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

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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. .

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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