I gave a talk at the Fifth Elephant -2013 in Bangalore. Here is the ppt of my talk "Smart Analytics in Smartphones".

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Smart Analytics in
Smartphones
Satnam Singh, PhD
Samsung Research India -Bangalore
Fifth Elephant Conference, Bangalore
July 13, 2012
Disclaimer: Talk is based on my personal views and knowledge gathered from open sources

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• What is Smart Analytics?
• Trends in Smart Analytics
• Why to do Analytics in Device?
• Case Study: Sensory Data Analytics
Outline

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Smart Analytics
- Analytics keeping end-user in mind
- Enable use cases to bring new experience, ease and
benefits to end-user
Buying habits
Location and time
Activity
Entertainment
User Presence
Sensor
Data
User
Data
Social
Data
SNS Data, RSS feeds
Images, Videos, Music,
Call logs, SMS data
Browser data

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Smart Analytics in Smartphones
Sensor
Data
- Enhance User Experience
- Recommendations
- Personalization
Social
data
User data…
Analytics (Text Mining, Machine
Learning, Signal Processing)
Sensor
Data
User
Data
Social
Data
3rd Party Applications, Native
Applications

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User Data Analytics- Trends
Breadcrumbs
• A Simple Timeline of your Day
• Everything happening at your places
• Offers and Deals for your favorite places
Radii
• Connecting Personality to Places
• Match the place's personality with users
personality to give the best recommendations
• Deliver movie-like game experiences,
videos, images and wallpapers
• Bring users into the film's story and world
Paramount Pictures - Star Trek Into Darkness
Qualcomm’s Gimbal Platform Applications

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Sensory Data Analytics - Trends
Galaxy S4 Sensors  Multiple sensors,
Environment sensing
Activity Recognition [Sensor Platforms, Alohar
mobile, ActiServ]

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Analytics in Server vs. Device
Device-based Analytics - Privacy concerns are taken care of..
• It works even if no network !!
• Need predictive models to run close to real-time and
automatically deploy them
• Power and battery consumption should be kept under
control
Server-based Analytics is needed if the application is too
compute intensive for a smart phone
• Latency and data transfer cost
• Data must be communicated securely
• Authentication before any data transfer

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Case Study: Sensory Data Analytics
Activity Recognition: Detect walking, driving, biking, climbing
stairs, standing, etc.
Activity
Recognition
Running Biking
Climbing stairs Walking
Sitting
1. If phone call comes then
Send an automated SMS to
call later
3. Do not refresh
location  Save
battery power
2. If phone
call then
increase ring
tone

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Data Visualization – Raw Data & Activity (Class Variable)
[Ref] Rattle R Data Mining Tool
Bar Plot
Example of Accelerometer data

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Activity Recognition - Steps
Feature
Extraction
Time Series Data 43 Features
Mean for each
acc. Axis (3)
Std. dev. for each
acc. Axis (3)
200 samples (10 sec)
Avg. Abs. diff. from
Mean for each
acc. Axis (3)
Avg. Resultant Acc. (1)
Histogram (30)
Classifier
CART: Decision Tree
Classify the
Activity
[Ref] Gary M. Weiss and Jeffrey W. Lockhart, Fordham University, Bronx, NY
[Ref] Jordan Frank, McGill University
[Ref] Commercial API Providers: Sensor Platoforms, Movea, Alohar

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[Ref] Rattle R Data Mining Tool
Decision Tree
-Accuracy for general model~75%, >95%
personalized model using 10 seconds
training for each activity
-Accelerometer sensor is low power
consuming sensor
- Use other sensors to figure out where is
smartphone  Enhance accuracy by 5-6%

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Activity Recognition: Engg. Challenges
“Design Considerations for the WISDM Smart Phone-based Sensor Mining Architecture,”
SensorKDD ’11, Fordham University
• Supervised models- problems in collecting user data
• Data sampling rate for each activity:
o High sampling rate than needed  waste CPU cycles,
o While low sampling rate degrade the performance
• App should work even if device is in hibernation mode
• Control SQLite database overheads
• Power consumption and real-time computations
• Benchmarking and user testing is a key challenge
• Global user – support multiple languages for any text
mining application

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• Fusion of data science and domain knowledge
can bring new experiences for end-users
• Getting data analytics-based feature in
product needs intense team effort between
various stakeholders
Summary
Thanks!!

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Backup Slides

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[Ref] Rattle R Data Mining Tool
…
Σ
Random Forest
Tree1 Tree2
Treen
Random Forest: An Ensemble of Trees

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Another Approach: Activity Recognition
Feature
Extraction
using
PCA
Classification
using
SVM
9 PCs Classify
the
activity
“Activity and Gait Recognition with Time-Delay Embeddings” Jordan Frank, AAAI Conference on
Artificial Intelligence -2010
McGill University