Measurement Sensors最新文献_第4页

Deep learning for liver evaluation: A comprehensive review and implications for ulcerative colitis detection 肝脏评估的深度学习：溃疡性结肠炎检测的全面回顾和意义

Q4 Engineering

Measurement Sensors

Pub Date : 2025-06-01 Epub Date: 2025-03-05 DOI: 10.1016/j.measen.2025.101867

Sunaina Verma , Manju Bala , Mohit Angurala

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.

本文探讨了基于深度学习的计算机辅助诊断（DL-CAD）系统在评估计算机断层扫描（CT）所得肝脏图像时的应用。它突出了当代最先进的深度学习框架（如卷积神经网络（cnn）和UNets）的能力，可以非常准确地自动分割和分类肝脏病变。该分析进一步扩展了一些全身性疾病如溃疡性结肠炎（UC）与特定肝脏相关疾病如原发性硬化性胆管炎、脂肪肝和自身免疫性肝炎之间存在的关系。上述情况在UC患者中经常出现，再次证明了成像技术在提供适当和及时治疗方面的重要性。我们的研究表明，DL-CAD系统可以适当修改，以识别由UC引起的肝脏变化，这在诊断方面具有优势，而不会给放射科医生带来过重的负担。此外，纳入可穿戴设备进行定期肝脏评估进一步支持个性化患者管理的概念。因此，本研究在UC患者肝脏病变及其并发症的分析方面，在临床实践和治疗结果方面有了显著的改进。

{"title":"Deep learning for liver evaluation: A comprehensive review and implications for ulcerative colitis detection","authors":"Sunaina Verma , Manju Bala , 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":"2025-06-01","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}

引用次数: 0

Human-machine interaction in mechanical systems through sensor enabled wearable augmented reality interfaces 通过传感器支持的可穿戴增强现实界面在机械系统中的人机交互

Q4 Engineering

Measurement Sensors

Pub Date : 2025-06-01 Epub Date: 2025-05-15 DOI: 10.1016/j.measen.2025.101880

K. Balamurugan , G. Sudhakar , Kavin Francis Xavier , N. Bharathiraja , Gaganpreet Kaur

The research improves mechanical systems by using wearable sensor-based Augmented Reality (AR) interfaces for better Human-Machine Interaction (HCI). Industrial AR systems currently face problems created by their static programming methods along with delayed responsiveness and restricted sensor collectability and insufficient wireless throughput that results in system inefficiency and elevated stress on users. A new wearable AR system using gloves with haptic feedback and flex sensors with Inertial Measurement Units provides precise gesture-control while displaying real-time contextual information. The dynamic gesture recognition system uses Random Forest as its lightweight machine learning model to achieve 93.4 % accuracy in mapping gestures to command sequences which represents a 14.6 % enhancement above conventional static models. The system leverages Edge Computing for low-latency processing (average latency <47 ms) and cloud-based analytics for predictive maintenance insights. The proposed setup demonstrated an enhanced industrial performance in a simulated environment through error reduction by 22.3 % along with a 31.1 % increase in task speed and a 27.8 % improvement in situational awareness recorded through NASA-TLX cognitive load evaluations. Findings prove that the system fills fundamental weaknesses with current AR-assisted industrial HCI systems by providing automatic adaptation features along with improved safety measures and precise operational capability.

该研究通过使用基于可穿戴传感器的增强现实（AR）接口来改善机械系统，以实现更好的人机交互（HCI）。工业AR系统目前面临着静态编程方法带来的问题，以及响应延迟、传感器可收集性受限和无线吞吐量不足，导致系统效率低下和用户压力增加。一种新的可穿戴AR系统使用带有触觉反馈的手套和带有惯性测量单元的弯曲传感器，在显示实时上下文信息的同时提供精确的手势控制。动态手势识别系统使用随机森林作为其轻量级机器学习模型，在将手势映射到命令序列方面达到93.4%的准确率，比传统静态模型提高了14.6%。该系统利用边缘计算进行低延迟处理（平均延迟47毫秒），并利用基于云的分析进行预测性维护洞察。通过NASA-TLX认知负荷评估记录，该装置在模拟环境中通过减少22.3%的错误，提高31.1%的任务速度和27.8%的态势感知能力，证明了工业性能的增强。研究结果证明，该系统通过提供自动适应功能以及改进的安全措施和精确的操作能力，填补了当前ar辅助工业HCI系统的基本弱点。

