On the basis of a coherent technological infrastructure operators of wireless and wired communications grows up the fragments of the global Internet of Medical Things (IoMT). Each fragments that focuses on acquisition and processing of biometric data is local telebiometrics system.

1. Internet of Medical Things – Technological
Environment of Personalized/Precision Medicine
Alexandre Prozorov, #mHealthLab
Laboratory of special medical equipment and technologies of MIPT
29.10.2015, 6-th Moscow Supercomputer Forum
1
MIPT
IBMP

2. What is Personalized Medicine?
The first era – FIGHT WITH INFECTIONS, INJURIES AND THEIR CONSEQUENCES
Ancient medicine - currently
• Development of surgery and therapy. Infections disease control (vaccination)
The second era – FIGHT WITH CHRONIC DISEASE
50 yy. ХХ century - currently
• Successful treatment of cardiovascular, cancer and socially significant diseases,
increased focus on the treatment of psychosocial and psychiatric diseases (obesity,
alcoholism, drug addiction, smoking, etc)
The third era – THE PRESERVATION AND MAINTENANCE OF HEALTH
Currently
• Personalized medicine – a new model of organization of medical care, based on the
selection of diagnostic, therapeutic and preventive tools that are optimal for a particular
patient, taking into account its genetic, physiological, biochemical, behavioral and other
characteristics
• Personalized medicine involves close integration of information technology, science and
clinical treatment to achieve the best clinical or preventive results
• Therefore, for the organization of personalized medicine requires close interaction doctor-
patient relationship is not only in the clinic but also in everyday life (by analogy with the
coaches and athletes)
2
#mHealthLab

3. Internet of Medical Things (IoMT)
What is IoMT? Tasks,
logic
levels,
protokols
and
architecture
of
telebiometrics
systems
3
#mHealthLab
MIPT
IBMP

4. What is IoMT?
#mHealthLab
Internet
of
Things
Medical
Devices
IoMT
4

5. Tasks of IoMT-systems
Each
fragments
that
focuses
on
acquisi>on
and
processing
of
biometric
data
–
is
local
telebiometrics
system,
which
aims:
• To
increase
the
level,
resolu>on
and
compa>bility
of
bio-­‐
quan>fica>on
• Using
the
standardized
interna>onal
system
of
measurement
of
biosignals
• To
deploy
a
standardized
encryp>on
method
from
each
node
in
the
collec>on
of
biometric
data
to
the
cloud
• To
ensure
confiden>ality
and
availability
of
biometric
data
on
demand
from
anywhere
#mHealthLab
On
the
basis
of
a
coherent
technological
infrastructure
operators
of
wireless
and
wired
communica>ons
grows
up
the
fragments
of
the
global
Internet
of
Medical
Things
(IoMT)
5

6. Logical levels of IoMT-systems
1. The
biological
target,
is
in
direct
contact
with
the
sensor
and
exposed
to
measurement
2. The
sensor,
is
designed
to
receive
(removal
rate)
of
biometric
data,
including
the
search
for
and
iden>fy
paNerns
in
the
recorded
analogue
and
digital
signals.
The
sensor
is
integrated
into
the
network
infrastructure
cloud
3. The
Protocol,
is
intended
for
preliminary
processing
and
transmission
of
biometric
data
to
the
cloud
applica>ons.
Its
main
tasks
are
interpreta>on,
quan>ta>ve
comparison
and
analysis
of
biological
and
measurement
values
of
measured
data
4. Cloud
applica@on,
is
the
recipient
biometric
data
and
performs
core
applica>on
tasks
according
to
their
recogni>on,
visualiza>on,
analysis,
comparison,
recommenda>ons,
etc.
5. Cloud
storage
of
biometric
data,
is
intended
for
accumula>on
and
long-­‐term
storage
of
data,
provides
the
proper
level
of
security,
availability
and
support
for
different
access
protocols
6
Data
Hub
PHR
rSO2
BCG
ECG
Temp
SpO2
MicPatient
Patient
Monitoring
Physician
Level 4
Level 1 Level 2
Level 3
Level 5

7. Potential borders of IoMT-ecosystems (schematically)
#mHealthLab
Categories
of
telebiometrics
applica@ons
Consumer
segment
Banking
+
digital
signature
Media
+
Social
networks
Wearable
devices
Saving
energy
+
Environmental
Monitoring
Physical
security
Gaming
+
Cameras
(video
/
photo)
Automobiles
Avoiding
collisions
Driver
recogni>on
Voice
recogni>on
Medicine
and
Healthcare
Monitoring
of
pa>ents
in
the
clinic
Monitoring
of
pa>ents
in
the
home
Mobile
monitoring
of
health
indicators
Tests
at
home
or
in
the
laboratory
Bio-­‐banks
Agriculture
Smart
farm
Livestock
management
Precision
agriculture
Monitoring
of
the
epidemiological
situa>on
Aero
Managing
the
drones
Monitoring
of
the
surrounding
space
Fellowship/in-­‐flight
entertainment
(pilot/
passenger)
Legal
issues
Registra>on
of
stay
Registra>on
of
firearms
Security
Iden>ty
management
Surveillance
Physical
access
control
Monitoring
of
convicts
Monitoring
of
popula>ons
7

