Pub Date : 2025-03-05DOI: 10.1109/JSEN.2025.3528277
Xiong Cheng;Zhixiang Zhai;Pengfei Zhang;Yiqi Zhou;Rui Wang;Wenhua Gu;Xiaodong Huang;Daying Sun
Presents corrections to the paper, Corrections to “Generating Multiple Distinct Feasible Solutions for MEMS Accelerometers Using Deep Learning”.
{"title":"Corrections to “Generating Multiple Distinct Feasible Solutions for MEMS Accelerometers Using Deep Learning”","authors":"Xiong Cheng;Zhixiang Zhai;Pengfei Zhang;Yiqi Zhou;Rui Wang;Wenhua Gu;Xiaodong Huang;Daying Sun","doi":"10.1109/JSEN.2025.3528277","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3528277","url":null,"abstract":"Presents corrections to the paper, Corrections to “Generating Multiple Distinct Feasible Solutions for MEMS Accelerometers Using Deep Learning”.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 5","pages":"9209-9209"},"PeriodicalIF":4.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912817","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-05DOI: 10.1109/JSEN.2024.3524872
Xin Sui;Bangwen Liao;Changqiang Wang;Zhengxu Shi
Presents corrections to the paper, (Corrections to “Improved XGBoost and GM UWB/MEME IMU Positioning Methods for Non-Line-of-Sight Environments”).
{"title":"Corrections to “Improved XGBoost and GM UWB/MEME IMU Positioning Methods for Non-Line-of-Sight Environments”","authors":"Xin Sui;Bangwen Liao;Changqiang Wang;Zhengxu Shi","doi":"10.1109/JSEN.2024.3524872","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524872","url":null,"abstract":"Presents corrections to the paper, (Corrections to “Improved XGBoost and GM UWB/MEME IMU Positioning Methods for Non-Line-of-Sight Environments”).","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 5","pages":"9208-9208"},"PeriodicalIF":4.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912815","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-05DOI: 10.1109/JSEN.2025.3537212
Michele Magno;Daniela de Venuto;Giuseppe Ferri;Seonyeong Heo
{"title":"Guest Editorial Special Issue on Energy-Efficient Embedded Intelligent Sensor Systems (S1)","authors":"Michele Magno;Daniela de Venuto;Giuseppe Ferri;Seonyeong Heo","doi":"10.1109/JSEN.2025.3537212","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3537212","url":null,"abstract":"","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 5","pages":"7733-7733"},"PeriodicalIF":4.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The operation temperature detection of high-temperature hot-end components in aerospace and other fields is of great significance for operational safety; however, the higher working temperature and noninvasive in situ measurement of temperature detectors currently face significant challenges. Here, the platinum-rhodium (PtRh) conformal resistance temperature detector (CRTD) was proposed, the structure and preparation process was elaborated, and the aging sintering process of the film was explored to form a stable conductive network with high porosity. The dense surface and cross-sectional characteristics of the film were demonstrated, the lattice characteristic of PtRh was demonstrated, and the composition and valence states of the elements were characterized in detail. The high-temperature testing system was built, and the test results showed that the developed PtRh CRTD could achieve dynamic temperature detection at $1200~^{circ }$ C, with a linear fitting goodness of 0.99834. The resistance drift rate of the 31 h high-temperature durability test was 1.11%/h, and the maximum full-scale error was 1.58%FS. Furthermore, the PtRh high-temperature conformal ceramic bolt was prepared and subjected to thermal shock testing. The results showed that it more accurately reflects the temperature of the structural component itself than the discrete surface mount temperature measurement method, providing a feasible solution for in situ detection of complex curved high-temperature hot-end components.
{"title":"Conformal Printed High-Temperature Platinum-Rhodium Resistance Temperature Detector for Ceramic Bolts","authors":"Yuelong Li;Disheng Qiang;Fuxin Zhao;Lida Xu;Chenhe Shao;Yanzhang Fu;Qingtao Yang;Qinnan Chen;Chao Wu;Daoheng Sun","doi":"10.1109/JSEN.2025.3525522","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3525522","url":null,"abstract":"The operation temperature detection of high-temperature hot-end components in aerospace and other fields is of great significance for operational safety; however, the higher working temperature and noninvasive in situ measurement of temperature detectors currently face significant challenges. Here, the platinum-rhodium (PtRh) conformal resistance temperature detector (CRTD) was proposed, the structure and preparation process was elaborated, and the aging sintering process of the film was explored to form a stable conductive network with high porosity. The dense surface and cross-sectional characteristics of the film were demonstrated, the lattice characteristic of PtRh was demonstrated, and the composition and valence states of the elements were characterized in detail. The high-temperature testing system was built, and the test results showed that the developed PtRh CRTD could achieve dynamic temperature detection at <inline-formula> <tex-math>$1200~^{circ }$ </tex-math></inline-formula>C, with a linear fitting goodness of 0.99834. The resistance drift rate of the 31 h high-temperature durability test was 1.11%/h, and the maximum full-scale error was 1.58%FS. Furthermore, the PtRh high-temperature conformal ceramic bolt was prepared and subjected to thermal shock testing. The results showed that it more accurately reflects the temperature of the structural component itself than the discrete surface mount temperature measurement method, providing a feasible solution for in situ detection of complex curved high-temperature hot-end components.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 5","pages":"8016-8023"},"PeriodicalIF":4.3,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Force plates utilizing various principles capable of simultaneously measuring vertical and shear forces have been developed to analyze ground reaction force (GRF) in studies of animal locomotion mechanics. Recently, a triaxial force plate with high accuracy and measurement independence has been proposed using the sampling moiré (SM) method and a prism. The SM method can detect in-plane displacement of a pattern image with significantly higher accuracy than pixel size. However, with implementations using area-scan cameras, it was difficult to achieve both large number of pixels, which determine force resolution, and high frame rate, which affects temporal resolution. Here, we propose an SM method force plate with high accuracy and time resolution using a line-scan camera with a single vertical pixel, high horizontal resolution, and high frame rate. To realize three-axis force measurement from 1-D images, a pair of tilted patterns are utilized for SM method analysis. The fabricated force plate was calibrated and confirmed to have a force resolution of less than 35 mN for each axis and a positional error of less than 3%. Therefore, the proposed force plate can be useful for highly accurate GRF measurement with high frame rate capabilities.