{"title":"Human-machine interaction in mechanical systems through sensor enabled wearable augmented reality interfaces","authors":"K. Balamurugan , G. Sudhakar , Kavin Francis Xavier , N. Bharathiraja , Gaganpreet Kaur","doi":"10.1016/j.measen.2025.101880","DOIUrl":"10.1016/j.measen.2025.101880","url":null,"abstract":"<div><div>The research improves mechanical systems by using wearable sensor-based Augmented Reality (AR) interfaces for better Human-Machine Interaction (HCI). Industrial AR systems currently face problems created by their static programming methods along with delayed responsiveness and restricted sensor collectability and insufficient wireless throughput that results in system inefficiency and elevated stress on users. A new wearable AR system using gloves with haptic feedback and flex sensors with Inertial Measurement Units provides precise gesture-control while displaying real-time contextual information. The dynamic gesture recognition system uses Random Forest as its lightweight machine learning model to achieve 93.4 % accuracy in mapping gestures to command sequences which represents a 14.6 % enhancement above conventional static models. The system leverages Edge Computing for low-latency processing (average latency <47 ms) and cloud-based analytics for predictive maintenance insights. The proposed setup demonstrated an enhanced industrial performance in a simulated environment through error reduction by 22.3 % along with a 31.1 % increase in task speed and a 27.8 % improvement in situational awareness recorded through NASA-TLX cognitive load evaluations. Findings prove that the system fills fundamental weaknesses with current AR-assisted industrial HCI systems by providing automatic adaptation features along with improved safety measures and precise operational capability.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101880"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144105432","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

High-fidelity EEG feature-engineered taxonomy for bruxism and PLMS prognostication through pioneering and avant-garde ML frameworks 高保真脑电图特征工程分类法为磨牙症和PLMS预测通过开拓和前卫的ML框架

Q4 Engineering

Measurement Sensors

Pub Date : 2025-06-01 Epub Date: 2025-03-13 DOI: 10.1016/j.measen.2025.101868

Shivam Tiwari , Deepak Arora , Barkha Bhardwaj

Periodic Leg Movement during Sleep (PLMS) and Bruxism are linked with changes in EEG signal characteristics. This work applies machine learning and data mining approaches to examine these changes. Patients with PLMS and bruxism had nighttime EEG recordings to examine changes in brain activity. The findings revealed constant variations in brain hemodynamics even in the absence of clearly observable arousals in the EEG. Wavelet decomposition was used to improve classification precision. Using the N3 sleep stage, accuracy varied from 92 % to 96 %, with an AUC of 0.85–0.89, in diagnosing binary sleep disorders. Still, adding wavelet-based elements greatly enhanced performance, obtaining an AUC of 0.99 with classification accuracy ranging from 94 % to 98 %. This emphasizes how strongly discriminative power wavelet-extracted EEG characteristics possess. Using K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) with Radial Basis Function (RBF), Bruxism categorization was accomplished. These models attained respectively 82 %, 90 %, and 93 % percent classification accuracy. This work is the first to show a direct connection among differences in brain activity based on PLMS, Bruxism, and EEG-based technologies. The results show how well machine learning methods and EEG feature extraction might diagnose sleep problems. Although the therapeutic relevance of these findings is yet unknown, the results imply that enhanced EEG-based classification techniques could produce more reliable and automated diagnostic instruments for Bruxism and PLMS.

