Measurement Sensors最新文献

{"title":"Deep learning approaches for continuous blood pressure estimation from photoplethysmography signal","authors":"R. Vanithamani,&nbsp;S. Sri Jayabharathi,&nbsp;S. Pavithra,&nbsp;E. Smily Jeya Jothi","doi":"10.1016/j.measen.2025.101866","DOIUrl":"10.1016/j.measen.2025.101866","url":null,"abstract":"<div><h3>Introduction</h3><div>Monitoring continuous Blood Pressure (BP) signals is essential as BP can vary rapidly. However, current Photoplethysmography (PPG)-based methods for estimating BP need to be more accurate and provide predictions for Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP).</div></div><div><h3>Materials and methods</h3><div>Full cycle of PPG waveform is considered to estimate SBP and DBP values. This study recommends deep learning techniques, including Temporal Convolutional Network (TCN), Long-Short Term Memory (LSTM), TCN-LSTM, and Autoencoder-LSTM, to estimate SBP and DBP.</div></div><div><h3>Results</h3><div>According to the outcomes, the proposed framework estimates continuous BP precisely utilizing PPG signals. Specifically, the Autoencoder-LSTM algorithm achieved a Mean Average Error (MAE) of 1.05 and 0.92 for SBP and DBP and a Standard Deviation (SD) of 1.89 and 1.05 for SBP and DBP, respectively, indicating that the model is suitable for estimating these values from PPG signals. The Autoencoder-LSTM approach produced a Mean Average Error (MAE) of 1.05 and 0.92 for SBP and DBP, respectively, as well as a Standard Deviation (SD) of 1.89 and 1.05, demonstrating that the model can estimate these values using PPG signals.</div></div><div><h3>Conclusion</h3><div>This paper evaluates an algorithm that estimates BP continuously using the PPG signal. Autoencoder-LSTM is suitable for estimating continuous BP values since MAE and SD values are low for SBP and DBP.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101866"},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for liver evaluation: A comprehensive review and implications for ulcerative colitis detection","authors":"Sunaina Verma ,&nbsp;Manju Bala ,&nbsp;Mohit Angurala","doi":"10.1016/j.measen.2025.101867","DOIUrl":"10.1016/j.measen.2025.101867","url":null,"abstract":"<div><div>This review explores the applications of deep learning based computer-aided diagnosis (DL-CAD) systems when evaluating liver images derived from Computed Tomography (CT) scans. It highlights the ability of contemporary state of the art deep learning frameworks such as Convolutional Neural Networks (CNNs) and UNets, to automate the liver lesions segmentation and classification with great accuracy. The analysis further expands on the relationship that existed between some systemic illnesses such as ulcerative colitis (UC) and specific liver related conditions such as Primary Sclerosing Cholangitis, fatty liver and autoimmune hepatitis. The above conditions which are frequently present in UC patients once again underpin the importance of imaging techniques in the provision of appropriate and timely treatment. Our research shows that the DL-CAD system may be modified appropriately in order to identify liver changes caused by UC which has advantages in diagnosis without overburdening radiologists. Furthermore, the inclusion of wearable devices for periodic liver evaluation further supports the concept of personalized patient management. Hence, this study includes notable improvements in the analysis of liver lesions and their complications in UC patients with respect to the clinical practice and treatment results.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101867"},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A fusion positioning system with environmental-adaptive algorithm: IPSO-IAUKF fusion of UWB and IMU for NLOS noise mitigation","authors":"Yiyang Lyu ,&nbsp;Mingsheng Wei ,&nbsp;Shidang Li ,&nbsp;Di Wang","doi":"10.1016/j.measen.2025.101864","DOIUrl":"10.1016/j.measen.2025.101864","url":null,"abstract":"<div><div>Accurate positioning in non-line-of-sight (NLOS) scenarios persists as a critical challenge for ultra-wideband (UWB) systems. This paper proposes a collaborative positioning framework that integrates an inertial measurement unit (IMU). An improved particle swarm optimization and adaptive unscented Kalman filter (IPSO-IAUKF) algorithm based on environmental assessment is also designed. The threefold contributions include: (1) A tightly coupled positioning system architecture is constructed by deeply integrating UWB ranging with IMU motion measurements; (2) An improved particle swarm optimization (IPSO) algorithm is proposed to optimize the initial coordinate estimation of UWB using a dynamic inertia weight strategy; (3) An adaptive Unscented Kalman Filter (UKF) framework is designed, incorporating an environmental state discrimination threshold and a real-time noise matrix update mechanism to dynamically optimize the covariance matrix, thereby enhancing positioning robustness in complex noise environments. Multi-scenario trajectory simulations and practical