IEEE Journal of Selected Areas in Sensors最新文献_第2页

SrcSense: Robust WiFi-Based Motion Source Recognition via Signal-Informed Deep Learning SrcSense：基于信号的深度学习的基于wifi的鲁棒运动源识别

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-12-16 DOI: 10.1109/JSAS.2024.3517514

Guozhen Zhu;Beibei Wang;Weihang Gao;Yuqian Hu;Chenshu Wu;K. J. Ray Liu

{"title":"SrcSense: Robust WiFi-Based Motion Source Recognition via Signal-Informed Deep Learning","authors":"Guozhen Zhu;Beibei Wang;Weihang Gao;Yuqian Hu;Chenshu Wu;K. J. Ray Liu","doi":"10.1109/JSAS.2024.3517514","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3517514","url":null,"abstract":"As WiFi has become a ubiquitous medium for communication, its role in sensing applications has expanded. However, the current WiFi sensing applications are limited by their assumption that any detected motion signifies human activity, overlooking the potential impact of nonhuman subjects. Existing attempts to recognize the interference from nonhuman motion impose stringent requirements regarding device positioning, data quality, environmental complexity, and nonhuman subject categories. In this study, we design a robust deep learning framework, SrcSense (“<bold>S</b>ou<bold>rc</b>e <bold>Sense</b>”), to recognize the motion source with WiFi signals through the wall. SrcSense extracts environment-independent features from single-link WiFi. We investigate the performance of popular deep neural networks and explore the efficacy of transferring pretrained models to WiFi sensing tasks. We implement SrcSense and evaluate the performance in five real-world complex environments with commodity WiFi devices. With a challenging dataset considering large pets, diverse human activities and multiple subjects coexisting cases, SrcSense achieves an average validation accuracy of 95.84% across five distinct environments and an average testing accuracy of 91.71% in unseen environments without further model training or parameter tuning. By accumulating 20 s of WiFi data, SrcSense can achieve an elevated recognition accuracy of 99.77% with ResNet-50. These results underline the robustness of our approach and its readiness for integration into ubiquitous intelligent Internet of Things (IoT) systems and applications.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"2 ","pages":"40-53"},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10803907","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975940","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}

引用次数: 0

A Comprehensive Planning Framework for Connected Elevated LiDAR Sensors 互联高架激光雷达传感器的综合规划框架

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-11-25 DOI: 10.1109/JSAS.2024.3506478

Nawfal Guefrachi;Michael C. Lucic;Mohammad Yassen;Hakim Ghazzai;Ahmad Alsharoa

引用次数: 0

Assessing the Impact of Distractions Using a Virtual-Reality-Based GO/NOGO Task 使用基于虚拟现实的GO/NOGO任务评估分心的影响

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-11-25 DOI: 10.1109/JSAS.2024.3506476

Chun-Chuan Chen;Yan-Qing Chen;Tzu-Yun Yeh;Chia-Ru Chung;Shih-Ching Yeh;Eric Hsiao-Kuang Wu

{"title":"Assessing the Impact of Distractions Using a Virtual-Reality-Based GO/NOGO Task","authors":"Chun-Chuan Chen;Yan-Qing Chen;Tzu-Yun Yeh;Chia-Ru Chung;Shih-Ching Yeh;Eric Hsiao-Kuang Wu","doi":"10.1109/JSAS.2024.3506476","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3506476","url":null,"abstract":"The GO/NOGO task provides an objective assessment of a subject's attention and response inhibition and is typically given to subjects without any unexpected distractions. Studying the impact of distractions is important from the therapeutic viewpoint as distractions may occur during exposure therapy and degrade treatment efficacy. In this study, we utilized a virtual classroom integrated with electroencephalogram (EEG) for a GO/NOGO task with multimode environmental distractions to study the impact of distractions on behavioral and neuronal activities. Thirty healthy male adults were recruited. Statistical analysis and machine learning methods were employed to analyze the behavioral and neuronal data. The results demonstrated no significant behavioral differences between conditions with and without distractions. However, the impacts of distractions manifested in the enhancement of frequency-specific power, including theta, alpha, and gamma oscillations in GO trials, as well as beta power and the N200 peak in NOGO trials, highlighting their role in attention regulation and response inhibition. Finally, machine learning result analysis identified significant differences between conditions with and without distractions using EEG features, achieving an accuracy rate of 98.3%. In conclusion, we found that introducing distractions into a GO/NOGO task provides a deeper understanding of the neuronal correlates of distractions, and these findings can inform the development of therapeutic strategies for attention-related disorders.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"2 ","pages":"21-27"},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10767192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875144","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}