睡眠期间周期性腿部运动（PLMS）和磨牙症与脑电图信号特征的变化有关。这项工作应用机器学习和数据挖掘方法来检查这些变化。患有PLMS和磨牙症的患者有夜间脑电图记录，以检查大脑活动的变化。研究结果显示，即使在脑电图中没有清晰观察到的觉醒，脑血流动力学也会不断变化。采用小波分解提高分类精度。使用N3睡眠阶段，诊断二元睡眠障碍的准确率从92%到96%不等，AUC为0.85-0.89。然而，添加基于小波的元素大大提高了性能，获得了0.99的AUC，分类精度在94%到98%之间。这强调了功率小波提取的脑电特征具有很强的判别性。采用k近邻（KNN）、人工神经网络（ANN）和径向基函数支持向量机（SVM）对磨牙症进行分类。这些模型分别达到了82%、90%和93%的分类准确率。这项工作首次显示了基于PLMS、磨牙症和基于脑电图技术的大脑活动差异之间的直接联系。结果表明，机器学习方法和脑电图特征提取可以很好地诊断睡眠问题。虽然这些发现的治疗相关性尚不清楚，但结果表明，增强的基于脑电图的分类技术可以为磨牙症和PLMS提供更可靠和自动化的诊断工具。

{"title":"High-fidelity EEG feature-engineered taxonomy for bruxism and PLMS prognostication through pioneering and avant-garde ML frameworks","authors":"Shivam Tiwari , Deepak Arora , Barkha Bhardwaj","doi":"10.1016/j.measen.2025.101868","DOIUrl":"10.1016/j.measen.2025.101868","url":null,"abstract":"<div><div>Periodic Leg Movement during Sleep (PLMS) and Bruxism are linked with changes in EEG signal characteristics. This work applies machine learning and data mining approaches to examine these changes. Patients with PLMS and bruxism had nighttime EEG recordings to examine changes in brain activity. The findings revealed constant variations in brain hemodynamics even in the absence of clearly observable arousals in the EEG. Wavelet decomposition was used to improve classification precision. Using the N3 sleep stage, accuracy varied from 92 % to 96 %, with an AUC of 0.85–0.89, in diagnosing binary sleep disorders. Still, adding wavelet-based elements greatly enhanced performance, obtaining an AUC of 0.99 with classification accuracy ranging from 94 % to 98 %. This emphasizes how strongly discriminative power wavelet-extracted EEG characteristics possess. Using K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) with Radial Basis Function (RBF), Bruxism categorization was accomplished. These models attained respectively 82 %, 90 %, and 93 % percent classification accuracy. This work is the first to show a direct connection among differences in brain activity based on PLMS, Bruxism, and EEG-based technologies. The results show how well machine learning methods and EEG feature extraction might diagnose sleep problems. Although the therapeutic relevance of these findings is yet unknown, the results imply that enhanced EEG-based classification techniques could produce more reliable and automated diagnostic instruments for Bruxism and PLMS.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101868"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682455","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

IOT based wearable sensor system architecture for classifying human activity 基于物联网的人体活动分类可穿戴传感器系统架构

Q4 Engineering

Measurement Sensors

Pub Date : 2025-06-01 Epub Date: 2025-03-24 DOI: 10.1016/j.measen.2025.101871

V. Mahalakshmi , Pramod Kumar , Manisha Bhende , Ismail Keshta , Swatiben Yashvantbhai Rathod , Janjhyam Venkata Naga Ramesh

Human Activity Recognition (HAR) has applications in diverse fields, including sports management and behavior classification. Existing HAR methods can be categorized into three main approaches: camera-based, wearable sensor-based, and Wi-Fi sensing-based. Camera-based methods suffer from privacy concerns, while wearable sensor-based methods face limitations in battery longevity and continuous monitoring. Wi-Fi sensing methods mitigate privacy and battery issues but rely on costly Intel 5300 network cards or software-defined radio (SDR) platforms, limiting scalability. This paper presents a cost-effective IoT-based human activity recognition system using ESP32, leveraging its Wi-Fi sensing capabilities. The proposed system follows a structured workflow: (i) channel state information (CSI) is extracted from ESP32 modules, (ii) data preprocessing is performed using Hampel and Gaussian filters for noise and outlier removal, (iii) dimensionality reduction is achieved through Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT), and (iv) activity classification is conducted using Dynamic Time Warping (DTW) and the K-Nearest Neighbors (KNN) algorithm. Experimental evaluations demonstrate that the proposed system achieves an average recognition accuracy of 98.6 % across six human activities, comparable to high-end Intel 5300-based HAR systems, while significantly reducing hardware costs and improving ease of deployment.

