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We will save 1 million lives by 2020
Coal miners used birds for early detection of gas leaks
We use mobile technology and AI for early detection of critical disease symptoms
The most advanced sensor devices in history
HYGROMETER MAGNETOMETER GYROSCOPE ACCELEROMETER GPS BAROMETER HEART RATE
Highly precise detection of micro movements
Detecting Sleep
Detecting Meals
Detecting Work Times
Detecting Inactivity
Detecting Cycling
TEAMMatching Sensor Data with Medical Data to prevent
Hypoglycaemia through Early Detection of Crit ical Patterns
Matching with CGM Data
Gets up at night Physical active Visits risk location
Energy
Correlat ion of glucose level and dai ly act iv i t ies of a d iabet ic
Passive Tracking for Diabetes
Physically active Visits risk location
TEAMMatching Sensor Data with Medical Data to prevent
Hypoglycaemia through Early Detection of Crit ical Patterns
Matching with CGM Data
Gets up at night Physical active Visits risk location
Energy
Correlat ion of glucose level and dai ly act iv i t ies of a d iabet ic
Passive Tracking for Diabetes
Physically active Visits risk location
Matching with CGM Data
Gets up at night Physical active Visits risk location
Energy
TEAMMatching Sensor Data with Medical Data to prevent
Hypoglycaemia through Early Detection of Crit ical Patterns
Matching with CGM Data
Gets up at night Physical active Visits risk location
Energy
Correlat ion of glucose level and dai ly act iv i t ies of a d iabet ic
Passive Tracking for Diabetes
Physically active Visits risk location
Matching with CGM Data
Gets up at night Physical active Visits risk location
Energy Gets up at night Physically active Visits risk location
Assessment of Contextual Data
Our algorithm translates raw data into real life activities and assesses their risk.
xbird Machine Learning Model
Dancing Jumping Walking
Data
Segmentation
Feature Extraction
Patient’s Accelerometer Data
Data is labeled (we know what people are doing)
xbird Machine Learning Model
Training Set Training Phase Testing Set
New Case Running Model Output
Is prediction
compatible with real
labels?
+
Confusion Matrix Heatmap
Walking Cycling Sleeping StationaryGeneral Physical
Activity
Walking 91,8 4,2 0 0,5 3,6
Cycling 2,9 78,1 0 8,9 10,2
Sleeping 0 0 98,5 1,4 0,1
Stationary 3,1 8,5 1,3 85,5 1,6
General Physical
Activity 2,3 9,1 0,3 3,7 84,5
Detecting Work & Home Location
Location Detection through Activity Data
Work Home
High Risk Locations
Flagging Locations correlated with the most ups and downs in blood sugar
TEAMWe offer an SDK, allowing for many use cases
Team
Sebastian Sujka
CEO
Matteo Carli
CTO
Dr. med. Jonas Harder
Chief Medical Officer
Dr. Silvan Türkcan
Scientific Advisor
Nikolai Stosch
User Experience
Alex Roslyakov
Tech Lead iOS
Joseph Lam
ML
Klaudia KobelrauschMedical Research
Awards and Endorsements 2016
Winner of the Health-i Award
2016
Winner of the Life Sciences Track at
Pioneers Festival 2016
Winner of the TK Innovation Award, exclusive 1-
year partnership with TK
Winner of the eHealth Venture Summit
Award 2016Digital Health Innovator
Part of Bayer’s Grants4Apps Accelerator
Program
Final Remark
This is happening
This is happening
Thank you
Sebastian Sujka
CEO
Matteo Carli
CTO
Dr. med. Jonas Harder
Chief Medical Officer
Dr. Silvan Türkcan
Scientific Advisor
Nikolai Stosch
User Experience
Alex Roslyakov
Tech Lead iOS
Joseph Lam
ML
Klaudia KobelrauschMedical Research
