اینترنت اشیا و داده های بزرگ پزشکی
ترجمه نشده

اینترنت اشیا و داده های بزرگ پزشکی

عنوان فارسی مقاله: چارچوب تجسم Webvr مبتنی بر اینترنت اشیا برای داده های بزرگ پزشکی در سلامت مرتبط
عنوان انگلیسی مقاله: An IoT-Based Framework of Webvr Visualization for Medical Big Data in Connected Health
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی صنایع، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: مهندسی سیستم های سلامت، اینترنت و شبکه های گسترده
کلمات کلیدی فارسی: اینترنت اشیا، پزشکی از راه دور، انتقال تصاعدی سبک وزن، کلان داه پزشکی، تجسم
کلمات کلیدی انگلیسی: Internet of Thing (IoT), telemedicine, lightweight progressive transmission, medical big data, visualization
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2957149
دانشگاه: School of Informatics, Xiamen University, Xiamen 361005, 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: 9
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: E14070
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

ABSTRACT


I. INTRODUCTION


II. REALTED WORK


III. SYSTEM DESIGN


IV. CASE STUDY


V. CONCLUSION


REFERENCES

نمونه متن انگلیسی مقاله

ABSTRACT


Recently, telemedicine has been widely applied in remote diagnosis, treatment and counseling, where the Internet of Things (IoT) technology plays an important role. In the process of telemedicine, data are collected from remote medical equipment, such as CT machine and MRI machine, and then transmitted and reconstructed locally in three-dimensions. Due to the large amount of data to be transmitted in the reconstructed model and the small storage capacity, data need to be compressed progressively before transmission. On this basis, we proposed a lightweight progressive transmission algorithm based on large data visualization in telemedicine to improve transmission efficiency and achieve lossless transmission of original data. Moreover, a novel four-layer system architecture based on IoT has been introduced, including the sensing layer, analysis layer, network layer and application layer. In this way, the three-dimensional reconstructed data at the local end is compressed and transmitted to the remote end, and then visualized at the remote end to show reconstructed 3D models. Thus, it is conducive to doctors in remote real-time diagnosis and treatment, and then realize the data processing and transmission between doctors, patients and medical equipment.


INTRODUCTION


Recently, Internet of Things (IoT) has been widely applied in information technology (IT) which is a concept of connecting physical objects via networks for data collection and sharing. The ‘Things’ in IoT is defined as devices connected to the Internet and able to transmit information to other devices [1]. There are many systems that can be associated with IoT, including green agriculture monitoring system [2], intelligent transportation system [3], environment monitoring system [4], and applications in healthcare industry [5], etc. The connected health model was proposed as an IoT aspect of healthcare, and the applications of connected health are aiming to improve health care services [6]. Medical IoT is considered as a basis of connected health, where the data exchanging is achieved among doctors, patients and medical equipment [7]. Medical IoT can break the regional restriction to doctors, where medical data and case history can be shared. Meanwhile, the real-time monitoring and diagnosis of patients via Medical IoT greatly reduces the cost and time for transporting patients, and improves the cure rate of emergency diseases [8]. The medical big data is defined as a collection based on health-related data which is produced in the entire diagnosis process, from clinic registration to hospital follow-up of patients [9], [10].

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