人体活动识别（Human Activity Recognition， HAR）在体育管理、行为分类等领域有着广泛的应用。现有的HAR方法可分为三种主要方法：基于摄像头、基于可穿戴传感器和基于Wi-Fi传感器。基于摄像头的方法存在隐私问题，而基于可穿戴传感器的方法则面临电池寿命和连续监测的限制。Wi-Fi传感方法减轻了隐私和电池问题，但依赖于昂贵的英特尔5300网卡或软件定义无线电（SDR）平台，限制了可扩展性。本文介绍了一种经济高效的基于物联网的人体活动识别系统，该系统使用ESP32，利用其Wi-Fi传感功能。所提出的系统遵循结构化的工作流程：(i)从ESP32模块中提取通道状态信息（CSI），（ii）使用Hampel和高斯滤波器进行数据预处理以去除噪声和异常值，（iii）通过主成分分析（PCA）和离散小波变换（DWT）实现降维，（iv）使用动态时间规整（DTW）和k -近邻（KNN）算法进行活动分类。实验评估表明，该系统在六种人类活动中实现了98.6%的平均识别准确率，与基于英特尔5300的高端HAR系统相当，同时显著降低了硬件成本并提高了部署的便利性。

{"title":"IOT based wearable sensor system architecture for classifying human activity","authors":"V. Mahalakshmi , Pramod Kumar , Manisha Bhende , Ismail Keshta , Swatiben Yashvantbhai Rathod , Janjhyam Venkata Naga Ramesh","doi":"10.1016/j.measen.2025.101871","DOIUrl":"10.1016/j.measen.2025.101871","url":null,"abstract":"<div><div>Human Activity Recognition (HAR) has applications in diverse fields, including sports management and behavior classification. Existing HAR methods can be categorized into three main approaches: camera-based, wearable sensor-based, and Wi-Fi sensing-based. Camera-based methods suffer from privacy concerns, while wearable sensor-based methods face limitations in battery longevity and continuous monitoring. Wi-Fi sensing methods mitigate privacy and battery issues but rely on costly Intel 5300 network cards or software-defined radio (SDR) platforms, limiting scalability. This paper presents a cost-effective IoT-based human activity recognition system using ESP32, leveraging its Wi-Fi sensing capabilities. The proposed system follows a structured workflow: (i) channel state information (CSI) is extracted from ESP32 modules, (ii) data preprocessing is performed using Hampel and Gaussian filters for noise and outlier removal, (iii) dimensionality reduction is achieved through Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT), and (iv) activity classification is conducted using Dynamic Time Warping (DTW) and the K-Nearest Neighbors (KNN) algorithm. Experimental evaluations demonstrate that the proposed system achieves an average recognition accuracy of 98.6 % across six human activities, comparable to high-end Intel 5300-based HAR systems, while significantly reducing hardware costs and improving ease of deployment.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101871"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716031","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

Comparative analysis of inertial measurement units and markerless video motion capture systems for assessing rotational parameters in snowboard freestyle 惯性测量单元与无标记视频运动捕捉系统在自由式滑雪中旋转参数评估中的对比分析

Q4 Engineering

Measurement Sensors

Pub Date : 2025-06-01 Epub Date: 2025-03-30 DOI: 10.1016/j.measen.2025.101872

Tom Gorges , Christian Merz , Felix Friedl , Ingo Sandau

In snowboard freestyle, the measured amount of rotation (mAR) is a key judging criteria. Rotational parameters like angular velocity (AV) support athletes and coaches in performance enhancements. This study evaluates the validity of on-snow available inertial measurement unit (IMU) data with a markerless optical tracking system. Eight elite snowboard riders performed 88 tricks with a bounce board on a trampoline that were concurrently measured using a board-mounted IMU and a video motion capture system (criterion). The validity of the IMU was determined for discrete (mAR) and time-series (AV) data via t-test, effect size (d), concordance correlation coefficient (CCC), standard deviation of differences (SDD), and bias ±limits of agreement (LoA). For discrete data, results indicated excellent absolute and relative concurrent validity of mAR (SDD = ±8.18°; SDD% = ±1.42%; CCC = 0.998; bias ± LoA = 1.80° ± 16.02°) despite significant mean differences (p

