Future Internet of IoT- A Survey of Healthcare Internet of Things (HIoT) : A Clinical Perspective

1. 0
Welcome To Our
Presentation

2. Our Team
Mohammad Piyer Mollah
Supervised by
Dr. Md. Abu Layek
Associate Professor
Dept of CSE, Jagannath University
Future Internet of IoT
CSE5103
M. Shamim Iqbal
Md. Kawsar Hamidi

3. Our Thesis
Paper Analysis
Topic :
A Survey of Healthcare Internet
of Things(HIoT) : A Clinical
Perspective

4. ABOUT THIS PAPER
In combination with current sociological trends, the
maturing development of IoT devices is projected to
revolutionize healthcare. A network of body-worn
sensors, each with a unique ID, can collect health data
that is orders-of-magnitude richer than what is available
today from sporadic observations in clinical/hospital
environments. When data based, analyzed, and compared
against information from other individuals using data
analytics, HIoT data enables the personalization and
modernization of care with radical improvements in
outcomes and reductions in cost.

5. Continue...
Three main technology areas underlie the development of this field:
 Sensing
 Communications
 Data analytics and inference,

6. HIoT can be classified into two sub-categories:
personal and clinical.
Personal HIoT includes devices such as activity/heart
rate trackers, smart clothes and smart watches .
Example :
Apple watch - that are used by consumers for self-
monitoring.
Discuss About Introduction section :
Clinical HIoT devices are built specifically
for health monitoring under the guidance
and with the involvement of a physician.
Example: Smart continuous glucose
monitors and connected inhalers

7. TRENDS, APPLICATIONDEMANDS,AND
CHALLENGES : (1)
The majority of prior surveys on smart healthcare limit their
discussion to specific aspects of the field such as sensing ,
communication , data processing and security .
Technological and Societal Trends :
 Data acquisition
 Data Processing
 Security and Privacy

8. Modern smart health care applications are intricate multi dimensional
systems that not only focus on the personalized acquisition of
physiological data but also incorporate information from various
external sources such as past records of patients from their hospitals,
research and educational resources, and even environmental
information from smart city applications
TRENDS, APPLICATIONDEMANDS,AND
CHALLENGES : (2)

9. Example of HIoT :
 Blood Glucose Monitoring
 Blood Pressure Monitoring
 Stroke Rehabilitation
 Cardiac Monitoring
 Stress Monitoring
 Sleep Monitoring
 Medical AdherenceBlood Glucose Monitoring
Stroke Rehabilitation
Medical Adherence

10. M. Shamim Iqbal
ID: M200305570

11. 1. System Architecture
2. Data Acquisition and Sensing
3. Data Communication, Aggregation and
preprocessing
Contents

12. The realization of inexpensive and reli-able systems
that can meet the requirements discussed in Section
necessitates a robust and inclusive design frame-
work.
In this section, we investigate the general
architecture of the typical HIoT systems and outline
each of the layers. Each layer is then discussed in
more detail in the subsequent sections.
HIoT System Architecture

13. System architecture has four parts:
• Data Acquisition, Sensing, and Transmission
• Data Aggregation/Preprocessing Cloudlet
• Cloud Processing and Storage
• Privacy, Security, and Quality of Service (QoS)
Management
HIoT System Architecture

14. The first HIoT component is data acquisition, where IoT devices and sen-sors
measure physiological and environmental signals. These devices are
connected to a WBAN, generally through an intermediate data aggregator
such as a smartphone. The primary function of the sensors is to sense and
gather data, but many are also now able to preprocess data before
transmission.
Data Acquisition, Sensing, and Transmission

15. • Data Aggregation/Preprocessing Cloudlet
• Cloud Processing and Storage
• Privacy, Security, and Quality of Service (QoS)
• Management

16. How works HIoT System architecture

17. Some of the most commonly used sensors and their
invasiveness, cost, and accuracy
• Activity detection sensors
• Respiration Sensors
• Heart beat monitoring sensors
• Blood pressure sensors
• Blood glucose monitoring sensors
• Wearable multisensors
HIoT Data acquisition and sensing

18. Activity detection is typically conducted through inertial
measurement units (IMUs), which are composed of
multiaxis accelerometers, gyroscopes, magnetometers, and
force sensors. IMUs can be implemented as wearable
sensors or sensors integrated into the environment
.Wearable sensors are typically inexpensive
Activity Detection Sensors

