This slide is based on Diabetes Diagnosis based on IoT

1. PRESENTED BY:
DEEPTANU DATTA
ROLL NO. 1811EE05
A
PRESENTATION
on
5G Smart Diabetes System
Guided by :
Dr. Preetam Kumar
Associate Professor
Department of Electrical
Engineering
IIT Patna
Indian Institute of Technology Patna
6 April 2019
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2. Outlines
 Introduction
 What is smart healthcare ?
 5G in Healthcare
 Diabetes
 Drawbacks with the present diagnosis system
 5G Smart Diabetes System
 Advantages of 5G Smart Diabetes System
 5G Smart Diabetes System Architecture
 Three Layers of 5G Smart Diabetes System
 Effective Data Sharing Mechanism
 Conclusions
 References
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3. Smart Healthcare
 Smart Healthcare refers to use of smart devices and
smart healthcare sensors for better diagnosis and
improved treatment of patients
 With the rise in population, chronic diseases like
diabetes, asthma, obesity, arthritis etc. are more
spreading now a days
 So, there is a need for effective smart healthcare
monitoring system
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4. Smart Healthcare System
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5. 5G in Healthcare
 Transmits large size images very quickly
 Language translation through video conference
 Real-time remote monitoring of patients through IoT
devices
 Expansion of Telemedicine
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6. Diabetes – a chronic disease
 Diabetes is a very common chronic disease that
affects 8.5 % of world’s population.
 Statistics shows that nearly 422 million people
suffers from diabetes per year
 It arises when the level of blood glucose in the body
increases
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7. Drawbacks with present diagnosis system
 System is not reliable
 Real-time data collection is difficult
 Lacks in continuous monitoring of patients
 Lacks in data sharing mechanism and personalized
data analysis
 There are no continuous suggestions for the
prevention and treatment of diabetes
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8. IDEA : INTEGRATION OF MODERN
TECHNOLOGIES, LIKE 5G MOBILE
NETWORKS, BIG DATA NETWORK,
MACHINE LEARNING, SMART
CLOTHING, MONITORING DEVICES
Solution : 5G-Smart Diabetes
System
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9. Advantages of 5G Smart diabetes system
 Low cost : Facilitates out of hospital treatment so
cost of treatment is reduced
 Comfort : It ensures that daily activities of the
patients’ is not disturbed
 Personalization : By collecting blood glucose data
through machine learning algorithm, it personalized
treatment for patients
 Smartness : Early detection of the disease is possible
with this system
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10. System Architecture
Personalized
Diagnosis
Layer
Data Sharing
Sensing
Layer
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11. Sensing Layer
 It senses physical information of the patient by
sensors
 Blood sugar is collected by blood glucose monitoring
device
 Body signals, like temperature and blood oxygen are
collected on real-time basis by smart clothing
 Data about physical activity of the patients, kcal
burned in exercise is collected by smart phone.
 All the data are uploaded to healthcare big data
cloud by 5G Network
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12. EFFICIENT MODELS ARE BUILD TO
ANALYSE AND PREDICT DIABETES BY
MACHINE LEARNING ALGORITHMS
Personalized Diagnosis Layer
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13. Data Sharing Layer
 It consists of social space and data space
 Social space is derived from social relationships
among patients, friends, personal health advisors,
and doctors
 Data space is constructed on the basis of different
patients’ data stored in different clouds
 Motive is to share data in these spaces efficiently in
terms of cost
 Patients living in nearby areas share data in same
cloud so cost of communication is less and vice-versa
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14. System Architecture of 5G Smart Diabetes
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15. Effective Data Sharing Mechanism
 Let D = (dij)n x n denotes data distances
between patient i’s data & patient j’s data in
cloud
 ‘n’ is the number of diabetes patients
 ‘D’ determines data sharing cost
 More is the distance between clouds in which
patient i and patient j store data, more is the
value of dij
 If both patients store data in same cloud, dij = O
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16. Effective Data Sharing Mechanism (Contd…)
 Let W = (wij)n x n denotes social relationship between
patient ‘i’ and patient ‘j’
 If patient ‘i’ and patient ‘j’ knows each other very
closely, ‘wij’ is large.
 If two patients do not know each other, wij = O
 Two patients share data if they knows each other
very well (Large wij)
 So, main objective is to maximise data sharing (high
W) with minimum communication cost (low D)
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17. Data Sharing and Personalized Analysis Model
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18. Results of Case Study
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19. Conclusions
 There is a high demand of 5G Network in healthcare
monitoring system
 5G Smart Diabetes System almost eliminates the
drawbacks with the present diagnosis system
 Patients can be continuously monitored as doctors
can access his data from big data cloud
 All these are achieved with very less suffering and
pains unlike in the case of hospital
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20. References
[1] Min Chen, Jun Yang, Jiehan Zhou, Yixue Hao, Jing Zhang, Chan-
Hyun Youn, “5G-Smart Diabetes : Toward Personalized Diabetes
Diagnosis with Healthcare Big Data Clouds” IEEE Communications
Magazine, April 2018
[2] https://www.technavio.com/blog/top-5-healthcare-technologies-
changing-global-smart-healthcare-market
[3] Min Chen, Yujun Ma, Yong Li, Di Wu, Yin Zhang, and Chan-Hyun
Youn, “Wearable 2.0: Enabling Human-Cloud Integration in Next
Generation Healthcare Systems” IEEE Communications Magazine,
January 2017
[4] Kazem Sohraby, Daniel Minoli, and Taieb Znati, “Wireless Sensor
Networks - Technology, Protocols, and Applications” WILEY Publishers
[5] https://www.business.att.com/learn/updates/how-5g-will-transform-
the-healthcare-industry.html
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21. THANK YOU
6 April 2019