<

0.05; d

< | 0.2 |

) between both systems. For time-series data, acceptable absolute and relative concurrent validity exist for AV (mean SDD

<

45°; mean SDD%

<

10%; mean CCC

>

0.9; bias ± LoA = −0.19°/s ± 87.48°/s) showing significant mean differences only in the first 1% of the time-series (p

<

0.05; d

> | 0.2 |

). In conclusion, using a board-mounted IMU is a valid approach to measure rotational parameters in snowboard freestyle, highlighting IMUs’ potential for on-field performance analysis. Nonetheless, caution is advised when interpreting AV at individual time points due to the observed variability, especially in close temporal proximity to take-off and landing events.

在单板自由泳中，测量的旋转量（mAR）是一个关键的评判标准。像角速度（AV）这样的旋转参数支持运动员和教练提高成绩。本文利用无标记光学跟踪系统对雪地可用惯性测量单元（IMU）数据的有效性进行了评估。八名优秀的滑雪板运动员在蹦床上用弹跳板表演了88个特技，同时使用板上IMU和视频动作捕捉系统（标准）进行测量。通过t检验、效应量(d)、一致性相关系数（CCC）、差异标准差（SDD）和偏倚±一致限（LoA）来确定离散（mAR）和时间序列（AV）数据的IMU效度。对于离散数据，结果显示mAR的绝对和相对并发效度良好(SDD =±8.18°；Sdd % =±1.42%；CCC = 0.998；偏差±LoA = 1.80°±16.02°)，尽管平均差异显著(p <；0.05;D <|0.2|)。对于时间序列数据，AV存在可接受的绝对和相对并发效度(mean SDD <；45°;平均SDD% <；10%;平均CCC >；0.9;偏差±LoA = - 0.19°/s±87.48°/s)，仅在前1%的时间序列中显示显著的平均差异(p <；0.05;d祝辞| 0.2 |)。总之，使用板载IMU是测量单板自由式旋转参数的有效方法，突出了IMU在现场性能分析中的潜力。尽管如此，由于观察到的可变性，特别是在接近起飞和着陆事件的时间点，在解释单个时间点的AV时，建议谨慎。

{"title":"Comparative analysis of inertial measurement units and markerless video motion capture systems for assessing rotational parameters in snowboard freestyle","authors":"Tom Gorges , Christian Merz , Felix Friedl , Ingo Sandau","doi":"10.1016/j.measen.2025.101872","DOIUrl":"10.1016/j.measen.2025.101872","url":null,"abstract":"<div><div>In snowboard freestyle, the measured amount of rotation (mAR) is a key judging criteria. Rotational parameters like angular velocity (AV) support athletes and coaches in performance enhancements. This study evaluates the validity of on-snow available inertial measurement unit (IMU) data with a markerless optical tracking system. Eight elite snowboard riders performed 88 tricks with a bounce board on a trampoline that were concurrently measured using a board-mounted IMU and a video motion capture system (criterion). The validity of the IMU was determined for discrete (mAR) and time-series (AV) data via t-test, effect size (d), concordance correlation coefficient (CCC), standard deviation of differences (SDD), and bias ±limits of agreement (LoA). For discrete data, results indicated excellent absolute and relative concurrent validity of mAR (SDD = ±8.18°; SDD% = ±1.42%; CCC = 0.998; bias ± LoA = 1.80° ± 16.02°) despite significant mean differences (p <span><math><mo><</mo></math></span> 0.05; d <span><math><mrow><mo><</mo><mrow><mo>|</mo><mn>0</mn><mo>.</mo><mn>2</mn><mo>|</mo></mrow></mrow></math></span>) between both systems. For time-series data, acceptable absolute and relative concurrent validity exist for AV (mean SDD <span><math><mo><</mo></math></span> 45°; mean SDD% <span><math><mo><</mo></math></span> 10%; mean CCC <span><math><mo>></mo></math></span> 0.9; bias ± LoA = −0.19°/s ± 87.48°/s) showing significant mean differences only in the first 1% of the time-series (p <span><math><mo><</mo></math></span> 0.05; d <span><math><mrow><mo>></mo><mspace></mspace><mrow><mo>|</mo><mn>0</mn><mo>.</mo><mn>2</mn><mo>|</mo></mrow></mrow></math></span>). In conclusion, using a board-mounted IMU is a valid approach to measure rotational parameters in snowboard freestyle, highlighting IMUs’ potential for on-field performance analysis. Nonetheless, caution is advised when interpreting AV at individual time points due to the observed variability, especially in close temporal proximity to take-off and landing events.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101872"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739109","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