19. • Respiration Sensors
• Heart beat monitoring sensors
• Blood pressure sensors
• Blood glucose monitoring sensors
• Wearable multisensors

21. In this section, we investigate two main aspects of HIoT
communication:
connectivity and
data aggregation.
• Data Communication
• Data Aggregation and Preprocessing: Front-End
• Data Aggregation and Preprocessing: Back-End
HIoT Data communication, aggregation, and
preprocessing

22. A WBAN consists of multiple low-power, resource-constrained devices that are connected to a
more computationally capable device, such as an access point (AP), through a low-range and
low-rate wireless link
Two type of wireless network communicate data
BLE and ZigBee
BLE specifically targets low-rate, low-power, and low-range IoT communications. It operates in
the 2.4-GHz indus-trial, scientific and medical frequency.
Zigbee is a low-cost, low-power, wireless mesh network standard targeted at battery-powered
devices in wireless control and monitoring applications. Zigbee delivers low-latency
communication. Zigbee chips are typically integrated with radios and with microcontrollers.
Data Communication

24. Data Aggregation and Preprocessing: Front-End
For data aggregation and preprocessing, an aggregator is used to collect and
combine all sensed data before transmission. This step also includes performing
preliminary computations on the data. This concept has seen more interest
recently, especially with the introduction of fog computing.
Data Aggregation and Preprocessing: Back-End
Many HIoT applications resort to cloud-based solutions and also to take full
advantage of cloud’s compatibility with off-the-shelf data analytics, always-on
property, scalability, and affordability. Particularly, the cloud can provide
permanent data storage services, which are the basis of data analytics and
inference.

25. Kaiser Ahmed Tonmoy
ID: M200305554

26. ANALYTICS AND INFERENCE
A. Algorithms
B. Data Availability
VISUALIZATION
A. Static Visualization
B. Interactive Visualization
SECURITY AND PRIVACY CONSIDERATIONS
Contents

27. The volume of healthcare data generated in recent years from bio-sensors,
EHRs, computerized physician order entry, social media, and
administrative entries was estimated to be 153 Exabytes in 2013 and is
expected to reach 2000 Exabyte's in 2020 This impressive accumulation of
data in HIoT has created a conducive environment for data analytics and
inference algorithms, which are now used in a variety of healthcare
applications for anomaly detection, prediction of future health events,
early detection of diseases, cost reduction, improved accuracy in clinical
diagnosis, and clinical decision support
Analytics and Inference

28. Algorithms

30. The algorithms described in the previous section require a significant amount of
well-structured clinical data to achieve useful levels of accuracy. Providing such
data presents multifaceted challenges, specifically, with respect to widespread data
availability and heterogeneity.
Data Availability

32. The primary objective of visualization is to present the results of data
analytics and inference algorithms in the form of intuitive tables, charts,
graphs, etc., to enable rapid and intuitive absorption and interpretation
of the patient data by healthcare professionals. Interactive methods
are especially useful when visualizing information that simultaneously
incorporates multidimensional temporal signals and generic static
information.
VISUALIZATION

33. Systems, such as hGraph and its related programming libraries (such
as the one introduced in), provide visualizations by combining in-clinic,
activity, sleep, BP, and nutrition data.
Static Visualization

34. Many applications focus on providing an interactive
environment in their visualization scheme. For example,
Care Cruiser is an interactive system that visualizes the
effects of applying patient treatment plan.
Interactive Visualization

35. System security and data privacy are the highest priorities in a medical
system and should be considered during every phase of the system
design. Additionally, designers must comply with the Health Insurance
Portability and Accountability Act (HIPAA),which mandates that all the
service providers ensure the privacy of their clients. In this section, we
discuss security and privacy considerations for each layer of our
proposed HIoT system.
SECURITY AND PRIVACY CONSIDERATIONS

36. This article reviewed the state-of-the-art in the HIoT technologies, particularly,
focusing on clinical applications of HIoT. It presented HIoT through the lens of
three of its primary components:
1) sensing and data acquisition;
2) communication; and
3) data analytics and inference.
As the underlying IoT technologies in HIoT become more mature, each one of these
three components will independently witness rapid progress within their own
domain. The Data acquisition and sensing technologies will benefit from the future
VLSI technologies that require lower battery power for their operation, while
communication standards will continuously advance to provide higher
communication throughput with decreasing power consumption demands from the
sensing networks.
CONCLUSION

37. CREDITS: This presentation template was created by
Slidesgo, including icons by Flaticon, and infographics &
images by Freepik
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