引用次数: 0

A Compact Home-Based Training System for Preventing Frailty Using a Mapping Model and Cross-Dataset Transfer Learning 基于映射模型和跨数据集迁移学习的家庭预防脆弱性训练系统

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-11-19 DOI: 10.1109/JSAS.2024.3502001

Lizheng Liu;Hsuan Hu;Shih-Ching Yeh;Eric Hsiao-Kuang Wu;Chun-Chuan Chen

{"title":"A Compact Home-Based Training System for Preventing Frailty Using a Mapping Model and Cross-Dataset Transfer Learning","authors":"Lizheng Liu;Hsuan Hu;Shih-Ching Yeh;Eric Hsiao-Kuang Wu;Chun-Chuan Chen","doi":"10.1109/JSAS.2024.3502001","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3502001","url":null,"abstract":"Frailty is becoming a more serious issue as the population ages. Numerous studies have shown that exercise can effectively slow the development of frailty. Compared with vigorous exercise, Baduanjin (BDJ), a kind of traditional Chinese Qigong with eight simple movements, is more suitable for frailty patients. BDJ has been used to train frailty patients by physical therapists. To provide an enhanced training method, we designed a lightweight family-based frailty training system via a virtual BDJ coach. To achieve a compact system, we use a webcam as the main device. The system also supports the Kinect framework. We use pose estimation and motion recognition methods to analyze the user's movements. In addition, a novel transfer learning method is proposed. We designed a mapping model called “Skeleton Mapnet” to convert skeletal data from different frameworks. This method enables datasets from different frameworks to share classification models. It can also mix skeletal data from different frameworks to solve the lack of webcam datasets. Such a design allows the system to be easily ported into other platforms. In addition, the system is also suitable for the use of the artificial intelligence of things. Our design ensures that frailty patients can easily learn and operate the system.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"2 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10757399","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859185","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}

引用次数: 0

Smart Pressure E-Mat for Human Sleeping Posture and Dynamic Activity Recognition 用于人体睡眠姿势和动态活动识别的智能压力电子垫

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-11-18 DOI: 10.1109/JSAS.2024.3501213

Liangqi Yuan;Yuan Wei;Jia Li

{"title":"Smart Pressure E-Mat for Human Sleeping Posture and Dynamic Activity Recognition","authors":"Liangqi Yuan;Yuan Wei;Jia Li","doi":"10.1109/JSAS.2024.3501213","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3501213","url":null,"abstract":"With the emphasis on healthcare, early childhood education, and fitness, noninvasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied because of its advantages of simple structure, easy access, visualization application, and harmlessness. This article introduces a Smart Pressure e-Mat (SPeM) system based on piezoresistive material, Velostat, for human monitoring applications, including recognition of sleeping postures, sports, and yoga. After a subsystem scans the e-mat readings and processes the signal, it generates a pressure image stream. Deep neural networks are used to fit and train the pressure image stream and recognize the corresponding human behavior. Four sleeping postures and 13 dynamic activities inspired by Nintendo Switch Ring Fit Adventure are used as a preliminary validation of the proposed SPeM system. The SPeM system achieves high accuracies in both applications, demonstrating the high accuracy and generalizability of the models. Compared with other pressure sensor-based systems, SPeM possesses more flexible applications and commercial application prospects, with reliable, robust, and repeatable properties.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"2 ","pages":"9-20"},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10756666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859235","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}

引用次数: 0

Terahertz Metasurface With High-Q Fano Resonance for Bio-Sensing 用于生物传感的具有高 Q 值法诺共振的太赫兹元表面

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-10-29 DOI: 10.1109/JSAS.2024.3487487

