1. Mobile monitoring applied to the chronic diseases
An expandable multisensor platform
eHealth Day Sierre, 6. June 2014
Awarded by the European Commission
as Europe's ´best eHealth SMEs´ 2013

2. Overview
June 2014 Proprietary Information Biovotion 2
Wearable
monitoring
Biophysics &
Physiology
Sensors &
Algorithms
Actionable
interface
Attachment
Markets &
Applications

3. From hospital care to home care
June 2014 Proprietary Information Biovotion 3
 Tight monitoring analogue to hospital
 Adequate «infrastructure»
 Continuous data
 Integration into existing «ICT» solutions
Hospital admission
Intensive hospital care
Non-critical hospital care
Patient home care

4. Example COPD
 G7 >34M COPD patients*, becoming 3rd leading cause of
death. Economic burden >$40B (NIH)
 ~20% of all acute hospital admissions, 24% readmission rate
 7.5% of COPD patients with major handicap in every day life
 Medical treatment limited, reduced level of function, inactivity,
frustration and social isolation >40% CVD
* WHO (2010)
Proprietary Information Biovotion 4June 2014

5. Fully
wearable,
continuous
& portable
medical
device
Simple
wearable
consumer
devices
Simple
portable
medical
devices Complex
stationary
Medical
Devices
Market developments
5
Typically spot monitoring
‘Moderate’ accuracy
Limited selection of vital signs
Ergonomic focus
‘Lower’ accuracy
Full range of vital sign parameters
Sophisticated algorithms
Reduced movement
‘High’ accuracy
Combine ergonomy/pricing/accuracy and
mobility towards new level of wearable
monitoring devices incl. eco system
June 2014 5Proprietary Information Biovotion

6. VSM 1-3: Parameters today
June 2014 Proprietary Information Biovotion 6
VSM1 (6 sensor signals) - Main vital signs**
 Heart rate
 Blood oxygenation
 Cutaneous blood perfusion/volume
 Temperature
 Movement
Additional parameters***
 Heart rate variability
 Energy expenditure
 Respiratory rate
 Stress
 Sleep
 Fall
VSM2 (13 sensor signals) - to include water
VSM3 (19 sensor signals) - to include glucose
*** Extensive IP portfolio existing, device shown above features a total of 19 different sensor signals
*** Performance on par with standard hospital systems
*** Expected to be part of VSM 1
*

7. Ecosystem propositions
Core
Portal Sensor
Person
ProviderPayer
Core
Portal Sensor
Person
ProviderPayer
Core
Portal Sensor
Person
ProviderPayer
Core
Portal Sensor
Person
ProviderPayer
«Consumer»
«Corporate Health» «Captive/Capitation»
«Additional Health»
Proprietary Information Biovotion 7June 2014

8. Biovotion eco system and services*
 Attachment concept
 Sensor design
 Algorithms
 Functionalities
 Actionable events
»» Reliable monitoring
View VSM data
via cloud
Monitor collects vital signs,
displays status. Sophisticated
functionalities **
** Stepwise market introduction, basic parts of overall concept expected to be available for testing in Q4/2014
** Based on standardised elements also for efficient integration into existing eco systems or
connection to support infrastructures
June 2014 8
User support
centre**
Health monitoring (customised eco system)
 Generational support, healthy living
 Fitness & lifestyle, quality of sleep
Medical monitoring (customised eco system)
 Pre hospital - critical injury, paramedic, ambulance, triage
 In hospital (low acuity, ambulatory patients)
 Out of hospital - disease specific support, 30 day monitoring,
long term condition monitoring
VSM/components
worn on upper
arm or wrist
Secure platform of VSM data/
evaluation. Sophisticated
functionalities
Eco system to offer different levels of subscription services
Proprietary Information Biovotion

9. Example - Overnight sleep healthy
June 2014 Proprietary Information Biovotion 9
 Mainly constant heart rate with
minor cycle visible
 Little movement
 Cycling temperature changes
 Constant blood oxygenation
 Sleep phases
Heartrate[bpm]
Movementindicator
SaO2[%]
SvO2[%]
SkinTemp[°C]
Perfusion[%]

