MEDICAL INTERNET OF THINGS (MIOT) & IMBEDDED INTELLIGENCE IN HEALTHCARE - DR. ABDELBASET KHALAF
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Medical Internet of Things (MIoT) &
Imbedded Intelligence in Healthcare
Dr. Abdelbaset Khalaf
khalafb@tut.ac.za
3rd GFMD-WHO
Medical Internet of Things (MIoT)
rate
• Integra)on of medical devices in a network connec)on
• Network can be managed from the web
• Provide informa)on in real )me
• Communica)on: person to person (P2P) & machine to
machine (M2M)
• Allow interac)on between health professionals & pa)ents
• MIoT can be seen from 3 paradigms:
Ø Internet-oriented middleware
Ø Things sensors oriented
Ø Knowledge-oriented seman)cs
Understand Internet: IP Addressing
IP Addresses connect the Internet
Number / Address
5 RIRs
Internet
Protocol
LIR / ISPs
Number / Address
End Users
6
5
4
3
2
1
0
IPv4 Address Space Issued IPv4 Address Space Issued by year
: RIRs to Customers Fixed length, 32 bit scheme, more than
(Jan. 1999 – Dec. 2008) 4 billion (232) addresses
Source: INR Status Report (NRO, As of 31 Dec. 2008)
by H Zhao ITU
IP Next Generation Protocol
IPv6 Addresses
2128 = 3.40282 x 1038
IPv6
Greatly expanded
address space More attractive for
(2128) future Internet applications
compared to IPv4
Potential socio-economic Multi Access:
beneZits for Enhanced life mobility
ubiquity of the Internet
IPv4: Fixed length, 32 bit scheme, more than 4 billion (232) addressesIPv6 Deployment: Essential for wireless Internet Emergence of mobiles as platform for wireless Internet access especially in developing countries will put more pressure on the IP address space Require a larger IP address space to enable wireless networking & mobility IPv6 protocol provides the availability & extensibility of IP addresses : Large-scale sensor networks, IP Security, Mobile IPv6, IP-based Mul)media IPv6 is emerging as the preferred plaJorm and is a core component of the wireless Internet architecture (3G & Beyond 3G) Internet is now a critical global infrastructure for socio-economic development and growing faster in developing countries
Embedded Intelligence in Healthcare
Insights /
Proximity Act ion
Low Inte llige nce
latency
Technical
De vice Se nsor Integration Quality of
Dat a Experience
Virtualization
Product Improve
Faster time to market monitoring care plans
New markets
Business Embedded Healthcare
Applications
Transformation
Revenue generation Intelligence
Disease Helping
detection doctor A
Cloud
Advance d Analyt ics /
Industry
Collaboration Pat t e rn Re cognit ion/
Standards Classifications/
IoT Applicat ion / Network Opt imizat ion
Dat a St ore
17/05/01 TUT/FSATI
7 2017Embedded Intelligence in M- IoT
• Growth at a high rate exceeding 7%
• Estimated Revenues by 2020 $2.2 trillion
• Healthcare is one of the Leading industries
What is it? How does it help?
Embedded int elligence is the ability of a - Monitor he alth and us age of products
product, proces s or s e rvice to monitor its to e xte nd t he ir performance and
- Ope rational pe rformance , lifetime
- us age load, - Improve marke t appe al and
- e nvironme nt acce ptance of products
- The ability for a s e rvice, s ystem or
Goals: product to be us e d by age ing and people
- e nhance pe rformance and lifetime , with special needs.
- incre ase quality and - Address skills shortages in limited resources
- improve cus tome r s atisfaction. - Enabling ne w re venue opportunities
17/05/01 TUT/FSATI
8 2017Artificial Intelligence (AI) on the Edge Supported
by Fog/Cloud
Edge Fog Cloud
Data in Mot ion Hist oric/ Pre dict ive
Analytics
More
comput ing
Healthcare Devices & Systems
More int e ract ion
and re s pons e
Device t o device
communicat ion
17/05/01 TUT/FSATI 2017
9
10 B RapoluPutting It All Together
17/05/01 TUT/FSATI
10
11 2017Scenario: Intelligent MRI Machines
Onboard Sensors
Imaging System - Captures temperature at various
- MR imaging controls positions on MR Machine
q Scan Dat a
q Syst em
Patient Data
- Body part for scan Failure Logs
- Weight of patient
Data Failure Event Pattern
Magnetic Field Control Matching
- Manual setting of magnetic Acquisition System
Algorithm
field required for scan as
prescribed by the doctor
Predictive Analytics Platform
Other data sources
q Equipment hist ory
q Maint enance records
q Environment al dat a
q Expected failures
q Maintenance Schedules
System Health Dashboard Predic)ve Asset
Optimisation Models
q Predictive Asset Maintenance
Maintenance 17/05/01 TUT/FSATI
11 2017Example: Architecture of System
17/05/01 TUT/FSATI
12 2017Success Requirements: Are we ready?
Watch the outcome of Horizon 2020:eHealth workforce development
• The core of any healthcare system is its workforce
• Healthcare system requires a robust supply of highly skilled
professionals
• And they must be digitally skilled in eHealth
Ø The future state of healthcare depends on workforce with eHealth
skills
Ø How can we address workforce shortage and the lack of access to
skills/competencies in eHealth/health IT?
v We need to map and quan)fy needs & supply, demands & trends for
skills & competencies for all eHealth actors
v The way forward: gap analysis, case studies and stakeholders
engagement to form bigger picture of eHealth workforce
v Development of eHealth/Health IT courses/curriculaCurrent developments and research domains • Body Sensors Network BSN applica)ons • Energy-efficiency for BSNs • Security and privacy for BSNs • BSN system architecture • Interference mi)ga)on in BSNs • Systems enabling pa)ent self-monitoring and assessment • Hardware for BSNs • BSNs with Cloud Compu)ng Capabili)es • BSNs for eHealth and ac)vity monitoring/biomonitoring • BSNs and wearables • Brain-2-Brain Communica)on • BSNs and the Internet of ThingsBrain-2-Brain communica)on • Expert systems for illness diagnosis in limited resources countries
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