Measurement Sensors最新文献

{"title":"Comparative analysis of inertial measurement units and markerless video motion capture systems for assessing rotational parameters in snowboard freestyle","authors":"Tom Gorges ,&nbsp;Christian Merz ,&nbsp;Felix Friedl ,&nbsp;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>&lt;</mo></math></span> 0.05; d <span><math><mrow><mo>&lt;</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>&lt;</mo></math></span> 45°; mean SDD% <span><math><mo>&lt;</mo></math></span> 10%; mean CCC <span><math><mo>&gt;</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>&lt;</mo></math></span> 0.05; d <span><math><mrow><mo>&gt;</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":"2025-03-30","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}

{"title":"Selected channel based multiclass emotion classification from wearable human brain EEG signal","authors":"Khushboo Singh,&nbsp;Mitul Kumar Ahirwal,&nbsp;Manish Pandey","doi":"10.1016/j.measen.2025.101874","DOIUrl":"10.1016/j.measen.2025.101874","url":null,"abstract":"<div><div>Emotion recognition is a crucial issue in human-computer interaction, and EEG (electroencephalography) plays a significant role in deciphering human emotions based on physiological data. However, the complex and non-stationary nature of EEG signals, coupled with redundant information from multi-channel recordings, poses challenges in accurate emotion classification. To address this, we propose a hybrid 1DCNN-Bi-LSTM model that integrates spatial feature extraction (1DCNN) with temporal dependency learning (Bi-LSTM), enhancing the robustness of emotion classification. Furthermore, we present a channel selection mechanism to find the most pertinent EEG channels for emotion recognition, hence lowering computing complexity without compromising accuracy. With the chosen-channel model (8 channels) attaining 85.16 % accuracy, a notable improvement over standard full-channel approaches, experimental results on the DEAP dataset show that the suggested methodology provides significant performance gains. This work fits wearable devices and real-time affective computing systems since it offers a scalable and effective method for EEG-based emotion recognition.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101874"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IOT based wearable sensor system architecture for classifying human activity","authors":"V. Mahalakshmi ,&nbsp;Pramod Kumar ,&nbsp;Manisha Bhende ,&nbsp;Ismail Keshta ,&nbsp;Swatiben Yashvantbhai Rathod ,&nbsp;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":"2025-03-24","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}

{"title":"High-fidelity EEG feature-engineered taxonomy for bruxism and PLMS prognostication through pioneering and avant-garde ML frameworks","authors":"Shivam Tiwari ,&nbsp;Deepak Arora ,&nbsp;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":"2025-03-13","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}