Linda Shao;Zhihang Wang;Ning Mu;Tunan Chen;Weiren Zhu

{"title":"Terahertz Metasurface With High-Q Fano Resonance for Bio-Sensing","authors":"Linda Shao;Zhihang Wang;Ning Mu;Tunan Chen;Weiren Zhu","doi":"10.1109/JSAS.2024.3487487","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3487487","url":null,"abstract":"High-quality factor Fano resonances offer exceptional potential for the creation of ultrasensitive refractive index sensors owing to their capacity to facilitate robust interactions between electromagnetic waves and analytes. In this article, we introduce a general approach for designing sensitive metasurface sensors leveraging high-Q Fano resonances. The metasurface, composed of metallic strips varying in length, produces the characteristic Fano line shape through the interference of bright and dark modes. Our findings reveal a remarkable sensitivity of up to 0.473 THz/RIU at 2.37 THz, with a maximum resonance Q value attainment of 38.12. The tunable properties of Fano resonances can be fine-tuned by adjusting geometric parameters. As a demonstration of the practical applicability of these high-Q resonances, we conducted experimental assessments of the metasurface sensor's performance in detecting the concentrations of bovine serum albumin and glucose. Notably, both the resonance frequency and amplitude undergo significant changes in response to increasing analyte concentrations. This allows for rapid and precise determination of both the concentration and molecule type based on observed frequency shifts. Our strategy paves the way for the design of ultrasensitive real-time sensors operating in the terahertz regime.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"1 ","pages":"272-279"},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737402","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736235","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}

引用次数: 0

A Dynamic Bayesian Multichannel Fusion Scheme for Heart Rate Monitoring With Ballistocardiograph Signals in Free-Living Environments 自由生活环境中使用球心动图信号进行心率监测的动态贝叶斯多通道融合方案

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-10-23 DOI: 10.1109/JSAS.2024.3485544

Jun Qi;Ruilin Cai;Qing Liu;Wei Wang;Jieming Ma;Jianjun Chen

{"title":"A Dynamic Bayesian Multichannel Fusion Scheme for Heart Rate Monitoring With Ballistocardiograph Signals in Free-Living Environments","authors":"Jun Qi;Ruilin Cai;Qing Liu;Wei Wang;Jieming Ma;Jianjun Chen","doi":"10.1109/JSAS.2024.3485544","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3485544","url":null,"abstract":"Ballistocardiograph (BCG) stands out as a noncontact technology for heart monitoring, offering a wealth of cardiovascular parameter information. Its applications have overshadowed traditional electrocardiogram particularly for free-living environment, such as home monitoring, in recent years. However, challenges arise from the susceptibility of BCG signals to positional variations, bodily movements, and systemic noise, posing formidable obstacles for detection algorithms. In this article, we propose a novel interbeat interval detection approach with the dynamic Bayesian network for multichannel fusion, in terms of five unique indicators for the precise localization of cardiac activity from extracted features. We also introduce a peak detection method to locate the positions of all HIJK complex within BCG segment and evaluate the generalization of the proposed method in the simulated environment of different noise generation. The results from the dataset comprising 36 healthy subjects and four cardiovascular disease patients show that the proposed method exhibits average coverage rate up to 96.15%; the mean square error is 0.04 compared with single-channel measures, which suggest the potential of our method in assisting the long-term heartbeat monitoring in free-living environments.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"1 ","pages":"261-271"},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10729850","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713978","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}

引用次数: 0

SCL-Fall: Reliable Fall Detection Using mmWave Radar With Supervised Contrastive Learning SCL-Fall：利用毫米波雷达和监督对比学习进行可靠的跌倒检测

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-10-15 DOI: 10.1109/JSAS.2024.3481205

