Measurement Sensors最新文献

{"title":"Wearable sensor-based intent recognition for adaptive control of intelligent ankle-foot prosthetics","authors":"Vidyapati Kumar,&nbsp;Dilip Kumar Pratihar","doi":"10.1016/j.measen.2025.101865","DOIUrl":"10.1016/j.measen.2025.101865","url":null,"abstract":"<div><div>Prosthetic motor control requires improvement to better adapt to varying gait speeds and terrain inclinations in real time. Traditional methods often fail to meet these demands, prompting research into advanced sensor data and machine learning algorithms. This research study tackles the challenge by using wearable sensors and comparing various machine learning approaches, namely Sparse Bidirectional Long Short-Term Memory (SBLSTM), Adaptive Neuro-Fuzzy Inference System (ANFIS), convolutional neural network (CNN), logistic regression, and K-nearest neighbors (KNN) for effective classification of gait speed and terrain inclination. Various wearable sensor data, such as FSR and accelerometers, were employed to develop robust models for prosthetic control. The SBLSTM model, which utilizes time-series data through Bi-Directional LSTM layers, demonstrated impressive performance with an accuracy of 96.3 %, precision of 96.4 %, recall of 96.5 %, and an F1-score of 96.4 %. In contrast, the ANFIS model, combining gradient-based learning and least squares estimation, showed reasonable predictive capabilities with root mean square error (RMSE) values of 0.12 for speed and 0.14 for inclination. The accuracy of CNN, logistic regression, and KNN was reported to be 60 %, 31 %, and 93 %, respectively. Comparing the other models in terms of computation, the mean inference time for SBLSTM was found to be 25 ms, which proved to balance speed and accuracy better than other models. Furthermore, the SBLSTM model is particularly suited for time-dependent data, making it more appropriate for real-time prosthetic control. The results highlight that using advanced machine learning algorithms and wearable sensor data has great potential to increase the responsiveness and adaptability of lower-limb prosthetic systems. Ultimately, the goal of this work is for prosthetic users to benefit in terms of quality of life-related to improved mobility and adaptability across a range of environmental conditions.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101865"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628498","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":"Selected channel based multiclass emotion classification from wearable human brain EEG signal","authors":"Khushboo Singh,&nbsp;Mitul Kumar Ahirwal,&nbsp;Manish Pandey","doi":"10.1016/j.measen.2025.101874","DOIUrl":"10.1016/j.measen.2025.101874","url":null,"abstract":"<div><div>Emotion recognition is a crucial issue in human-computer interaction, and EEG (electroencephalography) plays a significant role in deciphering human emotions based on physiological data. However, the complex and non-stationary nature of EEG signals, coupled with redundant information from multi-channel recordings, poses challenges in accurate emotion classification. To address this, we propose a hybrid 1DCNN-Bi-LSTM model that integrates spatial feature extraction (1DCNN) with temporal dependency learning (Bi-LSTM), enhancing the robustness of emotion classification. Furthermore, we present a channel selection mechanism to find the most pertinent EEG channels for emotion recognition, hence lowering computing complexity without compromising accuracy. With the chosen-channel model (8 channels) attaining 85.16 % accuracy, a notable improvement over standard full-channel approaches, experimental results on the DEAP dataset show that the suggested methodology provides significant performance gains. This work fits wearable devices and real-time affective computing systems since it offers a scalable and effective method for EEG-based emotion recognition.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101874"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739892","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":"Quantitation of total soil carbon (TSC) using an electrochemical impedance probe","authors":"Anirban Paul ,&nbsp;Mohammed A. Eldeeb ,&nbsp;Vikram N. Dhamu ,&nbsp;Aniruddh Sharma ,&nbsp;Shabbir Mufazzal Bohri ,&nbsp;Sriram Muthukumar ,&nbsp;Shalini Prasad","doi":"10.1016/j.measen.2025.101875","DOIUrl":"10.1016/j.measen.2025.101875","url":null,"abstract":"<div><div>Soil is an essential element of Earth's ecosystem that helps regulate the nitrogen and carbon cycles while providing an adequate environment to promote plant growth. Soil carbon is one of the key elements present in soil which provides valuable information on soil health. Total soil carbon (TSC) is a combined constituent of organic and inorganic sources of carbon, and it is important to further enhance our understanding of carbon sequestration in soil. An electrochemical sensor, using a three-electrode platform, modified by EMIM[TF₂N]-calixarene-chitosan composite was used to develop a proof of concept to track total soil carbon in-situ without sample pretreatment. Computational chemistry and FTIR spectroscopy were utilized to understand the interaction chemistry between TSC and transducing elements. Based on the interaction results obtained, the sensor was calibrated in three different soil textures; sandy loam, loamy clay, and clay loam. Electrochemical impedance spectroscopy (EIS) technique was used to measure TSC across the range of 0.01 %–4 %. The dose dependent response showed excellent repeatability for all three soil types. This is a novel proof of concept for building a consolidated total soil carbon in-situ sensor, which was further field tested using standard validation principle, to obtain its real field capability.