FEA data, the maximum von Mises stress (1.49 × 10⁸ N/m²) and deformation values remained within elastic limits under typical loading conditions, though cyclic fatigue and impact energy absorption were not experimentally validated and are planned for future work. The safety factor was estimated at ~3.3, indicating structural robustness. While sensor feedback and motor dynamics were idealized in the simulation, future work will address real-time uncertainties such as sensor noise and ground contact variability. The developed design enables precise, energy-efficient, and adaptive motion control, with an estimated average power consumption in the range of 7–9 W and an operational runtime exceeding 3 h per charge using a standard 18,650 cell pack. These results highlight the system’s potential for real-world locomotion on uneven surfaces. This research contributes to the advancement of affordable and functionally autonomous prostheses for individuals with transtibial amputation. Full article

(This article belongs to the Special Issue Recent Advances in Sensor Technology and Robotics Integration)

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