Mobile Phone based Inference Models using People-centric Features
This presentation is the property of its rightful owner.
Sponsored Links
1 / 1

Mobile Phone based Inference Models using People-centric Features Nicholas D. Lane PowerPoint PPT Presentation


  • 44 Views
  • Uploaded on
  • Presentation posted in: General

Mobile Phone based Inference Models using People-centric Features Nicholas D. Lane. Approach: Use features that leverage the ability of people to synthesize complex multivariate data. Example: Spoken Words as features for Activity Recognition. .

Download Presentation

Mobile Phone based Inference Models using People-centric Features Nicholas D. Lane

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Mobile phone based inference models using people centric features nicholas d lane

Mobile Phone based Inference Models using People-centric Features

Nicholas D. Lane

Approach: Use features that leverage the ability of people to synthesize complex multivariate data

Example: Spoken Words as features for Activity Recognition.

Problem: Inferences about society and where we live are challenging with mobile phones.

  • 10^9 mobile phones are in daily use but with limited sensing capabilities (e.g., localization accelerometer, microphone).

  • Important inferences are difficult based on these sensors (e.g., What are people doing? Are they sick? Are they safe?).

words selection

Can I have a coffee?

mobility patterns

people and the environment

Here is a coffee.

non-verbal sounds (sneeze)

Thanks for my coffee.

behaviour

Exploratory Experiment

Recognition Process

Audio Signals

Hypothesis: Even when recognizing only a fraction of ambient spoken words it is possible to perform complex forms of activity recognition using only a simple bag-of-words model.

MFCC feature vectors from audio frames

LBG-based vector quantization

Methodology: Build proof-of-concept iPhone-based prototype. Capture 19 hours of audio while doing different activities over 2 weeks.

Isolated word based discrete HMMs

Results

Collection of Words

Stemming & Stop Word Removal

  • With 17% of words recognized and using word only features mean activity recognition accuracy was 71%.

coffee

Activity class based bayesian

“bag-of-word” models

fast food

  • Recognizes different instances of classes (e.g., fast food) and does not confuse these with similar classes (e.g., restaurants).

Activities

Future Work

  • Evaluate other examples of People-centric features particularly those found in other modalities and across other time scales

  • Develop models that combine these examples with more conventional features.

  • Differentiates activity uses (e.g., coffee or book purchase) in the same physical space (e.g., bookstore).

unknown class


  • Login