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. .
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.
Can I have a coffee?
people and the environment
Here is a coffee.
non-verbal sounds (sneeze)
Thanks for my coffee.
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
Collection of Words
Stemming & Stop Word Removal
Activity class based bayesian