{"title":"Wearable sensor-based intent recognition for adaptive control of intelligent ankle-foot prosthetics","authors":"Vidyapati Kumar,&nbsp;Dilip Kumar Pratihar","doi":"10.1016/j.measen.2025.101865","DOIUrl":"10.1016/j.measen.2025.101865","url":null,"abstract":"<div><div>Prosthetic motor control requires improvement to better adapt to varying gait speeds and terrain inclinations in real time. Traditional methods often fail to meet these demands, prompting research into advanced sensor data and machine learning algorithms. This research study tackles the challenge by using wearable sensors and comparing various machine learning approaches, namely Sparse Bidirectional Long Short-Term Memory (SBLSTM), Adaptive Neuro-Fuzzy Inference System (ANFIS), convolutional neural network (CNN), logistic regression, and K-nearest neighbors (KNN) for effective classification of gait speed and terrain inclination. Various wearable sensor data, such as FSR and accelerometers, were employed to develop robust models for prosthetic control. The SBLSTM model, which utilizes time-series data through Bi-Directional LSTM layers, demonstrated impressive performance with an accuracy of 96.3 %, precision of 96.4 %, recall of 96.5 %, and an F1-score of 96.4 %. In contrast, the ANFIS model, combining gradient-based learning and least squares estimation, showed reasonable predictive capabilities with root mean square error (RMSE) values of 0.12 for speed and 0.14 for inclination. The accuracy of CNN, logistic regression, and KNN was reported to be 60 %, 31 %, and 93 %, respectively. Comparing the other models in terms of computation, the mean inference time for SBLSTM was found to be 25 ms, which proved to balance speed and accuracy better than other models. Furthermore, the SBLSTM model is particularly suited for time-dependent data, making it more appropriate for real-time prosthetic control. The results highlight that using advanced machine learning algorithms and wearable sensor data has great potential to increase the responsiveness and adaptability of lower-limb prosthetic systems. Ultimately, the goal of this work is for prosthetic users to benefit in terms of quality of life-related to improved mobility and adaptability across a range of environmental conditions.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101865"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning approaches for continuous blood pressure estimation from photoplethysmography signal","authors":"R. Vanithamani,&nbsp;S. Sri Jayabharathi,&nbsp;S. Pavithra,&nbsp;E. Smily Jeya Jothi","doi":"10.1016/j.measen.2025.101866","DOIUrl":"10.1016/j.measen.2025.101866","url":null,"abstract":"<div><h3>Introduction</h3><div>Monitoring continuous Blood Pressure (BP) signals is essential as BP can vary rapidly. However, current Photoplethysmography (PPG)-based methods for estimating BP need to be more accurate and provide predictions for Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP).</div></div><div><h3>Materials and methods</h3><div>Full cycle of PPG waveform is considered to estimate SBP and DBP values. This study recommends deep learning techniques, including Temporal Convolutional Network (TCN), Long-Short Term Memory (LSTM), TCN-LSTM, and Autoencoder-LSTM, to estimate SBP and DBP.</div></div><div><h3>Results</h3><div>According to the outcomes, the proposed framework estimates continuous BP precisely utilizing PPG signals. Specifically, the Autoencoder-LSTM algorithm achieved a Mean Average Error (MAE) of 1.05 and 0.92 for SBP and DBP and a Standard Deviation (SD) of 1.89 and 1.05 for SBP and DBP, respectively, indicating that the model is suitable for estimating these values from PPG signals. The Autoencoder-LSTM approach produced a Mean Average Error (MAE) of 1.05 and 0.92 for SBP and DBP, respectively, as well as a Standard Deviation (SD) of 1.89 and 1.05, demonstrating that the model can estimate these values using PPG signals.</div></div><div><h3>Conclusion</h3><div>This paper evaluates an algorithm that estimates BP continuously using the PPG signal. Autoencoder-LSTM is suitable for estimating continuous BP values since MAE and SD values are low for SBP and DBP.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101866"},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for liver evaluation: A comprehensive review and implications for ulcerative colitis detection","authors":"Sunaina Verma ,&nbsp;Manju Bala ,&nbsp;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":"2025-03-05","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}