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101875"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785788","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":"Distributed data acquisition optimization algorithm for wireless sensor networks","authors":"Youxian Zhang ,&nbsp;Zhen Nie ,&nbsp;Hongxu Zhang","doi":"10.1016/j.measen.2025.101883","DOIUrl":"10.1016/j.measen.2025.101883","url":null,"abstract":"<div><div>With the rapid development of applications such as the Internet of Things and intelligent transportation, wireless sensor networks play an important role in data collection and environmental monitoring. However, wireless sensor networks face low efficiency and high energy consumption in distributed data collection and node configuration. In this context, a sensor node configuration optimization algorithm based on an improved sparrow search algorithm by introducing reverse elite selection, dynamic perturbation, and dynamic warning update strategies is proposed. Secondly, a virtual grid partitioning strategy is designed, and a distributed data collection and transmission optimization algorithm is proposed. The node configuration algorithm achieved the most uniform distribution of nodes in simulation testing and almost achieved complete region coverage. Under 30 % node failure, its network coverage rate was 83.5 %. When the packet size was 1000 kb, the data transmission rate and average communication delay of the data collection algorithm were 4.2 Mbps and 42 ms, respectively. Compared with existing algorithms, the proposed scheme performs well in coverage retention, energy consumption reduction, and fault recovery capability, and can meet the efficient and reliable distributed data collection needs of wireless sensor networks in complex environments.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101883"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced measuring techniques for tidal turbine blades during structural testing","authors":"Tenis Ranjan Munaweera Thanthirige ,&nbsp;Michael Flanagan ,&nbsp;Ciaran Kennedy ,&nbsp;Yadong Jiang ,&nbsp;Clement Courade ,&nbsp;Patrick Cronin ,&nbsp;Tomas Flanagan ,&nbsp;Jamie Goggins ,&nbsp;William Finnegan","doi":"10.1016/j.measen.2025.101879","DOIUrl":"10.1016/j.measen.2025.101879","url":null,"abstract":"<div><div>Tidal energy is one of the most predictable and reliable renewable energy sources, capable of generating significant amounts of electricity in the coming decades. Scientists, researchers, and technology developers are working tirelessly to propel the industry forward, employing next-generation innovative design strategies to achieve future milestones in affordable and sustainable power generation. In this context, the validation of novel tidal turbine systems offers significant advantages to developers, enabling them to deploy the devices at various tidal potential sites worldwide with confidence that their designs will be able to withstand loading conditions during operation. A method used to help achieve this is to conduct a structural testing program of their tidal turbine blades, in accordance with DNV-ST-0164 and IEC DTS 62600-3 standards. Within this scope, developers are demanding accelerated, efficient, and reliable testing programs to de-risk innovative designs while traditional instrumentation methods have considerable disadvantages. Therefore, this study addresses these challenges by investigating the use of modern advanced measurement tools and offering recommendations to enhance the structural testing process of tidal turbine blades, aiming to improve testing effectiveness and deliver high-quality results within a shorter time frame. Within this study, laser scanning vibrometer, digital image correlation systems, infrared thermal imaging camera, fibre Bragg grating sensors, and laser displacement measuring sensors were employed, in parallel with the traditionally used sensors, to assess the structural testing program of a helical shape tidal turbine foil and studied the results. This study yields promising outcomes, highlighting the potential use of advanced measurement techniques to enhance the structural testing paradigm for tidal turbine blades for future accelerated testing programs. More importantly, it supports the developers in de-risking their technologies, while establishing a new knowledge base for the effective use of modern measurement tools, ultimately contributing to the reduction of the levelised cost of tidal stream energy devices.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101879"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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":"2025-06-01","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":"State-of-the-art review on fall prediction among older Adults: Exploring edge devices as a promising approach for the future","authors":"Md Maruf,&nbsp;Md Mahbubul Haque,&nbsp;Md Mehedi Hasan,&nbsp;Muqit Farhan,&nbsp;Ariful Islam","doi":"10.1016/j.measen.2025.101878","DOIUrl":"10.1016/j.measen.2025.101878","url":null,"abstract":"<div><div>Falling is one of the most serious threats to the health and well-being of older people, resulting in their daily activities and standard of living. In addition, the cost of treating fall-related injuries is substantial, and some patients face incomplete recovery. Current fall prediction methods focus mainly on biological factors such as locomotion, vision, and cognition, often overlooking the multifaceted nature of falls. This paper comprehensively reviewed state-of-the-art fall prediction systems and listed different factors directly associated with falls. We analyzed the current trends and extracted that machine learning, deep learning, sensors, and gait-based fall prediction methods are some of the most prevalent technologies. This paper also identifies the challenges of current fall prediction and prevention systems. It visualizes a road map for future systems that can be integrated into daily life and greatly improve telehealth monitoring and assessment. TinyML-based intelligent wearable technologies have significant potential to predict complex physiological phenomena such as falls. This study highlights the importance of leveraging TinyML-powered smart wearables to aid fall prevention in the geriatric population. By advancing the understanding of existing systems, this research aims to enhance the quality of life for older adults and guide future innovations in the field.