Corrigendum to “A deep learning based solution for data disproportionproblem in side channel attacks using intelligent sensors” [Measur. Sens. 33 (2024) 101137 1–8] “使用智能传感器的基于深度学习的侧信道攻击中数据歧化问题解决方案”的勘误表。Sens. 33 (2024) 101137 1-8]

Q4 Engineering

Measurement Sensors

Pub Date : 2025-06-01 Epub Date: 2025-04-05 DOI: 10.1016/j.measen.2025.101869

B. Indupriya , Vijaya Chandra Jadala , D.V. Lalitha Parameswari

引用次数: 0

Wearable sensor-based fall detection for elderly care using ensemble machine learning techniques 基于集成机器学习技术的可穿戴传感器的老年人跌倒检测

Q4 Engineering

Measurement Sensors

Pub Date : 2025-06-01 Epub Date: 2025-03-22 DOI: 10.1016/j.measen.2025.101870

Ch Gangadhar , P Pavithra Roy , R. Dinesh Kumar , Janjhyam Venkata Naga Ramesh , S. Ravikanth , N. Akhila

Older people face serious issues with unintentional collisions that result in healthcare admissions and fatalities. Since numerous accidents happen quickly, it might be difficult to identify crashes in context. Enhancing the quality of services for older people requires the development of a computerized surveillance network that can anticipate accidents before occur, offer protection throughout the incident, and send out remote warnings following an accident. This research suggested a wearing surveillance system that seeks to detect accidents at the onset and lineage, triggering an alarm to reduce damages caused by accidents and sending out an external alert when the human body hits the hard surface. Meanwhile, the research's offsite evaluation of a combined structure utilizing the Random Forest technique (RF), Supporting Vectors Machines (SVM), and available information were used to illustrate this idea. The suggested method employed RF to reliably retrieve features from speedometer and inertial facts, while SVM provides an estimator and classification-capable method. Each module in the unique category-based composite structure is recognized at a certain level. The suggested strategy outperformed modern fall identification techniques when tested using the labeled KFall database, achieving average precision of 95 percent, 96 percent, as well as 98 percent for Non-Falls, Pre-Falls, as well as detectable fall incidents, correspondingly. The whole assessment proved the algorithmic learning structure's efficacy. Older people's standard of existence will increase, and accidents will be avoided because of such smart tracking devices.

老年人面临着严重的意外碰撞问题，导致医疗入院和死亡。由于许多事故发生得很快，因此可能很难在上下文中识别事故。要提高为长者提供的服务质素，就必须发展电脑监控网络，在意外发生前作出预测，在意外发生时提供全程保护，并在意外发生后发出远程警告。该研究提出了一种穿戴式监视系统，该系统可以在事故发生和发生过程中发现事故，并发出警报以减少事故造成的损害，当人体接触到坚硬的表面时，会发出外部警报。同时，该研究利用随机森林技术（RF）、支持向量机（SVM）和现有信息对组合结构进行了场外评估，以说明这一想法。该方法利用射频可靠地从速度计和惯性事实中检索特征，而支持向量机提供了一种估计和分类能力的方法。唯一的基于类别的复合结构中的每个模块都在某个级别上得到识别。当使用标记的KFall数据库进行测试时，建议的策略优于现代跌倒识别技术，相应的，在非跌倒、预跌倒和可检测的跌倒事件中，平均精度分别达到95%、96%和98%。整个评估证明了算法学习结构的有效性。老年人的生活水平将会提高，事故也会因为这样的智能跟踪设备而避免。

