IEEE Sensors Letters最新文献_第6页

Drone Inspection System Based on the Electrochemical Impedance Detector by Dengue NS1 Biomarkers in Water Environments 基于电化学阻抗检测器的无人机水环境登革热 NS1 生物标记检测系统

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-26 DOI: 10.1109/LSENS.2024.3449342

Sung-Lin Tsai;Jiunn-Jye Wey;Szu-Chia Lai;You-Qian Lin;Chiao-Jou Chang;Pao-Cheng Huang

{"title":"Drone Inspection System Based on the Electrochemical Impedance Detector by Dengue NS1 Biomarkers in Water Environments","authors":"Sung-Lin Tsai;Jiunn-Jye Wey;Szu-Chia Lai;You-Qian Lin;Chiao-Jou Chang;Pao-Cheng Huang","doi":"10.1109/LSENS.2024.3449342","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3449342","url":null,"abstract":"Dengue viruses are currently one of the deadliest mosquito-borne infectious diseases; there is no effective treatment, and a vaccine for dengue fever is not yet available. Therefore, monitoring and preventing virus transmission is currently the most effective controlling method. This study focuses on environmental traces of dengue transmission, and a low-cost portable drone system for dengue virus inspection based on water sources is presented, which is equipped with a drone, a water collector, a microfluidic chip, and an electrochemical impedance converter using an Arduino development broad and an Analog Devices AD5934 chip. Water samples are carried back by a drone with a water collector, which can be measured and analyzed outdoors, that is not required to be brought back to the laboratory. The concentration of 10- and 20-μg/cc dengue nonstructural protein 1 can be identified by impedance magnitude in the microfluidic chip using the bead-based biomarker technology. The presented novel device using a drone-based collector with a low-cost electrochemical impedance sensor may have great potential for the creation of dengue maps, becoming a valuable technique that is beneficial to trace dengue transmission. In the future, it may quickly identify differences in impedance spectroscopy between numerous viruses for environmental investigation.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137590","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}

引用次数: 0

Signal Quality-Aware Frequency Demodulation-Based ECG-Derived Respiration Rate Estimation Method With Reduced False Alarms 降低误报的基于信号质量的频率解调心电图呼吸频率估计方法

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-26 DOI: 10.1109/LSENS.2024.3449328

Aditya Nalwaya;M. Sabarimalai Manikandan;Ram Bilas Pachori

{"title":"Signal Quality-Aware Frequency Demodulation-Based ECG-Derived Respiration Rate Estimation Method With Reduced False Alarms","authors":"Aditya Nalwaya;M. Sabarimalai Manikandan;Ram Bilas Pachori","doi":"10.1109/LSENS.2024.3449328","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3449328","url":null,"abstract":"In this letter, we present an automated signal quality-aware frequency demodulation (FD)-based electrocardiogram (ECG)-derived respiration rate (FD-ECG-derived RR) estimation method with reduced false alarms under noisy ECG signals, which are unavoidable in resting and ambulatory health monitoring applications. The proposed FD-ECG-derived RR estimation method includes three major steps of signal quality checking to discard noisy ECG signals, respiratory-induced frequency variation (RIFV) waveform extraction using a frequency demodulation envelope detector by determining peaks of the derivative ECG waveform using a simple R-peak detector, and respiration rate estimation using the Fourier magnitude spectrum of the extracted RIFV waveform. On the standard Capnobase and BIDMC databases, the proposed FD-ECG-derived RR estimation method provides promising results with mean absolute error values of 5.01 and 5.37 breaths/min, respectively. The signal quality-aware RR estimation method used can reduce false alarm rate of 84.85\u0000<inline-formula><tex-math>${%}$</tex-math></inline-formula>\u0000 by discarding noisy ECG signals with quality assessment accuracy of 85.25\u0000<inline-formula><tex-math>${%}$</tex-math></inline-formula>\u0000. The proposed simplistic method having lightweight signal processing approaches makes it suitable for real-time health monitoring applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143629","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}

引用次数: 0

ALS Detection Framework Based on Automatic Singular Spectrum Analysis and Quantum Convolutional Neural Network From EMG Signals 基于自动奇异谱分析和量子卷积神经网络的肌电图信号 ALS 检测框架

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-26 DOI: 10.1109/LSENS.2024.3449369

Kiran Kumar Makam;Vivek Kumar Singh;Ram Bilas Pachori

引用次数: 0

Inferior Myocardial Infarction Detection From Lead II of ECG: A Gramian Angular Field-Based 2D-CNN Approach 从心电图第 II 导联检测下心肌梗死：基于革兰氏角场的 2D-CNN 方法

