Mining Smartphone Data (with Python)

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Neal Lathia

Talk at #pydatalondon 2016

Données & analyses

Mining Smartphone Data (with Python)
@neal_lathia
PyData London 2016

Smartphones have sensors!
● Accelerometer (acceleration)
● Gyroscope (orientation)
● GPS, Wi-Fi (location)
● ...
● Microphone (sound)
● Bluetooth (co-location)

This talk
● Collecting accelerometer data
● A peek at the raw data
● Magnitude data
● Applications
● Feature extraction
● Focus on classifcation
● https://github.com/nlathia/pydata_2016

The magnitude vector
● We don't know how the phone is oriented
● We want to capture what is happening in the 3
axes in a single time series

Applications
● Step counting
– Brajdic, Harle. “Walk detection and step counting on
unconstrainted smartphones.” ACM Ubicomp '13.
● Unsupervised learning (profling)
– Lathia et al. “Happy People Live Active Lives.” Under
Submission.
● Activity classifcation

Activity classifcation: overview
● Get the time series data into some way to train
a classifer
● Train a classifer
● Predict activities
● ??
● Proft

Windowing
● Extract features from each window

Extract features from windows
● Statistical (mean, std dev)
● Time-series (jitter, kurtosis)
● Signal (frequency)
Reading: Hemminki, Nurmi, Tarkoma.
“Accelerometer-based Transportation Mode
Detection on Smartphones.” ACM Sensys '13.

Extract features from windows
Label Features

Further thoughts
● Collecting data effciently
– Background processes use loads of battery
● Real data is messier
– This was one person, one phone
● Feature engineering
– This was just an example.
● Other favours of classifcation
– Binary: “Is this walking?”
– Personalized vs. global models

Conclusion
● Collecting accelerometer data
● A peek at the raw data
● Magnitude data
● Applications
● Feature extraction
● Focus on classifcation

Mining Smartphone Data (with Python)
@neal_lathia
PyData London 2016
https://github.com/nlathia/pydata_2016

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