{"title":"Wearable sensor-based fall detection for elderly care using ensemble machine learning techniques","authors":"Ch Gangadhar , P Pavithra Roy , R. Dinesh Kumar , Janjhyam Venkata Naga Ramesh , S. Ravikanth , N. Akhila","doi":"10.1016/j.measen.2025.101870","DOIUrl":"10.1016/j.measen.2025.101870","url":null,"abstract":"<div><div>Older people face serious issues with unintentional collisions that result in healthcare admissions and fatalities. Since numerous accidents happen quickly, it might be difficult to identify crashes in context. Enhancing the quality of services for older people requires the development of a computerized surveillance network that can anticipate accidents before occur, offer protection throughout the incident, and send out remote warnings following an accident. This research suggested a wearing surveillance system that seeks to detect accidents at the onset and lineage, triggering an alarm to reduce damages caused by accidents and sending out an external alert when the human body hits the hard surface. Meanwhile, the research's offsite evaluation of a combined structure utilizing the Random Forest technique (RF), Supporting Vectors Machines (SVM), and available information were used to illustrate this idea. The suggested method employed RF to reliably retrieve features from speedometer and inertial facts, while SVM provides an estimator and classification-capable method. Each module in the unique category-based composite structure is recognized at a certain level. The suggested strategy outperformed modern fall identification techniques when tested using the labeled KFall database, achieving average precision of 95 percent, 96 percent, as well as 98 percent for Non-Falls, Pre-Falls, as well as detectable fall incidents, correspondingly. The whole assessment proved the algorithmic learning structure's efficacy. Older people's standard of existence will increase, and accidents will be avoided because of such smart tracking devices.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101870"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747686","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

Recent progress in the implementation of sustainable farming 实施可持续农业的最新进展

Q4 Engineering

Measurement Sensors

Pub Date : 2025-06-01 Epub Date: 2025-04-24 DOI: 10.1016/j.measen.2025.101877

Murugesan Muthukumar , Alagar Karthick

Cutting-edge technology in agriculture has the capacity to revolutionize the industry and advance sustainability objectives. With escalating apprehensions over climate change and food insecurity, there is an increasing agreement that sophisticated agricultural methods are vital. This study examines how data analytics, Internet of Things (IoT) sensors, and precision agriculture might assist farmers in enhancing decision-making, optimizing resource management, and minimizing environmental impact. This article seeks to elucidate the intricacies of these technologies, offering stakeholders guidance to facilitate the extensive acceptance and progression of sustainable farming methods.

农业的尖端技术有能力彻底改变这个行业，推进可持续发展目标。随着对气候变化和粮食不安全的担忧不断升级，越来越多的人认为先进的农业方法至关重要。本研究探讨了数据分析、物联网（IoT）传感器和精准农业如何帮助农民加强决策、优化资源管理和最大限度地减少环境影响。本文旨在阐明这些技术的复杂性，为利益相关者提供指导，以促进可持续农业方法的广泛接受和发展。

引用次数: 0

A new deadweight force standard machine for classifying C6 load cells under temperature conditions 一种在温度条件下对C6称重传感器进行分类的新型自重力标准机

Q4 Engineering

Measurement Sensors

Pub Date : 2025-05-01 Epub Date: 2024-12-24 DOI: 10.1016/j.measen.2024.101333

Daniel Schwind , Martin Eller , Olaf Krusemark , Kai Reglitz

While the main focus of Force Standard Machines (FSM) is on the uncertainty and traceability to national or international standards when calibrating force transducers, the linearity, hysteresis and reproducibility requirements under thermal conditions are at the forefront when testing load cells [1]. Beyond that it is often underestimated that deadweight machines are unavoidable for the requirements of C6 load cells in accordance with OIML R60 [2]. But such deadweight machines largely determine the cost and at the end the selling price of the individual load cell. This results in the need to find an economical solution for the standard machine through new concepts.