experiments are conducted based on the established positioning model. Numerical simulation results demonstrate that the proposed fusion framework achieves a 52.6 % improvement in positioning accuracy compared to standalone UWB solutions, with a 44.6 % enhancement in noise resistance under NLOS interference compared to traditional fusion algorithms. Further practical tests reveal that the IPSO-IAUKF algorithm achieves average positioning accuracy improvements of 52.1 %, 45.5 %, and 46.0 % in two typical noise environments compared to conventional UKF and algorithms 1 and 2 used in this paper, respectively, while the maximum positioning error decreases by 44.6 %, 23.9 %, and 29.7 %, respectively. These results verify the superiority of this method in complex scenarios.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101864"},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a novel disposable flowcell for spectroscopic bioprocess monitoring","authors":"Phil Thiel ,&nbsp;Tobias Steinwedel ,&nbsp;Philipp Raithel ,&nbsp;Mathias Belz ,&nbsp;Dörte Solle","doi":"10.1016/j.measen.2025.101862","DOIUrl":"10.1016/j.measen.2025.101862","url":null,"abstract":"<div><div>Regulatory authorities require product control for market release, especially for medical products due to legal regulations. Thus, end product control is conducted before drug market release. For real-time release in terms of Process Analytical Technology (PAT), product quality must be designed into the process. Process sensors are needed to monitor critical process parameters (CPP) for immediate control. Conventional sensors lack interfaces for disposable bioreactors, but new flow cell systems enable spectroscopic bioprocess monitoring via a bypass system. The flow cell is gamma-sterilized and clamped into a reusable holder, allowing spectroscopic techniques like turbidity, UV/VIS spectroscopy, and fluorescence.</div><div>The cell setup and biocompatibility are presented, with in-vitro toxicity of various 3D printable materials evaluated per ISO 10993 to find suitable materials. Polyamide (PA), Acrylonitrile Butadiene Styrene (ABS) and Polymethyl Methacrylate (PMMA) were used for manufacturing flow cells and tested for in vitro biocompatibility. Results confirm the suitability of these materials and processes, with UV–VIS spectroscopy providing key insights. Selectivity and sensitivity for three different important bioprocess variables were evaluated and enables precise sensor system characterization across various analytes, advancing flow cell and sensor technology in biosensing and analytical chemistry.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101862"},"PeriodicalIF":0.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Open-source, real-time, low-cost, wearable head impact monitoring system","authors":"Alaa Aldin Ghazal ,&nbsp;S.G. Ganpule","doi":"10.1016/j.measen.2025.101863","DOIUrl":"10.1016/j.measen.2025.101863","url":null,"abstract":"<div><div>Mild traumatic brain injury (mTBI) is a significant health concern that can occur due to rapid head movements during activities such as contact sports, motor vehicle accidents, industrial mishaps, falls, and combat situations. These events can lead to cellular and chemical changes in the brain, disrupting neural pathways and causing symptoms such as headaches, dizziness, cognitive difficulties, and emotional changes. Raising awareness about mTBI and implementing preventive measures to reduce its incidence and mitigate its impact on affected individuals is crucial. Head kinematics measurement is one of the quickest methods for making on-field initial diagnosis decisions. This paper describes the development of an open-source, low-cost, real-time device that can be attached to a helmet. It monitors the head kinematics data (linear acceleration and rotational speed). It sends it over a Wi-Fi connection to a web browser of a monitor device (PC or Mobile phone) connected to the same network so the user who observes the data can call a doctor to check mTBI symptoms. The device utilizes an inertial measurement unit (IMU) and high-g (g = 9.8 m/s²) linear accelerometers interfaced with an Internet of Things (IoT) based microcontroller (WeMos D1 mini) programmed using the Arduino IDE. This setup facilitates data visualisation through an interactive HTML webpage, enabling the user (e.g., coach, medical personnel) to assess the data and potentially recommend seeking medical attention if concerning readings are observed. The application of this device can be in different areas such as road accidents, contact sports, and mine workers.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101863"},"PeriodicalIF":0.