Transcript [Lulwah Alkwai](slides)

Pocket, Bag, Hand, etc. Automatically Detecting Phone
Context through Discovery
Emiliano Miluzzoy, Michela Papandreax, Nicholas
D. Laney, Hong Luy, Andrew T. Campbelly
Presented by:
Lulwah Alkwai
Introduction
Discovery Framework
Phone Sensing Context
Design
System Implementation
Preliminary System Evaluation
Conclusion
Introduction
What is “phone sensing context”?
•
•
The position of the phone carried by a
person (e.g. in the pocket , hand ,
backpack , arm , ...) in relation to the
event being sensed.
It is a fundamental building block for new
distributed sensing application built on
mobile phones.
Observation
has
grown
out
of
implementation
of
CenceMe
and
SoundSense.
•
•
CenceMe:
Is a personal sensing system that
enables members of social networks to
share their sensing presence with their
buddies in a secure manner.
SoundSense:
Top end mobile phones include a number of
specialized (e.g., accelerometer, compass,
GPS) and general purpose sensors (e.g.,
microphone, camera) that enable new
people-centric sensing applications.
Discovery Framework
Phone sensing Context
Design
System implementation
Phone Sensing Context
Accurate
Robust
Low duty cycle
Design
•
Using the entire suite of sensing modalities
available on a mobile phone to provide enough
data features for context discovery at low cost
and for increased accuracy and robustness.
System Implementation
Feature selection:
1st-19th: Audio signal classification
problems
20th: Power of audio signal/raw audio
data
21st,22nd: Mean and standard deviation
23rd: # of times exceeds a certain
points
Training
Predictions
(a) FFT power of an audio clip ,when the phone inside the pocket
(b) FFT power of an audio clip ,when the phone outside the pocket
(c) Count the number of times the FFT power exceeds the threshold
Preliminary System Evaluation
•
The result highlight that the audio modality is
effective in detecting the in/out of pocket context
with reasonable accuracy.
IN/OUT POCKET
A: GMM
B: SVM
C: GMM TRAINING AND EVALUATING INDOOR
D: SVM TRAINING AND EVALUATING OUTDOOR
E: SVM TRAINING AND EVALUATING INDOOR
F: SVM TRAINING OUTDOOR AND EVALUATING INDOOR
G: GMM TRAINING USING ONLY MFCC
H: SVM TRAINING USING ONLY MFCC
Conclusion
•
Initial implementation looks promising , has
potential , when implemented in its full form to
become a core component of future mobile
sensing systems.
Thank you