Talk at #pydatalondon 2016

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

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

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

5. 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

6. Collecting Data

7. Collecting Data

8. Collecting Data

9. The raw data

10. Raw: Standing

11. Raw: Walk

12. Raw: Run

13. Raw: Stairs

14. Raw: On a Train

15. 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

17. 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

18. Unsupervised learning

19. Activity Classifcation

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

21. Related Problem

22. Windowing
● Extract features from each window

23. 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.

24. Extract features from windows

25. Extract features from windows

26. Extract features from windows

27. Extract features from windows
Label Features

28. Data is ready.. classify

29. Data is ready.. classify

30. 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

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

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