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-26 DOI: 10.1109/LSENS.2024.3450176

Asim Yousuf;Rehan Hafiz;Saqib Riaz;Muhammad Farooq;Kashif Riaz;Muhammad Mahboob Ur Rahman

{"title":"Inferior Myocardial Infarction Detection From Lead II of ECG: A Gramian Angular Field-Based 2D-CNN Approach","authors":"Asim Yousuf;Rehan Hafiz;Saqib Riaz;Muhammad Farooq;Kashif Riaz;Muhammad Mahboob Ur Rahman","doi":"10.1109/LSENS.2024.3450176","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3450176","url":null,"abstract":"This letter presents a novel method for inferior myocardial infarction (MI) detection using lead II of electrocardiogram (ECG). We evaluate our proposed method on a public dataset, namely, Physikalisch Technische Bundesanstalt (PTB) ECG dataset from PhysioNet. Under our proposed method, we first clean the noisy ECG signals using db4 wavelet, followed by an R-peak detection algorithm to segment the ECG signals into beats. We then translate the ECG timeseries dataset to an equivalent dataset of grayscale images using Gramian angular summation field (GASF) and Gramian angular difference field (GADF) operations. Subsequently, the grayscale images are fed into a custom 2-D convolutional neural network (CNN), which efficiently differentiates between a healthy subject and a subject with MI. Our proposed approach achieves an average classification accuracy of 99.68%, 99.80%, 99.82%, and 99.84% under GASF dataset with noise and baseline wander, GADF dataset with noise and baseline wander, GASF dataset with noise and baseline wander removed, and GADF dataset with noise and baseline wander removed, respectively. Most importantly, this work opens the floor for innovation in wearable devices to measure lead II ECG (e.g., by a smart watch worn on right wrist, along with a smart patch on left leg), in order to do accurate, real-time, and early detection of inferior wall MI.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160069","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}

引用次数: 0

Measurement Offset Fault Detection Logic for PMSM Position Sensor PMSM 位置传感器的测量偏移故障检测逻辑

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-22 DOI: 10.1109/LSENS.2024.3447897

Hafiz Ahmed

引用次数: 0

IEEE Sensors Letters Subject Categories for Article Numbering Information 用于文章编号信息的 IEEE 传感器快报主题类别

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-21 DOI: 10.1109/LSENS.2024.3442595

引用次数: 0

IEEE Sensors Letters Publication Information IEEE 传感器快报》出版信息

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-21 DOI: 10.1109/LSENS.2024.3442591

引用次数: 0

Intervention of Machine Learning and Explainable Artificial Intelligence in Fiber-Optic Sensor Device Data for Systematic and Comprehensive Performance Optimization 将机器学习和可解释人工智能介入光纤传感器设备数据，实现系统性和综合性能优化

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-21 DOI: 10.1109/LSENS.2024.3445324

Jatin Rana;Anuj K. Sharma;Yogendra Kumar Prajapati

{"title":"Intervention of Machine Learning and Explainable Artificial Intelligence in Fiber-Optic Sensor Device Data for Systematic and Comprehensive Performance Optimization","authors":"Jatin Rana;Anuj K. Sharma;Yogendra Kumar Prajapati","doi":"10.1109/LSENS.2024.3445324","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3445324","url":null,"abstract":"This letter illustrates the successful application of machine learning (ML) models with explainable artificial intelligence (XAI) to enhance the efficacy of a surface plasmon resonance (SPR)-based fiber-optic sensor device (FOSD). The investigation also examines the correlation between the sensor's figure of merit (FoM) and the following variables: light wavelength (λ), sensing region length, metal layer thickness, and refractive index (RI) of surrounding (i.e., sensing or analyte) medium. The study established that the FoM datasets were consistent with various boosting algorithms, such as XGBoost, CatBoost, etc. Incorporating these algorithms into datasets with a λ-resolution of 1 nm led to enhanced FoM magnitudes. The dataset comprises 32 768 data points, each of which falls within one of 15 distinct thickness values and 25 distinct sensing length values. The selected CatBoost ML model exhibits a high level of consistency with the data in terms of trend matching, with all other evaluation parameters lying within acceptable ranges. Furthermore, we have implemented XAI to gain a more comprehensive understanding of the model's internal mechanism in relation to FoM prediction. The results from the shapley additive explanations (SHAP) method indicate that analyte RI and λ play significantly bigger role in dictating the FoM of the SPR-based FOSD. This study emphasizes that the efficient finalization of sensor design and improved sensing performance can be achieved by selecting an appropriate ML model along with XAI and implementing it on a variety of FOSD datasets.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169731","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}