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vehicle maneuver recognition and correction algorithm for road quality measurement system optimization","authors":"Roland Nagy ,&nbsp;István Szalai","doi":"10.1016/j.measen.2025.101816","DOIUrl":"10.1016/j.measen.2025.101816","url":null,"abstract":"<div><div>Vibrations in road vehicles related to road surface damage have a number of harmful consequences for the health of the occupants and for the components of the vehicle. To mitigate these effects and support timely pavement repairs, continuous road condition monitoring is essential. Vibration-based measurement systems have gained prominence in recent years, but their accuracy can be significantly compromised by vehicle maneuvers, particularly on urban or curvy roads. Despite this, the influence of aggressive maneuvers has largely been overlooked in previous studies. In this paper, we address this gap by presenting a comprehensive investigation into the impact of abrupt maneuvers on vibration-based road quality measurement. We introduce a novel, computationally efficient soft-sensor algorithm that detects and isolates aggressive maneuvers using sensor data from existing road quality measurement systems, classifying them into four categories. This algorithm combines rule-based methods with machine learning, offering enhanced accuracy and lower computational costs compared to alternative approaches. In this way, the overall maneuver classification achieves an accuracy of 93%. By applying the introduced approach to identify and correct the influence of maneuvers, we achieved a 7% increase in accuracy of pavement quality classification in a suburban environment and a 10% increase in an urban environment. The proposed solution can be easily integrated into current vibration-based road quality measurement frameworks, enhancing their performance while maintaining scalability and low operational cost.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101816"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simple and inexpensive design of radioactive source for accurate level and height gauging in petrochemical industries","authors":"S.Z. Islami rad ,&nbsp;R. Gholipour Peyvandi","doi":"10.1016/j.measen.2025.101817","DOIUrl":"10.1016/j.measen.2025.101817","url":null,"abstract":"<div><div>Radiation sources are used for measurement and control of industrial processes, determining the height of materials inside the vessel, analyzing the composition and structure of materials, and detecting defects in industrial processes due to the complexity of the production process. In petrochemical industries, the height of urea in a vessel can be measured using the nuclear level gauging method, which is a non-destructive technique. Therefore, the energy of the gamma emitting source, the design, and arrangement of the source geometry (including the point or rod sources), and the detector material (NaI (Tl) crystal or plastic scintillator) are crucial parameters. In this research, a nuclear level gauge, including the source, detector, and reactor containing urea and gases at high temperatures and pressures, was simulated by MCNPX Monte Carlo code and the results were compared and validated with experimental values. Then, the detector's response was evaluated and optimized based on the different arrangements of the radioactive source and its distances, as well as the type and geometry of the detector, and the best arrangement was selected. The comparison of the simulation results and the resulting analysis indicated that using point sources at specific distances (three points) instead of rod sources is a viable alternative due to its simpler structure, higher accuracy and stability, and lower production cost compared to the high cost of rod sources. Additionally, for accurate level and height gauging, rod detectors should be replaced with point detectors.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101817"},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Motor control method using single-sensor phase current reconstruction","authors":"Yin Lu,&nbsp;Yuntian Huang,&nbsp;Hao Guo","doi":"10.1016/j.measen.2024.101803","DOIUrl":"10.1016/j.measen.2024.101803","url":null,"abstract":"<div><div>This work aims to address the current sensing issue in a three-phase bridge inverter circuit and discuss a motor control method based on single-sensor phase current reconstruction. By collecting the motor's current signals and utilizing signal processing techniques such as Fourier transform and wavelet transform, information about the three-phase currents is extracted from the data of a single sensor. Simultaneously, optimization algorithms like neural networks are employed to learn from historical data to predict and estimate the current values of the three phases. Software tools such as MATLAB and LabVIEW are used for data processing and analysis in the implementation process. An experimental platform is set up to verify the accuracy and real-time performance of the reconstruction method. The experimental results indicate that employing the Mixed Space Vector Pulse Width Modulation (MSVPWM) control strategy reduces the reconstruction error from the original e = 3.5 % to e = 3.1 %. The current transition is smooth throughout the vector plane, and even in unobservable regions, the phase current can be accurately reconstructed. The motor control method based on single-sensor phase current reconstruction exhibits high accuracy and real-time performance, meeting practical requirements for motor control. In conclusion, this work provides technical support and a theoretical basis for the precise control of motors.