10. Example – Sleep apnoea patient
June 2014 Proprietary Information Biovotion 10

11. June 2014 Proprietary Information Biovotion 11
»» monitoring in motion
» easy to use
» accurate
» robust
HR
SAT
CBP
CBV
Temp
Mov
RR
HRV
Biovotion AG | Technoparkstr. 1 | 8005 Zurich | Switzerland | www.biovotion.com | info@biovotion.com

12. COMPASS: COntinuous Multi-variate monitoring for
Patients Affected by chronic obstructive pulmonary diSeaSe
 CTI Project 15888.1
 Partners:
 Biovotion
 Mr Stephan Bachofen
 HES-SO Sierre, E-Health Unit
 Dr Stefano Bromuri (Deputy Project Manager, PI)
 Mr Thomas Hofer
 Dr Michael Schumacher
 Running From April 2014 to April 2016.
June 2014 12

13. COMPASS: Challenges
 Challenges:
 Standardisation of the communication stack according to the
Continua Alliance standards to ensure interoperability.
 Signal compression and analysis at the mobile application level to
minimise the power requirements of the system
 Machine learning algorithm for
 Prediction of exacerbation of the COPD condition.
 Provide rehabilitation advices for the patient in COPD.
 HL7 CDA R2, to interface to existing care management solutions.
 Test on real patients.
June 2014 13

14. COMPASS: General Architecture
June 2014 14

15. COMPASS: Interoperability using
CONTINUA
 Continua Care for Devices:
 Based on IEEE 11073
 Medical / Health care device communications standards
 Enables communications between point of care devices and
remote servers
 Client-related health care information, vitals
 Equipment-related identity, performance and functional
status
 Supports three domains
 Disease Management,
 Health and Fitness,
 Living Independence
June 2014 15

16. Our Current Focus in the CONTINUA
Stack
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17. COMPASS: Feature Extraction and
Data Compression
 Lossless data compression: It is a class of
data compression algorithms that allows the original data to
be perfectly reconstructed from the compressed data.
 Lossy data compression: it permits reconstruction only of an
approximation of the original data, though this usually allows
for improved compression rates (and therefore smaller sized
files).
 No free lunch: there is no such thing as the universal
compression algorithm, some algorithms work differently in
different settings.
June 2014 17

18. COMPASS: Lossless Compression
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DE
DEF
DD
INF
DE
=
Delta
Encoding
DEF
=
Deflate
INF
=
Inflate
DD
=
Delta
Decoding

19. 0 100 200 300 400 500 600 700
0.7
0.8
0.9
1
0 100 200 300 400 500 600 700
−0.5
0
0.5
1
0 100 200 300 400 500 600 700
0.7
0.8
0.9
1
COMPASS: Lossless Compression
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You
start
with
a
signal
You
end
with
the
same
signal
Compression
rate
=
10%
Apply
the
Process

20. COMPASS: Lossy Compression using
Compressive Sensing
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is
uniquely
determined
by
is
random
with
high
probability
Donoho,
2006
and
Candès
et.
al.,
2006
NP-­‐hard
Convex
and
tractable
Greedy
algorithms:
OMP,
FOCUSS,
etc.
Donoho,
2006
and
Candès
et.
al.,
2006
Tropp,
Co6er
et.
al.
Chen
et.
al.
and
many
other
Compressed
sensing
(2003/4
and
on)
–
Main
results
Donoho
and
Elad,
2003

21. COMPASS: Compressive Sensing
Schema
June 2014 Proprietary Information Biovotion 21
S
P
A
R
S
I
F
Y
Ax
=
y
x0
=
A’y
T
R
A
N
S
M
I
T
s
y
x
D
E
S
P
A
R
S
I
F
Y
x
is
sparse
y<<x
O
P
T
I
M
I
Z
E
x0
s

22. COMPASS: CS First Attempt example
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RED:
Original
Signal
BLUE:
Recovered
Signal
Compression
Rate
=
20%
RMSE
=
0.0097

23. Future Work
 Finish the CONTINUA stack for the transmission
 Define two compression modules:
 LOSSLESS Compression Module
 Lossy Compression Module
 Use the features Extracted with CS to perform Machine
Learning Tasks.
June 2014 23

24. Thank You For your Attention
Questions?
June 2014 24