In this session, learn how the Intel team of software engineers and data scientists, in collaboration with the Michael J Fox Foundation, built a big data analytics platform using Hadoop and other IoT technologies. The solution leverages wearable sensors and smartphone application to monitor PD patient's motor activities, 24/7. The platform collects and processes large stream of data, and enables different analytics services such as activity recognition and different PD related measurements to researchers. These machine learning algorithms are used to detect patterns in the data that can help researchers understand the progression of the disease and develop effective treatments. You leave with a comprehensive view of the tools and platforms from Intel that you can use in building your own applications on AWS. In addition there will be a deeper dive to explain the way this platforms enables near real- time analytics as part of the ingestion process. Parkinson's Disease-a neuromuscular disease that causes gradually worsening symptoms such as tremors, difficulty in movement, and sleep loss -affects over 5 million people worldwide. Because the symptoms vary from individual to individual, research into the disease is hampered by the lack of objective data. As is typical of many healthcare applications, the collection, storage, and analysis of data is complex, expensive, and time-consuming. Intel is tackling this challenge by building a solution that uses wearable devices to collect data from patients anonymously and store it securely. Sponsored by Intel.

1. MotyFania-Principal Architect, Intel

2. Parkinson Disease (PD)

3. The Hypothesis / Opportunity
The problem –
PD Big-Data is not really available
Solution
•Enable breakthroughs in Parkinson disease research through Big Data analytics
•Small disparate sources of data
•Most data is limited and unavailable
•Instrument PD patients with wearable devices for large scale, continuous 24 X 7 data collection

4. Patients are not able to objectively evaluate their condition
No Objective measure of Parkinson disease symptoms
Cost of trials are in the scales of $M and they take several years to complete
Very small number of patients contribute to research
Researchers can not scale to large N due to technology limitations

5. 30
subjects
5
Days per Subject
0.15TB
Per Subject per Day
500
subjects
30
Days per Subject
1GB
Per Subject per Day
15TB
Every month
1000
subjects
365
Days per Subject
Per Subject per Day
365TB
Every year

6. Smartphone App
Big Data Analytics
Wearable Monitor
24 x 7 monitoring
Objective measurements
•
Activity Identification
•
Anomaly Detection
Location
Movement
Medications
…
Sleep Patterns
Gait
Balance
Tremors
Researcher
physician
Patient

7. 7
Cloud Infrastructure
UI
Data Platform
Analytics Platform
Datacenter
Network
Thing
Services
Gateway

8. 2 Start an application
1 Wear a watch

10. Activity recognition
Activity characteristics
symptoms identification
Activity
monitoring

12. 8:00
9:00
10:00
11:00
12:00
13:00
8:00
9:00
10:00
11:00
12:00
13:00

13. 8:00
9:00
10:00
11:00
12:00
13:00
8:00
9:00
10:00
11:00
12:00
13:00
Active

14. 8:00
9:00
10:00
11:00
12:00
13:00
8:00
9:00
10:00
11:00
12:00
13:00
Active
Low Movement

15. 8:00
9:00
10:00
11:00
12:00
13:00
8:00
9:00
10:00
11:00
12:00
13:00
Active
Low Movement
Hand Movement

16. 8:00
9:00
10:00
11:00
12:00
13:00
8:00
9:00
10:00
11:00
12:00
13:00
Active
Low Movement
Active

17. 8:00
9:00
10:00
11:00
12:00
13:00
8:00
9:00
10:00
11:00
12:00
13:00
Active
Low Movement
Active
Low Movement

18. 8:00
9:00
10:00
11:00
12:00
13:00
8:00
9:00
10:00
11:00
12:00
13:00
Active
Low Movement
Active
Low Movement
Active

19. 8:00
9:00
10:00
11:00
12:00
13:00
8:00
9:00
10:00
11:00
12:00
13:00

20. 10:00
10:12
10:24
10:36
10:48
11:00
10:00
10:12
10:24
10:36
10:48
11:00

21. 10:00
10:12
10:24
10:36
10:48
11:00
10:00
10:12
10:24
10:36
10:48
11:00

22. Frequency ~5Hz –Typical to tremor
3.5
10.5
17.5
24.5
31.5
Time [sec]

23. Rest
Leg Tremor Indication
Rest
Leg Tremor Ind.
Rest
3.5
10.5
17.5
24.5
31.5
Time [sec]

24. 8:00
9:00
10:00
11:00
12:00
13:00
8:00
9:00
10:00
11:00
12:00
13:00

26. Sitting Down

27. Sitting Down
Changing hand posture while sitting

28. Sitting Down
Getting Up

29. Sitting Down
Hand changes position to assist the movement
Getting Up

30. Sitting Down
Getting Up
Measuring movements duration can indicate on slowness of movement

31. Sitting Down
Getting Up
Walking

32. Sitting Down
Getting Up
Walking
Interrupts (e.g., turning around, handshake)

33. Sitting Down
Getting Up
Walking
Changing hand posture while walking

34. Sitting Down
Getting Up
Walking
70 Steps in average pace of 103 Steps per minute (in general, pace can indicate on slowness of movement)

35. Sitting Down
Getting Up
Walking
Sitting Down

37. 37
~45 ServersLet’s look Inside

38. (Big) Data Ingestion
Platform-as-a-Service (PaaS) for real-time data & event processing
Analytics Services
Monitoring & Alerting
Data Visualization
Reporting & Querying
Advanced Analytics Services
Time Series Analysis
Anomaly / Change Detection
Activity Recognition / Context Extraction
Smart Cities
Retail
Industrial
Transportation
Health

39. Predictive Maintenance
Inventory & Asset Mgmt.
Cyber / Malicious Activity
Mobile Health
Load Balancing

40. Speed Layer
Batch Layer
Data
Sources
Ingestion
Layer
Servicing
Layer

41. Configuration & MetaData
Web Services
Data Storage
Analytics
Rule Engine
Web
Site
MQTT
Node.js
Express
Angular.js
Bootstrap
Node.js
Express
MongoDB
Message Broker
Scala
Java
Akka
Spray
Apache Kafka
Apache Phoenix
Spark
R
ElasticLoad Balancing
CDH 5.2
YARN
MapReduce 2
HBase (time series)
InfiniDB for AWS (aggregates)
Cache
Redis
Deployment:
AWS Cloud Formation
OpsCodeChef
Amazon VPC
Monitoring:
Nagios/ Zabbix
Logging:
Logstash
Auto Scaling

42. Intel announced a comprehensive developer program for hobbyists, students and entrepreneurial developers with outreach, training and tools required to rapidly develop, test and deploy applications for the Internet of Things.
•Package of easy to use hardware, software & tools, services
•Global HackathonChallenge with prizes
•20 City IoT Roadshow distributing 5,000 kits
•University Program with courseware and labs starting with Carnegie Mellon
•On-line community for learning, building sharing
See Edison Live at the Intel Booth

46. You can use this platform to collect data and build your own solution with Edison!

48. http://bit.ly/awsevals