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101878"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing acute leukemia classification through hybrid fuzzy C means and random forest methods","authors":"K. Lakshmi Narayanan ,&nbsp;R. Santhana Krishnan ,&nbsp;Y. Harold Robinson ,&nbsp;S. Vimal ,&nbsp;Tarik A. Rashid ,&nbsp;Chetna Kausha ,&nbsp;Md. Mehedi Hassan","doi":"10.1016/j.measen.2025.101876","DOIUrl":"10.1016/j.measen.2025.101876","url":null,"abstract":"<div><div>Leukemia is a category of cancer that is normally found in blood and bone marrow, and which causes rapid abnormal development in the making of white blood cells than the required amount. The produced white blood cells could be ineffective to fight against harmful infections and can even prejudice or restrict the capability of the bone marrow to generate red blood cells and blood platelets. If this is not diagnosed in the earlier stage, it may start to affect the function of the internal organs and cause death. Normally, entire blood counts image analysis and diagnosis are done manually which is an inaccurate and time-intensive process. In this proposed method the classification is tested with two Machine Learning algorithms which are Hybrid Fuzzy C Means (FCM) and Random Forest algorithm (RF) and Support Vector Machine for the detection and classification of Acute Leukemia disease and their performance was evaluated. The dataset comprised of 8637 images which included infected images, normal images and augmented images from different dataset providers and RGB to CMYK conversion with histogram equalization is applied for pre-processing, K means for Image Segmentation. Experimental results convey that Hybrid FCM and RF Algorithm attained an accuracy of 99.06 %, a sensitivity of 99.4 %, and a specificity of 97.8 % respectively, and the ROC (Receiver Operating Characteristic) curve shows that the result produced by the Hybrid FCM &amp; RF based Classifier is best suitable in diagnosing the classification of the Acute Leukemia disease. The tool used for developing the proposed method was Matlab R2018 software.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101876"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799514","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":"Entropy based earlier detection and mitigation of DDOS attack using stochastic method in SDN_IOT","authors":"I. Varalakshmi,&nbsp;M. Thenmozhi","doi":"10.1016/j.measen.2025.101873","DOIUrl":"10.1016/j.measen.2025.101873","url":null,"abstract":"<div><div>Software-defined networking (SDN) is characterized by the separation of control plane as well as data plane in the network. Data packets are forwarded by the data plane, while routing decisions are made by the control plane. This separation of concerns allows for greater flexibility and programmability in the network. It is a promising technology that can allow IoT networks to perform better, be more secure, and be more manageable. However, there are some challenges that need to be addressed before SDN can be widely adopted in IoT environments. The requests can be made from a variety of sources, including compromised computers, botnets, and even legitimate users who have been tricked into visiting a malicious website. Detecting and mitigating DDoS attacks at an early stage is the goal of a stochastic method based on Entropy that prevents failure of SDN controller. The proposed algorithm Entropy based DDoS Detection algorithm (EDDA) detects the attack by analyzing entropy fluctuations in incoming data packets, thereby preserving the integrity of sensor-generated data and dynamically configure rate-limiting mechanisms on network devices to restrict the rate at which packets can be transmitted. With our proposed method, DDoS attacks like TCP, UDP, and ICMP SYN Flood can be detected with high accuracy, using less computing power. As a result of the proposed solution, DDoS attacks are detected and mitigated using SDN-based techniques under 70 hosts connected within 9 switches with a high degree of detection accuracy and significantly low detection time. By integrating entropy as a measurement parameter, the proposed system effectively distinguishes between legitimate and malicious network flows, ensuring stable and secure data transmission in sensor-driven IoT networks.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101873"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved travel calculation method for high-speed carriers penetrating objects","authors":"Xingcheng Wei ,&nbsp;Tao Zeng ,&nbsp;Li Sui ,&nbsp;Gengchen Shi","doi":"10.1016/j.measen.2025.101966","DOIUrl":"10.1016/j.measen.2025.101966","url":null,"abstract":"<div><div>To improve the accuracy of travel calculations during the penetration of objects by high-speed carriers, this paper proposes a method for processing acceleration data during the penetration process. Through simulation, penetration acceleration data were obtained, and Fourier Transform was employed to analyze the time-frequency characteristics of these data. Given that penetration acceleration signals are often contaminated by high-frequency oscillatory noise, a novel signal processing approach is introduced. Using a controlled variable method in simulation, the frequency boundary between high-frequency noise and useful signals was identified. After applying a low-pass filter with a cutoff frequency of 10 kHz, the simulation results indicated that the processed signal provides sufficiently accurate travel information. On the hardware side, a specialized signal conditioning circuit was designed for the acceleration sensor signals, and its effectiveness was verified through Machette hammer experiments. Additionally, semi-physical simulation tests further confirmed the accuracy of the proposed travel calculation method. The results demonstrate that the improved method presented in this paper can significantly enhance the precision of travel calculations during the penetration of objects by high-speed carriers.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101966"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}