{"title":"Three-Axis Force Plate Using High-Speed Line-Scan Camera and Tilted Pattern for Sampling Moiré Method","authors":"Yukitake Nakahara;Ohga Nomura;Toshihiro Shiratori;Hidetoshi Takahashi","doi":"10.1109/JSEN.2025.3530770","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3530770","url":null,"abstract":"Force plates utilizing various principles capable of simultaneously measuring vertical and shear forces have been developed to analyze ground reaction force (GRF) in studies of animal locomotion mechanics. Recently, a triaxial force plate with high accuracy and measurement independence has been proposed using the sampling moiré (SM) method and a prism. The SM method can detect in-plane displacement of a pattern image with significantly higher accuracy than pixel size. However, with implementations using area-scan cameras, it was difficult to achieve both large number of pixels, which determine force resolution, and high frame rate, which affects temporal resolution. Here, we propose an SM method force plate with high accuracy and time resolution using a line-scan camera with a single vertical pixel, high horizontal resolution, and high frame rate. To realize three-axis force measurement from 1-D images, a pair of tilted patterns are utilized for SM method analysis. The fabricated force plate was calibrated and confirmed to have a force resolution of less than 35 mN for each axis and a positional error of less than 3%. Therefore, the proposed force plate can be useful for highly accurate GRF measurement with high frame rate capabilities.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 5","pages":"8085-8092"},"PeriodicalIF":4.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-29DOI: 10.1109/JSEN.2025.3528030
Reza Rahimi Azghan;Nicholas C. Glodosky;Ramesh Kumar Sah;Carrie Cuttler;Ryan McLaughlin;Michael J. Cleveland;Hassan Ghasemzadeh
Wearable sensor systems have demonstrated great potential for real-time, objective monitoring of physiological health to support behavioral interventions. However, obtaining accurate labels in free-living environments remains challenging due to limited human supervision and reliance on self-labeling by patients, complicating data collection and supervised learning. To address this, we introduce cannabis use detection with label efficiency (CUDLE), a novel framework that leverages self-supervised learning with real-world wearable sensor data to automatically detect cannabis consumption in free-living environments. CUDLE identifies consumption moments using sensor-derived data through a contrastive learning framework, first learning robust representations via a self-supervised pretext task with data augmentation. These representations are then fine-tuned in a downstream task with a shallow classifier, allowing CUDLE to outperform traditional supervised methods, especially with limited labeled data. To evaluate our approach, we conducted a clinical study with 20 cannabis users, collecting over 500 h of wearable sensor data and user-reported cannabis use moments through ecological momentary assessment (EMA) methods. Our analysis shows that CUDLE achieves a higher accuracy of 73.4% compared to 71.1% for the supervised approach, with the performance gap widening as the number of labels decreases. Notably, CUDLE not only surpasses the supervised model while using 75% fewer labels but also reaches peak performance with far fewer subjects, indicating its efficiency in learning from both limited labels and data. These findings have significant implications for real-world applications, where data collection and annotation are labor-intensive, offering a path to more scalable and practical solutions in computational health.
{"title":"CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments Using Wearables","authors":"Reza Rahimi Azghan;Nicholas C. Glodosky;Ramesh Kumar Sah;Carrie Cuttler;Ryan McLaughlin;Michael J. Cleveland;Hassan Ghasemzadeh","doi":"10.1109/JSEN.2025.3528030","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3528030","url":null,"abstract":"Wearable sensor systems have demonstrated great potential for real-time, objective monitoring of physiological health to support behavioral interventions. However, obtaining accurate labels in free-living environments remains challenging due to limited human supervision and reliance on self-labeling by patients, complicating data collection and supervised learning. To address this, we introduce cannabis use detection with label efficiency (CUDLE), a novel framework that leverages self-supervised learning with real-world wearable sensor data to automatically detect cannabis consumption in free-living environments. CUDLE identifies consumption moments using sensor-derived data through a contrastive learning framework, first learning robust representations via a self-supervised pretext task with data augmentation. These representations are then fine-tuned in a downstream task with a shallow classifier, allowing CUDLE to outperform traditional supervised methods, especially with limited labeled data. To evaluate our approach, we conducted a clinical study with 20 cannabis users, collecting over 500 h of wearable sensor data and user-reported cannabis use moments through ecological momentary assessment (EMA) methods. Our analysis shows that CUDLE achieves a higher accuracy of 73.4% compared to 71.1% for the supervised approach, with the performance gap widening as the number of labels decreases. Notably, CUDLE not only surpasses the supervised model while using 75% fewer labels but also reaches peak performance with far fewer subjects, indicating its efficiency in learning from both limited labels and data. These findings have significant implications for real-world applications, where data collection and annotation are labor-intensive, offering a path to more scalable and practical solutions in computational health.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 5","pages":"9093-9100"},"PeriodicalIF":4.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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