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101803"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wireless sensor network for fire detection with network coding to improve security and reliability","authors":"Johannes Braun,&nbsp;Faouzi Derbel","doi":"10.1016/j.measen.2024.101404","DOIUrl":"10.1016/j.measen.2024.101404","url":null,"abstract":"<div><div>This article proposes a wireless sensor network (WSN) for fire alarm systems that leverages network coding to enhance system reliability and security. The proposed WSN is designed to comply with European standards, with a particular focus on German standards, and enables the deployment of sensor nodes that automatically construct a robust and reliable network. The self-healing, redundant, decentralized routed network ensures uninterrupted functionality in case of a communication failure. The network coding technique is utilized to manage the surge in data traffic during hazard situations, reducing the overall number of telegrams while maintaining data integrity and reliability. By implementing network coding, the proposed WSN reduces energy consumption and enhances the efficiency and reliability of fire alarm systems, thereby contributing to greater safety in emergency situations. To further leverage the advantages of network coding, a new decentralized routing technique is introduced. This technique operates locally on nodes through the use of virtual cluster heads and an innovative Weighted Composite Value (WCV) Table, optimizing routing decisions for improved performance and scalability. This article provides a comprehensive exploration of the benefits of network coding in WSNs for fire alarm systems and presents a real-world implementation, demonstrating its potential for improving the performance of these systems.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101404"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PCA and PSO based optimized support vector machine for efficient intrusion detection in internet of things","authors":"Mutkule Prasad Raghunath ,&nbsp;Shyam Deshmukh ,&nbsp;Poonam Chaudhari ,&nbsp;Sunil L. Bangare ,&nbsp;Kishori Kasat ,&nbsp;Mohan Awasthy ,&nbsp;Batyrkhan Omarov ,&nbsp;Rajesh R. Waghulde","doi":"10.1016/j.measen.2024.101806","DOIUrl":"10.1016/j.measen.2024.101806","url":null,"abstract":"<div><div>The Internet of Things (IoT) is a network that interconnects many everyday objects, including computers, televisions, washing machines, and even whole urban areas. These devices has the capability to collect and disseminate information because to their integration of electronics, software, sensors, and connectivity to a network. The Internet of Things enables the remote sensing, identification, and control of physical things via the utilisation of existing network infrastructure. By using this function, it becomes feasible to integrate elements of the physical world into computerised systems, resulting in enhanced levels of efficiency, precision, and financial profitability. The Internet of Things (IoT) encompasses a diverse array of applications. The Internet of Things (IoT) may be used in several sectors such as healthcare, smart cities, smart homes, transportation, logistics, agriculture, and smart traffic management. The quantity of Internet of Things (IoT) devices is increasing rapidly and exponentially. The surge in numbers is accompanied by a significant escalation in security vulnerabilities. This article presents the development of an intrusion detection system for the Internet of Things using machine learning and feature selection techniques. The system aims to accurately categorise and forecast attacks on IoT devices. This approach utilises the publicly accessible NSL KDD dataset as its input dataset. During the data collecting process for NSL-KDD, all symbolic qualities are transformed into their corresponding numerical representations. Conversely, all numerical features are translated back into symbolic form at the conclusion of the procedure. Principal component analysis is employed to achieve the objective of attribute extraction. After completing the preparation step, the data set is classified using several machine learning techniques such as support vector machine, linear regression, and random forest. Evaluating the veracity, exactness, and retrieval rate of different machine learning algorithms is crucial for choosing the most effective ones. The accuracy of the Intrusion Detection System (IDS) based on Particle Swarm Optimisation (PSO) is 98.5 percent. The PSO-based SVM method is shown superior performance compared to random forest and linear regression methods in terms of precision, recall, and specificity.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101806"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}