Enable breakthrough in Parkinson disease research- Ido Karavany

1. Enable breakthroughs in Parkinson
disease research through wearables and
Big Data analytics technologies

2. About us…
• Part of the Big Data Analytics Solutions group @Intel
• Developing products & solutions leveraging:
• Big Data & edge-technologies
• Self developed machine learning & steam analytics algorithms
• Our team includes developers, data scientists and system analysts
• I am a Big Data Analytics Architect and R&D Manager responsible for
leading-edge technology projects within Intel involving Big Data and
stream analytics solutions in the Internet of Things and Mobile
Healthcare
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3. How It All Started?
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Big data analytics
IOT

4. Parkinson’s Disease
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OVER AGE
0F 60
1/100 60,000
NEW
1M/US
5M/WORLD
NO CURE,
MEDICATION ONLY HELPS WITH
SYMPTOMSThere is
NO TEST
and no
PROGRESSION
MARKER
Common symptoms include
TREMOR SLEEP QUALITY
SLOWNESS DEPRESSION

5. Challenges To Address
NO
OBJECTIVE
MEASURE
3-6 MONTHS
BETWEEN
PHYSICIAN
VISITS
CHANGES ARE
SLOW
AND HARD TO
DETECT
AVERAGE
TRIAL SIZE
< 100
PATIENTS
VERY
SMALL
number of
patients
contribute
to research
COST OF
TRIALS
are in the
scales of
$M
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6. HOW?
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7. The Solution
Wear a watch Start an application
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1 2

8. Use Cases
MANAGE
THE
DISEASE
USING
DATA
FREE DATA
FOR 1000’S
OF PATIENTS
ACCURATE
REPORT
SINCE LAST
VISIT
MEASURE
MEDICATION
EFFECT
RESEARCHER
PHARMACEUTICAL
CLINICIAN
INTEL BIG DATA CLOUDANALYSTICS
INSIGHT / VALUE
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9. THE APPLICATION
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10. 10
Medication
reporting
Medication
reminder
Report
something
PATIENT
REPORTED
OTHER
Configurable
data
collections
Contribution
score
Useful
Accessories
Pebble
notifications
OBJECTIVE
MEASURES
Gait
Sleep
Tremor
Activity Level
Controlled
Tests

11. BIG-DATA and IOT
TECHNOLOGIES
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12. SERVICE LAYER
BATCH ANALYTICS LAYER
STREAM ANALYICS LAYER
INGESTION LAYER
STORAGE LAYER
USER INTERFACE LAYER
Mosquitt
o
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CLOUD COMPUTING SERVICES

13. • Cloudera Enterprise Data Hub
• HBase as main scalable time series data storage layer
• Allows high writes throughput
• Random real-time access to stored data
• Highly available MySQL as metadata storage
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STORAGE LAYER

14. • Based on Apache Spark over HBase
• Spark is a fast and general engine for large-scale
data processing
• Algorithms & Calculations are being executed on large
data sets on a daily basis
• Layer includes set of self developed complex machine
learning algorithms
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BATCH ANALYTICS LAYER

15. • Rule Engine
• Support simple and complex event based rules
• Calculations over large datasets to extract statistical baseline
• Automatic Change Detection
• Calculates normal sensors’ activity over large data sets
• Automatically detect changes in sensors’ activity
• Data Export Service
• Enables transform and export of large data sets using Spark
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BATCH ANALYTICS LAYER

16. CHALLENGES
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17. Challenges
• Backward compatibility
• Started with Spark 0.7.0
• Versions upgrading often required code changes / recompilation
• Spark over HBase access and tuning
• Yarn integration improvements
• Pioneers with Spark on Yarn mode
• Missing parameters were added following our work
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18. Challenges
• Data Scientists / Algorithms developers education
• Spark as a reporting tool for interactive data extraction over HBase
• Failed to achieve quick fast response times
• Execution method of many small jobs
• Spark Context per job
• Single Spark context managing many jobs
• Spark context driver GC issues
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19. ANALYTICS
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20. Activity Level
• The main feature the patients have asked for
• Motivates the patients to be more active (known to
be important for PD patients)
• Describes the intensity of the patient’s activity
throughout the day alongside with his/her
medication intake
• Personalized high activity threshold per patient
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21. Activity Level – An Example
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Activity Level in Controlled Session (ON State)
Activity Level in Controlled Session (OFF State)

22. Night Time Analysis
• Many Parkinson patients suffer from sleep
disorders or experience PD symptoms during
the night
• Provide patients with analysis of their
movements during the night
• Reports minimal, moderate and intense
movement periods during the night
• Allows patients to better plan their sleep time
and wake up times for their medications taking
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23. Night Time Analysis
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Total time of minimal movement: 6hr 5min, Periods of extensive movement: 5
Total time of minimal movement: 8hr 10min, Periods of extensive movement: 0
Parkinson
Patient
Healthy
Person

24. TRIALS AND PARTNERS
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25. REAL PD
L-DOPA RESPONSE TRIAL
DATA GATHERING TRIAL
FOX INTEL APPLICATION TRIAL
1000
30
30
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FOX INSIGHT WEAR 1000
50
30
30
20
500
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Trial And Partners
SCRIPPS TREMMOR TRIAL 10010

26. WHAT’S NEXT?
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27. SCALE PLATFORM
• Scale to 1000’s of
patients in the US
• Scale to 1000’s of patients in the
Netherlands
• IOS Full support
• Support additional wearables
• Build more value
generating capabilities
• Enrich Platform (i.e. Reporting
capabilities)
• Provide a solution for clinicians to
access the data
• Expand insights extractions from
collected data
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28. Q&A

29. Thank you!
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