Wenxuan Li;Dongheng Zhang;Yadong Li;Ruiyuan Song;Yang Hu;Qibin Sun;Yan Chen

{"title":"SCL-Fall: Reliable Fall Detection Using mmWave Radar With Supervised Contrastive Learning","authors":"Wenxuan Li;Dongheng Zhang;Yadong Li;Ruiyuan Song;Yang Hu;Qibin Sun;Yan Chen","doi":"10.1109/JSAS.2024.3481205","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3481205","url":null,"abstract":"Fall is a severe health threat for elders' health care. While existing systems could achieve promising performance under specific scenarios, the required computing resources are usually not affordable, which is not applicable for real-time detection. In this article, we propose SCL-Fall, a real-time fall detection system using millimeter wave signal with supervised contrastive learning, which can achieve impressive accuracy with low computation complexity. Specifically, we first extract the signal variation corresponding to human activity with spatial–temporal processing. We incorporate reweighting and denoising techniques in the signal processing process. To enhance the system performance and robustness, we perform data augmentation by shifting, flipping, extracting, and interpolating the signal. Finally, we design a lightweight convolutional neural network to achieve real-time fall detection. Extensive experimental results demonstrate that the proposed system could achieve state-of-the-art performance with limited computation complexity.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"1 ","pages":"237-248"},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716775","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600359","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}

引用次数: 0

Adjusting Detectable Velocity Range in FMCW Radar Systems Through Selective Sampling 通过选择性采样调整 FMCW 雷达系统的可探测速度范围

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-10-14 DOI: 10.1109/JSAS.2024.3479110

Seungheon Kwak;Dahyun Jeon;Seongwook Lee

{"title":"Adjusting Detectable Velocity Range in FMCW Radar Systems Through Selective Sampling","authors":"Seungheon Kwak;Dahyun Jeon;Seongwook Lee","doi":"10.1109/JSAS.2024.3479110","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3479110","url":null,"abstract":"In a frequency-modulated continuous wave (FMCW) radar system, a series of waveforms with frequencies that increase linearly over time is transmitted. Once the transmitted signal reaches the target and returns, sampling is applied to the received signal, followed by the Fourier transform for distance and velocity estimation. In general, the detectable velocity range depends on the duration of a single waveform in the FMCW radar systems. If the target moves at a velocity that exceeds the detectable velocity of the radar, accurate velocity estimation is impossible due to Doppler ambiguity. Therefore, in this article, we propose a method for adjusting the detectable velocity range using a selective sampling method. In the proposed method, velocity ambiguity can be resolved by dual processing the samples obtained along the time axis at different rates. When the proposed method is applied to targets beyond the detectable velocity range of a conventional FMCW radar system, it effectively resolves Doppler ambiguity, enabling efficient velocity estimation. Our method has been verified to be well-applicable to data obtained from both simulation and real-world measurements. The comparison of the estimated velocity using our method with the ground truth in real-world measurements indicates an error of 0.07 m/s. We expect our proposed method to contribute to resolving the issue of velocity estimation ambiguity in the FMCW radar systems.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"1 ","pages":"249-260"},"PeriodicalIF":0.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10715571","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636298","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}

引用次数: 0

Novel Human-Posture Recognition System Based on Advanced Graph Convolutional Network Using Skeletal Data 基于先进图卷积网络、使用骨骼数据的新型人体姿态识别系统

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-10-07 DOI: 10.1109/JSAS.2024.3475355

Guannan Liu;Rende Xie;Shih-Hau Fang;Hsiao-Chun Wu;Kun Yan

{"title":"Novel Human-Posture Recognition System Based on Advanced Graph Convolutional Network Using Skeletal Data","authors":"Guannan Liu;Rende Xie;Shih-Hau Fang;Hsiao-Chun Wu;Kun Yan","doi":"10.1109/JSAS.2024.3475355","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3475355","url":null,"abstract":"Automatic human-posture or human-activity recognition is a very important research problem nowadays. In this work, we propose a novel human-posture recognition approach using the 3-D skeletal data acquired by the Kinect V2 sensor. The acquired skeletal data are first segmented using our recently proposed automatic-segmentation technique and each segment can be labeled with a particular kind of human-posture. We propose four different types of node feature matrices extracted from the segmented skeletal data, which can serve as the input features to the advanced graph convolutional network for multiclassification. The realworld experimental results demonstrate that our proposed novel human-posture recognition system can reach a very high average classification-accuracy of 91.56%. In addition, the ablation study of the effect of skeletal-graph variations on the recognition performance is also presented. The average classification-accuracy further reaches up to 92.33% when four confusing joint-nodes are removed from the skeletal graph. Our proposed novel human-posture recognition approach can be very useful for practical applications, such as human-computer interface, intelligent healthcare, robotics, etc.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"1 ","pages":"224-236"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706704","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565489","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}

引用次数: 0