We would like to invite you to submit an abstract for oral presentation to the mini-symposium on “Computer vision/Machine Learning for Structural Dynamics & SHM”, organized for th, Dear Colleagues, London, Nov 15 (IANS): University of Cambridge engineers has developed a computer vision technology into a free mobile phone app for regular monitoring of glucose levels in people with diabetes. Human Health Monitoring Based on Computer Vision has gained rapid scientific growth in recent years, with many research articles and complete systems based on set of features, extracted from face and gesture. Good condition of infrastructure facilities ensures the safety and economic well-being of society. low frequency and amplitude), I designed, built, and tested the novel wireless, MEMS-based accelerometer sensor b, To address the limitations of current sensor systems for field applications, the research community has been actively exploring new technologies that can advance the state-of-the-practice in structural health monitoring (SHM). Such structural dynamic measurements further facilitate many structural assessment procedures such as system/modal identification, damage detection, force estimation, and model validation and updating. see link: http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=4147. "When you look at a screen, you're so involved that you forget to blink. In the past two decades, a significant number of innovative sensing and monitoring systems based on the machine vision-based technology have been exploited in the field of structural health monitoring (SHM). Significant advantages of the vision sensor include its low cost, ease of setup and operation, and flexibility to extract displacements of any points on the structure from a single video measurement. It is believed that identified modal parameters can be a better substitute for model updating, system identification, and detect damages, etc., as the vision sensor can achieve smoother mode shapes while the resolution of mode shapes from accelerometers is limited by the sensor number. Here damage is defined as changes to the material and/or geometric properties of a structural system, including changes to the boundary conditions and system connectivity, which adversely affect the system's performance. concept, SHM with smartphones, is focused to utilize multisensory smartphone features for a hybridized structural vibration response measurement framework. Results showed the sensor board’s capability in measuring sub-Hertz vibrations having amplitude on the order 10-2 m∙s-2 with the same accuracy of wired, high-sensitivity, integrated electronics piezoelectric (IEPE) sensors. Application in Model Updating of Railway Bridges under Trainloads, 6. Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring (Struct Control Hlth) Link. Computer Vision has been promising in identifying any cancer cells during a cancer screening process. New computer vision technology developed into a free mobile phone app can monitor glucose levels in people with diabetes. Computer Vision-based Descriptive Analytics of Seniors’ Daily Activities for Long-term Health Monitoring. Compared with conventional sensors, computer vision sensors are far more cost-effective and agile to set up, and provide significantly higher spatial-density measurements where each pixel could represent a measurement point. Computer Vision for Health Monitoring By leveraging computer vision technology doctors can analyse health and fitness metrics to assist patients to make faster and better medical decisions. INTRODUCTION AND SYSTEM OVERVIEW The deterioration of the civil infrastructure in North America, Europe and Japan has been … Currently, computer vision sensing has been drawing attention and gaining popularity in two major areas: (1) vision-based sensors for dynamic response measurement and their SHM applications for modal/parameter identification, damage detection, force estimation, and model validation and updating; and (2) visual monitoring for structural surface defect detection and condition assessment. A computer vision technology developed by University of Cambridge engineers has now been developed into a free mobile phone app for regular monitoring … Below is summary of the mini-symposium: Computer vision, an AI technology that allows computers to understand and label images, is now used in convenience stores, driverless car testing, daily medical diagnostics, and in monitoring the health of crops and livestock. Computer vision describes the process when a computer using artificial intelligence algorithms can identify and process images (photos, videos, etc.) Development of accurate vision-based displacement sensors; In this study, a recently introduced SHM, This paper presents an innovative structural health monitoring (SHM) platform in terms of how it integrates smartphone sensors, the web, and crowdsourcing. Provides comprehensive coverage of theory and hands-on implementation of computer vision-based sensors for structural health monitoring. Crowdsourcing has given rise to citizen initiatives becoming a vast source of inexpensive, valuable but heterogeneous data. Achievements, Challenges, and Opportunities, About ASME Conference Publications and Proceedings, ASME Press Advisory & Oversight Committee. Please don’t hesitate to let us know if you have any questions. Computer Vision and Deep Learning-based Data Anomaly Detection Method for Structural Health Monitoring (Struct Health Monit) Link Computer vision and deep learning–based data anomaly detection method for structural health monitoring Yuequan Bao, Zhiyi Tang, Hui Li, and Yufeng Zhang Structural Health Monitoring 2018 18 … The app uses computer vision techniques to read and record the glucose levels, time and date displayed on a typical glucose test via the camera on a mobile phone. This information is Previously, the, Join ResearchGate to discover and stay up-to-date with the latest research from leading experts in, Access scientific knowledge from anywhere. The book also features a wide range of tests conducted in both controlled laboratory and complex field environments in order to evaluate the sensor accuracy and demonstrate the unique features and merits of computer vision-based structural displacement measurement. Zelun Luo*, Jun-Ting Hsieh*, Niranjan Balachandar, Serena Yeung, Guido Pusiol, Jay Luxenberg, Grace Li, Li-Jia Li, N. Lance Downing, Arnold Milstein, Li Fei-Fei. Chuan-Zhi Dong, F Necati Catbas Structural Health Monitoring. Provides comprehensive coverage of theory and hands-on implementation of computer vision-based sensors for structural health monitoring. Its use cases are video surveillance, self-driving car testing, daily medical diagnostics, and monitoring the health of crops and livestock. The increased use of computers in the workplace has brought about the development of a number of health concerns. We present our learnings from building such models for detecting stem and wheat rust in crops. This book offers comprehensive understanding of the principles and applications of computer vision for structural dynamics and health monitoring. Computer Vision in AI: Modeling a More Accurate Meter. Although some research efforts have been directed toward computer vision-based safety and health monitoring, its application in real practice remains premature due to a number of technical issues and research challenges in terms of reliability, accuracy, and applicability. In this paper, integrated use of video images and sensor data in the context of SHM is demonstrated as promising technologies for safety and security of bridges. Other new emerging vision-based sensing technologies. An iOS application was developed to enable citizens to measure structural vibration and upload the data to a server with smartphones. Performance Evaluation Through Laboratory and Field Tests, 4. At the same time, due to the continuous development, rising wealth of the society and socio-economic integration of countries, the number of infrastructural objects is growing. Autonomous Nurses (http://emi2019.caltech.edu/). The level of discomfort appears … London, Nov 15 : University of Cambridge engineers has developed a computer vision technology into a free mobile phone app for regular monitoring of glucose levels in people with diabetes. Both original research articles and reviews are welcome. New computer vision technology developed into a free mobile phone app can monitor glucose levels in people with diabetes. Double-integration or differentiation among different measurement types is performed to combine multisensory measurements on a comparative basis. A review of computer vision–based structural health monitoring at local and global levels. Application in Simultaneously Identifying Structural Parameters and Excitation Forces, 8. The results of these tests show a novel and successful implementation of a hybrid motion sensing platform through multiple sensor type and device integration. The app uses computer vision techniques to read and record the glucose levels, time and date displayed on a typical glucose test via the camera on a mobile phone. ResearchGate has not been able to resolve any citations for this publication. The technology, which doesn’t require an internet or Bluetooth connection, works for any type of glucose meter, in any orientation and in a variety of light levels. The computer vision comes into play by recognizing all the medication and then reconciling that with the patient’s electronic health record.
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