8. mHealth and IoMT-infrastructure
Preconditions, stakeholders, the notion of medical care,
architecture, data flows, technological stack, and features of
systems of mHealth
8
ЦЖС МФТИ
ИМБП РАН
#mHealthLab

9. mHealth Economic Preconditions
#mHealthLab
9
Preventable complications
Unnecessary procedures
Inefficiency
Mistakes
Positive
outcome
30-40%
losses
60-70%
benefit
Positive
outcome100%
benefit
2020
2015 When
the
pa@ent
is
willing
to
pay?

10. mHealth stakeholders
#mHealthLab
10
Physician
?
Insurer
?
Patient
?
mHealth
Why?
That it (medication,
manipulation,
treatment) gives me?
What's going on?
How it (medication,
manipulation, treatment)
affects of the patient?
What will happen?
How it (medication,
manipulation, treatment) will
affect health insurance?
Scientist
?
How it works?
How it (medication,
manipulation, treatment) works
in different conditions?

11. The concept of medical care in mHealth*
#mHealthLab
*
According
to
«mHealth:
From
Smartphones
to
Smart
Systems»
11
Patient
The convenience and
cost reduction in the
treatment
(in clinic or at home)
Support of decisions
of the doctor
Coordination of
treatment or
rehabilitation
Involve patients in the
process of treatment
or rehabilitationManagement of a course of treatment
(rehabilitation)
Management of
monitoring of
patients
Prevention and
rehabilitation
Hospitalization, ambulance or
high-tech medical aid
Personal
communications
Remote
monitoring
Diagnosis
Training courses
and coaching
Representation of
interests
(for the insured,
employee, etc.)
Factual information
about the patient's
condition
Data flow to PHR/
EHR
Devices
Infrastructure

12. The business models of the mHealth-operator
12
Business
model
of
mHealth
Service
level
Variants
Comments
BRANDED
SERVICE
L7
В2С
Direct
info-­‐medical
services
to
clients
L6
В2С2B
Direct
informa>on
and
communica>on
monitoring
services,
exper>se
through
partners
BRANDED
PLATFORM
L5
В2В2С
PHR
plalorm
+
own
IoMT
devices
and
applica>ons.
All
services
through
partners
L4
В2В
PHR
plalorm
("engine")
for
service
providers
CONNECTIVITY
L3
В2В2С
Aggrega>on
of
services
of
various
service
providers
of
IoMT
-­‐
a
market
плейс
+
tariffs
+
billing
L2
В2С
Provision
of
basic
communica>on
services
for
health
monitoring
services
L1
В2В
Provision
of
basic
communica>on
services
for
medical
ins>tu>ons

13. #mHealthLab
13
# mHealth Applications Variants Benefits
1 Control of chronic diseases Wearable monitors Anticipatory manipulations
2 Observance of a course of
treatment
Reminders and alarms by means
of messages, email, mobile
applications
Increase patient satisfaction
3 Remote patient monitoring System of tracking of location and
safety of the patient
Reducing the cost of treatment
4 Access to health information Electronic health records (PHR/
EHR)
Moving to a nursing home without loss of medical
information
5 Interactions between
physicians and other medical
personnel
Social networks, based on the
Web
The increased share of self-government
6 Individual program for
rehabilitation and fitness
System for monitoring diet,
physical activity, quality of life,
based on Web technologies
• Improvement of health and rehabilitation
• The increased quality of life
• Reducing the burden on family members and health care
staff
• Better interaction between doctor, patient, family and staff
for better care
mHealth: examples of applications and their results

14. The architecture of mHealth systems (schematically)
#mHealthLab
14
100% mobility
In clinic or home
IoMT-frontend
IoMT-backend
IoMT applications M2M networks IoMT devices
App Backend
BioData
Storage
HL7 Gateway
Patients
monitoring
Courses of
treatment
Сhronic
diseases
Physician
Scientist
Rehabilitation
and fitness
Corporate
Social nets
PHR/EHR
Data hub
Smartphone
Patient
Patient
Satellite
segment
Mobile
segment
Wired and
wireless
segments
WBAN
- IEEE 802.15.6
- ZigBee / IEEE 802.15.4
- Bluetooth, Bluetooth LE
- Wireless USB
- Proprietary solutions
(ANT, Sensium, Zarlink,
Z-Wave)
Access networks
- GSM, UMTS
- LTE, LTE-A
- WiMAX
- WLAN
- Satellite
Insurer
Patient

