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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.
Citation preview
Moty Fania - Principal Architect, Intel
Parkinson Disease (PD)
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
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
30subjects
5Days per Subject
0.15TBPer Subject per Day
500subjects
30Days per Subject
1GBPer Subject per Day
15TBEvery month
1000subjects
365Days per Subject
365TBPer Subject per Day
365TBEvery year
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 physicianPatient
7
Cloud Infrastructure
UI
Data Platform
Analytics Platform
DatacenterNetworkThing
Services
Gateway
2 Start an application1 Wear a watch
Activity recognition
Activity characteristics
symptoms identification
Activity
monitoring
8:00 9:00 10:00 11:00 12:00 13:00
8:00 9:00 10:00 11:00 12:00 13:00
8:00 9:00 10:00 11:00 12:00 13:00
8:00 9:00 10:00 11:00 12:00 13:00
Ac
tive
8:00 9:00 10:00 11:00 12:00 13:00
8:00 9:00 10:00 11:00 12:00 13:00
Ac
tive
Low Movement
8:00 9:00 10:00 11:00 12:00 13:00
8:00 9:00 10:00 11:00 12:00 13:00
Ac
tive
Low Movement
Hand
Movement
8:00 9:00 10:00 11:00 12:00 13:00
8:00 9:00 10:00 11:00 12:00 13:00
Ac
tive
Low Movement
Ac
tive
8:00 9:00 10:00 11:00 12:00 13:00
8:00 9:00 10:00 11:00 12:00 13:00
Ac
tive
Low Movement
Ac
tive
Low Movement
8:00 9:00 10:00 11:00 12:00 13:00
8:00 9:00 10:00 11:00 12:00 13:00
Ac
tive
Low Movement
Ac
tive
Low Movement
Ac
tive
8:00 9:0010:0
011:00 12:00
13:0
0
8:00 9:0010:0
011:00 12:00
13:0
0
10:00 10:12 10:24 10:36 10:48 11:00
10:00 10:12 10:24 10:36 10:48 11:00
10:00 10:12 10:24 10:36 10:48 11:00
10:00 10:12 10:24 10:36 10:48 11:00
Frequency ~5Hz – Typical to tremor
3.5 10.5 17.5 24.5 31.5
Time [sec]
Rest
Leg Tremor Indication
Rest
Leg
Tremor
Ind.
Rest
3.5 10.5 17.5 24.5 31.5
Time [sec]
8:00 9:0010:0
011:00 12:00
13:0
0
8:00 9:0010:0
011:00 12:00
13:0
0
Sitting Down
Sitting Down
Changing hand
posture while sitting
Sitting Down
Gettin
g U
p
Sitting DownHand changes position
to assist the movement
Gettin
g U
p
Sitting Down
Gettin
g U
p
Measuring movements duration can
indicate on slowness of movement
Sitting Down
Gettin
g U
p
Walking
Sitting Down
Gettin
g U
p
Walking Interrupts (e.g., turning
around, handshake)
Sitting Down
Gettin
g U
p
WalkingChanging hand posture
while walking
Sitting Down
Gettin
g U
p
Walking
70 Steps in average pace of 103
Steps per minute (in general, pace
can indicate on slowness of
movement)
Sitting Down
Gettin
g U
p
Walking Sitting Down
37
~45 Servers
Let’s look Inside
(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 RetailIndustrial TransportationHealth
Predictive
Maintenance
Inventory &
Asset Mgmt.Cyber /
Malicious
Activity
Mobile Health
Load
Balancing
Speed Layer
Batch LayerData
Sources
Ingestion
LayerServicing
Layer
Configuration &
MetaDataWe
b
Se
rvic
es
Data Storage
Analytics
Rule Engine
Web
Site
MQT
T
Node.js
Express
Angular.js
Bootstrap
Node.js
Express
MongoDB
Message Broker
Scala
Java
Akka
Spray
Apache Kafka
Apache Phoenix
Spark
RElastic Load
Balancing
CDH 5.2
YARN
MapReduce 2
HBase (time series)
InfiniDB for AWS (aggregates)
Cache
Redis
Deployment:
AWS Cloud Formation
OpsCode Chef
Amazon VPC
Monitoring:
Nagios / Zabbix
Logging:
Logstash
Auto Scaling
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 Hackathon Challenge 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
You can use this platform to collect data and
build your own solution with Edison!
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