{"title":"A fusion positioning system with environmental-adaptive algorithm: IPSO-IAUKF fusion of UWB and IMU for NLOS noise mitigation","authors":"Yiyang Lyu ,&nbsp;Mingsheng Wei ,&nbsp;Shidang Li ,&nbsp;Di Wang","doi":"10.1016/j.measen.2025.101864","DOIUrl":"10.1016/j.measen.2025.101864","url":null,"abstract":"<div><div>Accurate positioning in non-line-of-sight (NLOS) scenarios persists as a critical challenge for ultra-wideband (UWB) systems. This paper proposes a collaborative positioning framework that integrates an inertial measurement unit (IMU). An improved particle swarm optimization and adaptive unscented Kalman filter (IPSO-IAUKF) algorithm based on environmental assessment is also designed. The threefold contributions include: (1) A tightly coupled positioning system architecture is constructed by deeply integrating UWB ranging with IMU motion measurements; (2) An improved particle swarm optimization (IPSO) algorithm is proposed to optimize the initial coordinate estimation of UWB using a dynamic inertia weight strategy; (3) An adaptive Unscented Kalman Filter (UKF) framework is designed, incorporating an environmental state discrimination threshold and a real-time noise matrix update mechanism to dynamically optimize the covariance matrix, thereby enhancing positioning robustness in complex noise environments. Multi-scenario trajectory simulations and practical experiments are conducted based on the established positioning model. Numerical simulation results demonstrate that the proposed fusion framework achieves a 52.6 % improvement in positioning accuracy compared to standalone UWB solutions, with a 44.6 % enhancement in noise resistance under NLOS interference compared to traditional fusion algorithms. Further practical tests reveal that the IPSO-IAUKF algorithm achieves average positioning accuracy improvements of 52.1 %, 45.5 %, and 46.0 % in two typical noise environments compared to conventional UKF and algorithms 1 and 2 used in this paper, respectively, while the maximum positioning error decreases by 44.6 %, 23.9 %, and 29.7 %, respectively. These results verify the superiority of this method in complex scenarios.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101864"},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a novel disposable flowcell for spectroscopic bioprocess monitoring","authors":"Phil Thiel ,&nbsp;Tobias Steinwedel ,&nbsp;Philipp Raithel ,&nbsp;Mathias Belz ,&nbsp;Dörte Solle","doi":"10.1016/j.measen.2025.101862","DOIUrl":"10.1016/j.measen.2025.101862","url":null,"abstract":"<div><div>Regulatory authorities require product control for market release, especially for medical products due to legal regulations. Thus, end product control is conducted before drug market release. For real-time release in terms of Process Analytical Technology (PAT), product quality must be designed into the process. Process sensors are needed to monitor critical process parameters (CPP) for immediate control. Conventional sensors lack interfaces for disposable bioreactors, but new flow cell systems enable spectroscopic bioprocess monitoring via a bypass system. The flow cell is gamma-sterilized and clamped into a reusable holder, allowing spectroscopic techniques like turbidity, UV/VIS spectroscopy, and fluorescence.</div><div>The cell setup and biocompatibility are presented, with in-vitro toxicity of various 3D printable materials evaluated per ISO 10993 to find suitable materials. Polyamide (PA), Acrylonitrile Butadiene Styrene (ABS) and Polymethyl Methacrylate (PMMA) were used for manufacturing flow cells and tested for in vitro biocompatibility. Results confirm the suitability of these materials and processes, with UV–VIS spectroscopy providing key insights. Selectivity and sensitivity for three different important bioprocess variables were evaluated and enables precise sensor system characterization across various analytes, advancing flow cell and sensor technology in biosensing and analytical chemistry.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101862"},"PeriodicalIF":0.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Open-source, real-time, low-cost, wearable head impact monitoring system","authors":"Alaa Aldin Ghazal ,&nbsp;S.G. Ganpule","doi":"10.1016/j.measen.2025.101863","DOIUrl":"10.1016/j.measen.2025.101863","url":null,"abstract":"<div><div>Mild traumatic brain injury (mTBI) is a significant health concern that can occur due to rapid head movements during activities such as contact sports, motor vehicle accidents, industrial mishaps, falls, and combat situations. These events can lead to cellular and chemical changes in the brain, disrupting neural pathways and causing symptoms such as headaches, dizziness, cognitive difficulties, and emotional changes. Raising awareness about mTBI and implementing preventive measures to reduce its incidence and mitigate its impact on affected individuals is crucial. Head kinematics measurement is one of the quickest methods for making on-field initial diagnosis decisions. This paper describes the development of an open-source, low-cost, real-time device that can be attached to a helmet. It monitors the head kinematics data (linear acceleration and rotational speed). It sends it over a Wi-Fi connection to a web browser of a monitor device (PC or Mobile phone) connected to the same network so the user who observes the data can call a doctor to check mTBI symptoms. The device utilizes an inertial measurement unit (IMU) and high-g (g = 9.8 m/s²) linear accelerometers interfaced with an Internet of Things (IoT) based microcontroller (WeMos D1 mini) programmed using the Arduino IDE. This setup facilitates data visualisation through an interactive HTML webpage, enabling the user (e.g., coach, medical personnel) to assess the data and potentially recommend seeking medical attention if concerning readings are observed. The application of this device can be in different areas such as road accidents, contact sports, and mine workers.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101863"},"PeriodicalIF":0.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551758","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}