15. The data flows of mHealth systems
#mHealthLab
15
Patient
WBAN M2M net
mHealth
operator
Clinic
Cloud solution
Diet and
lifestyle
Fitness
PHR Physician
Hospital Information
System
Patient
monitoring
Courses of
treatment
Management of
chronic
diseases
Rehabilitation
EHR
Implantable
medical devices
On-body (tattoo,
sticker) medical
devices
Wearable medical
devices
Stationary medical
devices
Data
hub
Scientist
Patient
Insurer

16. Technological stack of mHealth systems (tasks)
#mHealthLab
16
Core Network
IoMT
IoMT Device
Sensor
Primary signal
processing
WBAN transmitter
IoMT Data Hub
WBAN receiver
Semantic signal
processing
M2M transmitter
M2M net
WLAN/ Ethernet/
PSTN/ Cellular/
etc
QoS for realtime
IoMT traffic
mHealth
Operator
IoT Middleware
M2M receiver
Decoding and data
aggregation
Moving data on
storage
IoMT Platform
Long term data
retention
The search for
patterns and
generate events
Providing data on
demand
Clinic
IHE Components
Integration to
mHealth operator
cloud
EHR
Modellind and
Machine Learning
Modelling
Platform
Machine
Learning Tools
Analitycs
Analitycs
Platform
Visualization
Tools

17. Technological stack of mHealth systems (solutions)
#mHealthLab
Hadrware IoT Middleware IHE
Components
Modelling and
ML Tools
Analitycs and
Visualuzation
HW (inc. WBAN)
Hardware platform:
- Renesas
- Texas Instrumets
- Microchip
- STM
- Arduino (Amtel)
- Raspberry, etc
Transport wireless protocols:
- IEEE 802.15.6
- ZigBee / IEEE 802.15.4
- Bluetooth, Bluetooth LE
- etc
Middleware and
Platforms
IoT Middleware:
- OpenRemote
- OpenHAB
- iotsys, etc
IoMT Platforms:
- MS HealthVault
- Google Health
- Qualcomm Life 2net, etc
M2M Protocols
App. Level
Protocols
Encoding:
- CSV, JSON, XML
- BSON, Message Pack
- Protocols Buffers
M2M communications:
- MQTT
- MQTT-SN
- AMQP
- CoAP
- HTTP
Platforms
Interoperability:
- Mirth Connect
- eTransX
- HL7 Interface Engine, etc
EHR:
- OpenEMR
- FreeMED
- OpenMRS, etc
Frameworks and
Platforms
ML Frameworks:
- scikit-learn
- shogun
- MLlib, etc
Platforms:
- R + RStudio
- Matlab
- Spark, etc
Libraries and
Platforms
Charting libraries:
- D3.js
- Chart.js
- Highchart.js, etc
Analitycs Platforms:
- Tableau
- QlikView
- Omniscope, etc
17

18. Specifics of IoMT-devices
#mHealthLab
Many
IoT
devices
generate
personal
data,
protected
by
152
Federal
Low.
However,
with
IoMT
devices
is
much
more
complicated:
• IoMT-­‐medical
devices
generate
data
that
is
most
sensi>ve
to
compromise
• Breaking
and
unauthorized
use
of
IoT-­‐devices
can
lead
to
death
or
problems
with
health
of
the
owner
• Interest
of
malefactors
in
blackmail
and
extor>on
by
means
of
a
compromise
of
IoMT-­‐devices
with
high
probability
in
the
long
term
will
lead
through
3-­‐5
years
to
the
"black"
market
of
the
corresponding
criminal
services
(by
analogy
with
the
botnets
market)
• FSB
is
necessary
with
coordina>on
to
the
interna>onal
ins>tutes
of
standardiza>on
as
soon
as
possible
to
begin
work
on
standardise
and
cer>fica>on
of
reliable
mechanisms
of
protec>on
of
the
IoMT-­‐devices
applicable
in
the
territory
of
the
Russian
Federa>on
Nanoribbon
Heart
Implant
18

19. MedCore – medical grade stickers and non-
contact IoMT-devices
Solvable problems, nomenclature and options for the use of IoMT-
devices, the variants of mHealth systems architecture
19
ЦЖС МФТИ
ИМБП РАН
#mHealthLab