This paper describes the design and performance of a new 2000 kg standard machine for economic testing of C6 load cells under temperature conditions by implementing a load cell magazine, full automation and digital connection to the production network of Minebea Intec at Hamburg, a leading global manufacturer of industrial weighing and inspection technologies.

虽然力标准机（FSM）在校准力传感器时主要关注的是不确定性和对国家或国际标准的可追溯性，但在测试称重传感器[1]时，热条件下的线性、滞后和再现性要求是最重要的。除此之外，通常被低估的是，根据OIML R60[2]的要求，C6称重传感器是不可避免的。但是这种沉重的机器在很大程度上决定了单个测压元件的成本和最终的销售价格。这导致需要通过新概念为标准机器找到经济的解决方案。本文介绍了一种新的2000公斤标准机器的设计和性能，该机器用于在温度条件下对C6称重传感器进行经济测试，通过实现称重传感器杂志，全自动和数字连接到位于汉堡的Minebea Intec的生产网络，该网络是全球领先的工业称重和检测技术制造商。

{"title":"A new deadweight force standard machine for classifying C6 load cells under temperature conditions","authors":"Daniel Schwind , Martin Eller , Olaf Krusemark , Kai Reglitz","doi":"10.1016/j.measen.2024.101333","DOIUrl":"10.1016/j.measen.2024.101333","url":null,"abstract":"<div><div>While the main focus of Force Standard Machines (FSM) is on the uncertainty and traceability to national or international standards when calibrating force transducers, the linearity, hysteresis and reproducibility requirements under thermal conditions are at the forefront when testing load cells [<span><span>1</span></span>]. Beyond that it is often underestimated that deadweight machines are unavoidable for the requirements of C6 load cells in accordance with OIML R60 [<span><span>2</span></span>]. But such deadweight machines largely determine the cost and at the end the selling price of the individual load cell. This results in the need to find an economical solution for the standard machine through new concepts.</div><div>This paper describes the design and performance of a new 2000 kg standard machine for economic testing of C6 load cells under temperature conditions by implementing a load cell magazine, full automation and digital connection to the production network of Minebea Intec at Hamburg, a leading global manufacturer of industrial weighing and inspection technologies.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101333"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211946","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

New Sartorius mass comparator for highest accuracy including fully automatic vacuum transfer system 新型赛多利斯质量比较仪，精度最高，包括全自动真空传输系统

Q4 Engineering

Measurement Sensors

Pub Date : 2025-05-01 Epub Date: 2024-12-23 DOI: 10.1016/j.measen.2024.101360

Falko Hilbrunner , Mario Schreiber , Thomas Fröhlich , Thomas Fehling , Folker Schwesinger , Gunter Krapf

Sartorius cooperated with Technische Universität Ilmenau to develop the innovative automatic mass comparator VMC1007. This facilitates highly accurate mass comparisons of weights between 100 g and 1 kg under controlled atmospheric conditions, and in a vacuum. For permanent storage of weights under vacuum conditions, the mass comparator can optionally be configured with a vacuum transfer system and corresponding containers.

The device is very compact, has eight universal positions for directly handling weights of different shapes and allows for precise and fast mass comparisons.

In order to make the processes for inserting and ejecting weights as simple and safe as possible, all mechanical movement axes are motorised.

Mass comparisons can be controlled, monitored and analysed easily and irrespective of the operating system using web-based user interface. In all processes, the user is intuitively guided through the individual steps to enable safe, efficient and convenient work.

赛多利斯与Technische Universität Ilmenau合作开发了创新的自动质量比较器VMC1007。这有助于在受控大气条件下和真空中高度精确地进行100克和1千克之间的质量比较。为了在真空条件下永久储存重量，质量比较器可选择配置真空传递系统和相应的容器。该设备非常紧凑，有八个通用位置直接处理不同形状的重量，并允许精确和快速的质量比较。为了使插入和抛出重量的过程尽可能简单和安全，所有机械运动轴都是电动的。使用基于网络的用户界面，无论操作系统如何，都可以轻松地控制、监测和分析大量比较。在所有流程中，用户都能直观地通过各个步骤进行指导，从而实现安全、高效、方便的工作。

引用次数: 0