مدیریت ایمنی و شناسایی مجدد بیماران
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مدیریت ایمنی و شناسایی مجدد بیماران

عنوان فارسی مقاله: استنتاج توپولوژی منطقی مبتنی بر داده برای مدیریت ایمنی و شناسایی مجدد بیماران از طریق اینترنت اشیا (IoT) چند دوربینه
عنوان انگلیسی مقاله: Data-Driven Logical Topology Inference for Managing Safety and Re-Identification of Patients Through Multi-Cameras IoT
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: اینترنت و شبکه های گسترده، شبکه های کامپیوتری
کلمات کلیدی فارسی: تجزیه و تحلیل همبستگی کانونی، اطلاعات متقابل با تاخیر زمانی، شبکه عصبی پیچشی عمیق، استنتاج توپولوژی چند دوربینه
کلمات کلیدی انگلیسی: Canonical correlation analysis, time delayed mutual information (TDMI), deep convolutional neural network (DCNN), multi-camera topology inference
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2951164
دانشگاه: School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 13
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13971
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Related Work


III. Method


IV. Experiments


V. Analysis


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


As Internet of Things (IoT) develops, IoT technologies are starting to integrate intelligent cameras for managing safety within mental health hospital wards and relevant spaces, seeking out specified individuals from these surveillance videos filmed by the various cameras. Because monitoring is one of the important application of IoT based on distributed video cameras. In order to fine-grained re-identification of patients and their activities against the very low resolution, occlusions and pose, viewpoint and illumination changes, we propose a novel data-driven model to infer multi-cameras logical topology and re-identify patients captured by different cameras. In our model, we employ a Time-Delayed Mutual Information (TDMI) model in order to address multi-cameras logical topology inference. Additionally, we use a welltrained Deep Convolutional Neural Network (DCNN) to extract characteristics. Moreover, we employ a name-ability model to discover deep attributes and a classifier based on a structural output of attributes is designed to tackle the re-identification of patients, especially who possess psychiatric behaviour. In order to improve the present model’s performance, we resort to the parallelized implementations. Experimental results show that our model possesses the best performance as compared to state-of-the-art model,especially, when the semantic restrictions are imposed onto the production of patients’ specific attributes with structural output. Further, the deep learning model is used to produce characteristics when there is no supervision on the learning model of attributes.


Introduction


From last two decades, videos surveillance systems have been used for managing safety within healthcare industry especially in mental health hospital, asylum, seclusion room, and wards for controlling the suspicious activities of psychotic patients. Such as spotting suspicious behavior, violence, hyperactivity, and safety issues. Further, these issues lead to treatment-related harms caused by medical care of patient and staff. Additionally, those adverse events also lead to harm caused by errors, medical treatment errors, potential for harm. further, multi-cameras monitoring surveillance system can sort the problems related to managing medical care protocols [1]–[3]. Health monitoring research leads to significant focus to improve and achieve good healthcare environment. The analytical evaluation of mentioned issues and analyzing their causes can help in healthcare planning and design advance smart health care system too. Unfortunately, most hospitals and health systems have rudimentary methods to identify this issues or activities. These rudimentary methods mostly depend on the use of voluntary help, chart-reports results, and scanning of manually observed behavior of patient. These methods face challenges and limitations those lead them to accidents. Further, these methods required human resources as well. Detecting these events is especially challenging because of the unpredictable nature of patients. Along with powerful re-identification features, this paper proposes a model to improve patient safety in hospitals through the usage of Multi-Cameras IoT.

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