20. Solvable problems
#mHealthLab
MedCore
is
an
integrated
set
of
IoMT-­‐devices
and
complementary
sotware
to
build
complex
medical
or
telebiometrics
solu>ons
in
the
following
areas:
• Medicine
(treatment
of
chronic
pa>ents,
monitoring
of
pa>ents…)
• Rehabilita>on
(the
care
of
newborn
infants,
bedridden
pa>ents…)
• Sports
and
fitness
(tracking
indicators,
the
>ming
of
the
training…)
• Healthy
lifestyle
(the
>ming
of
the
sleep,
control
snoring…)
• Intensive
produc>on
processes
(health
monitoring
operators,
managers,
fighters…)
MedCore
aimed
to
comprehensive
solu>on
of
problems
of
biometric
monitoring
of
health
indicators
in
real-­‐>me
and
>mekeeping
of
the
human
condi>on
with
medical
precision
20

21. #mHealthLab
21
# IoMT-device/ software The principle of operation Logic level
1 Non-contact BCG-sensor Mechanical vibrations of a fragment of a body placed over
the sensor
Sensor
2 Sensor-sticker single-channel ECG Fluctuations of the electric potential taken with the skin in the
chest area
Sensor
3 Sensor-sticker wideband microphone Sound vibrations taken from the skin in the chest or
abdomen
Sensor
4 Sensor-clip SpO2 The fluctuations of transparent ability of skin measured in
area of fingers, an auricle
Sensor
5 Sensor-sticker body temperature The skin temperature at the chest, abdomen Sensor
6 Sensor-sticker movement and body
position (3D)
Mechanical oscillations and the position of the chest,
abdomen, back, arms and legs
Sensor
7 Data hub (device) Acquisition biometric data from sensors, filtering and
semantic analysis of data and transfer clear data to the cloud
Protocol
8 API for smartphone (Android, IOS) Acquisition biometric data from sensors, filtering and
semantic analysis of biometric data
Protocol
9 API for cloud solutions (Linux, Windows) Acquisition biometric data from data hubs and smartphones Protocol
MedCore: the range of devices and software

22. #mHealthLab
22
# IoMT device Usage scenario
1 Non-contact BCG-sensor • Chronometry of sleep
• Chronometry of bed rest
• Registration apnea
• Registration of seizures
• Measurement of basic vital indicators of the person in a lying position
• Measurement of stress and fatigue the operator (driver, pilot, etc.) in a sitting position
2 Sensor-sticker single-
channel ECG
• Cardiac monitoring during the day
• Cascadable for multi-channel devices record ECG including Holter monitoring
• Measurement of stress and fatigue the operator (driver, pilot, etc.) in a mobile position
3 Sensor-sticker wideband
microphone
• Listening to the fetal heart (for pregnant women)
• The definition of extraneous noise during breathing
• Determination of the respiration rate during the day
• Determination of the intensity of environmental noise
4 Sensor-clip SpO2 • Determination of hemoglobin saturation of arterial blood
5 Sensor-sticker body
temperature
• Definition of body temperature
6 Sensor-sticker movement
and body position
• Determining the position of a body
• Determining movement of the body
• Cascading devices for registering 3D-BCG
7-9 Data hub, API • The collection of bio data from the sensors, data transmission in IoT-Middleware or mHealth-cloud
• Semantic processing of «raw» bio data
MedCore: options for using devices and software

23. Creation of mHealth-system on the basis of Open mHealth (schematically)
#mHealthLab
23
Phar
macy
Inven
tory
ETL
EHR
REST
Mobile
HL7
GATE
WEB
HIS
3D
Data
Hub
BCG
ECG
Temp
IoMT devices M2M net Clinic
SpO2 IT-systems
of clinic
Open mHealth
componentsMedCore
devices
The devices used
in the clinic or at
home

24. Creation of the IoTM-system based on OpenHAB (schematically)
#mHealthLab
24
xPL
KNX
Add-
ons
Core
REST
Mobile
Event
Bus
WEB
VSCP
3D
Data
Hub
BCG
ECG
Temp
IoMT Devices M2M net
OpenHAB
Cloud
SpO2
The OpenHAB
components for
integration from
Smart-devices
(locally in the house)MedCore
components
Mic
Smart
phone
Persis
tence
Event
Bus
OpenHAB smart
Interfaces
RS-
232
The OpenHAB
components in a
public cloud
The devices used
in the bedroom
The devices used
during sport
activities

25. 25
PROZOROV Alexandre
Research associate of Laboratory of special medical
equipment and technologies of MIPT
Research associate of the Innovative center of space
medicine of IMBP Russian Academy of Sciences
CEO of "Mobile Health Lab”
Email: ap@mhealthlab.ru
Mobi: +7 916 9989619
Have questions? Ask!
#mHealthLab