引用次数: 0

In-Band Sensing and Communication for Optical Access Networks Using Δϕ-OTDR With Simplified Transceivers 使用简化收发器的Δj-OTDR 为光接入网络提供带内传感和通信功能

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-21 DOI: 10.1109/LSENS.2024.3447091

Pallab K. Choudhury;Élie Awwad

{"title":"In-Band Sensing and Communication for Optical Access Networks Using Δϕ-OTDR With Simplified Transceivers","authors":"Pallab K. Choudhury;Élie Awwad","doi":"10.1109/LSENS.2024.3447091","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3447091","url":null,"abstract":"An in-band integration strategy is proposed by inserting a sensing probe signal over communication data by modulating the same wavelength channel for next-generation optical access network targeting wavelength-division multiplexing (WDM) point-to-point links. The integration is done by exploring the dc-balanced property of a Manchester-coded signal allowing an effective reduction of low-frequency components to accommodate an in-band Golay-coded lower frequency signal that acts as a sensing probe. The system is demonstrated for 10-Gb/s downstream data over a 20-km fiber with a simple direct-detection receiver in a mobile- or enterprise-fronthaul-based WDM link. Differential-phase-sensitive optical time-domain reflectometry is used to locate external perturbations by using the Golay-coded signal for channel estimation and a coherent receiver at the central office. The presented results show that the downstream data can be successfully retrieved from the integrated signal within a pre-forward error correction bit error rate limit of 10\u0000−3\u0000 maintaining enough input optical power budget at the receiver side. Moreover, the backscattered signal is analyzed for accurate detection of two simultaneous events applied over the fiber maintaining a sensing spatial resolution of 2.1 m and a maximum acoustic bandwidth of 381 Hz with a strain sensitivity down to 15 nϵ\u0000pp\u0000 (peak to peak).","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160005","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}

引用次数: 0

Discrete Gesture Recognition Using Multimodal PPG, IMU, and Single-Channel EMG Recorded at the Wrist 使用多模态 PPG、IMU 和腕部单通道 EMG 记录离散手势识别

IF 2.2

IEEE Sensors Letters Pub Date : 2024-08-21 DOI: 10.1109/LSENS.2024.3447240

Ethan Eddy;Evan Campbell;Ulysse Côté-Allard;Scott Bateman;Erik Scheme

{"title":"Discrete Gesture Recognition Using Multimodal PPG, IMU, and Single-Channel EMG Recorded at the Wrist","authors":"Ethan Eddy;Evan Campbell;Ulysse Côté-Allard;Scott Bateman;Erik Scheme","doi":"10.1109/LSENS.2024.3447240","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3447240","url":null,"abstract":"Discrete hand-gesture recognition using sensors built into wrist-wearable devices could enable always-available input across a wide range of ubiquitous environments. For example, a user could flick their wrist to dismiss a phone call or tap their thumb and index fingers together to make a selection in mixed reality. To move toward such applications, this work evaluates a new multimodal commercially available device (the \u0000<italic>BioPoint\u0000 by \u0000<italic>SIFI Labs\u0000) for recognizing seven dynamic hand gestures. Three sensors were evaluated, including a single channel of electromyography (EMG), a three-axis accelerometer (ACC), and photoplethysmography (PPG). Using a deep LSTM-based network, the relative performance of each sensor and all possible combinations were compared for their gesture classification abilities. The results show that the combination of all sensors led to the highest classification accuracy (\u0000<inline-formula><tex-math>$>$</tex-math></inline-formula>\u000096%), significantly outperforming the individual performance of each sensor (p \u0000<inline-formula><tex-math>$< $</tex-math></inline-formula>\u0000 0.05). In addition, the fusion of all sensors significantly improved performance across days (p \u0000<inline-formula><tex-math>$< $</tex-math></inline-formula>\u0000 0.05) and was significantly more resilient when classifying gestures elicited in unseen limb positions (p \u0000<inline-formula><tex-math>$< $</tex-math></inline-formula>\u0000 0.05). These results highlight the complementary benefits of fusing EMG, ACC, and PPG signals as a viable path forward for the reliable recognition of discrete event-driven gestures using wrist-based wearables.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123028","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}

引用次数: 0