Meetup presentation Jonatan Samoocha, Thursday August 20th, Enschede Jonatan was a (very) casual runner until 3y ago, when he decided without apparent reason to run a 10k race, 45m or faster… He found out that there are lots of “expert opinions”, some seem more “reasonable” or “sophisticated” than others. Jonatan ventured out with the plan for “beginning competitive” runners. Being a data expert, Jonatan decided to collect data from sports app Strava, and start analyzing this, creating a machine learning model to recognize different exercise types including the difference between road cycling and mountain biking.

1. GoDataDriven
PROUDLY PART OF THE XEBIA GROUP
github.com/jsamoocha
jonatansamoocha@godatadriven.com
Exercise Type Detection
Jonatan Samoocha
Number Cruncher
- Formal education in AI
-Doing quantitative & computational stuff (nowadays called “data science”)
-@ GoDataDriven - doing Big Data / Data Science consultancy
-Nearly 20-person strong team, various backgrounds (CS, econometrics, theoretical physics, etc)
-This project emerged from the GDD Friday
-Why this topic?

2. GoDataDriven
Google is our friend?
- (Very) casual runner until 3y ago
-decided without apparent reason to run a 10k race, 45m or faster…
-lots of “expert opinions”
-some seem more “reasonable” or “sophisticated” than others
-chose one, for “beginning competitive” runners

3. GPS Becoming a Commodity:
- Bought myself one of those (and a pair of new shoes :-))
- Uploads data to web app of vendor

4. “Fancy” analytics
by GPS vendors
- Just showing history, fail to answer simple questions:
- what's my level?
- what's my progress?
- is goal X achievable?
- on track for goal X?

5. - Consumes data from most GPS device vendors
-Focus on social (friends, clubs, leaderboards)
-Same shortcomings of analytics - just representations of history
-Huge amount of users, estimated 1.2M active
-potential for retrospective experiments with n > 1(00)k
-E.g. what is the most efficient way (time/injury/etc) to train for sub-45 10k, famous cyclosportive?

6. Explaining / Predicting
Race Performance
- Thought experiment: what could we do if we had all that data?

7. GoDataDriven
Exercise data (overview)
Potential aggregates (by period):
-total volume
-ramp-up (rate of increase in volume)
-speed/hr/power/etc variance

8. GoDataDriven
Exercise data (streams)
Potential aggregates (by exercise):
-within-activity variance
-distribution of speed/power/hr/etc

9. GoDataDriven
Time series analysis on streams
Allows detection of:
-blocks
-repetitions
-structure

10. GoDataDriven
Time series analysis, GPS streams
Allows detection of:
-Corners
-Acceleration/deceleration

11. GoDataDriven
Detecting Races
?
Precondition for predicting race performance…

12. GoDataDriven
The Plan
•Collect data from Strava
•Transform raw (GPS) streams to concepts of
higher abstraction level:
•Blocks, repetitions, = exercise types
•Aggregates by period of exercise types
•Aggregates by period of other exercise
properties
•Use Machine Learning to relate these to race
performance

13. Personalized training
advice for the masses!
Caveat: probably less suitable for elite athletes seeking marginal improvements

14. Need to deal with some “practicalities”

15. Getting the data
- REST API, all data is available
-Unfortunately, there’s no simple “download all” button or endpoint
-Need an access token for each user
-Host a web app, OAuth to Strava

16. GoDataDriven
25 “Data Donors”
•Obtained by recruiting in Facebook
•Diverse group
•Hardly active vs. elite level
•Running, Cycling, Duathlon,Triathlon
•9434 activities (2015-08-16)

17. GoDataDriven
Curse of Dimensionality
•Controlling some measure (e.g. race time) by
gender, age, weight
•Dividing age and weight in 10 bins totals 200 bins
•Assume 1000 users uniformly distributed
•Gives n=5 per bin :-(
•Need many more users!

18. Level
Progress
Activity Feedback

19. GoDataDriven
Level by sport should be easy…
Wait, where’s “mountainbike” as activity type?
- Some cyclists ride on the road as well as in the dirt
-Complicates computation of “level”!

20. Exercise Type
Detection

21. GoDataDriven
Challenge 1: Road vs. MTB
- Also using machine learning
-Classification, supervised

22. GoDataDriven
astroml.org
Validation method:
- Split data into train/test
- Train model on train
- Predict on test
- Compare predicted with actual label
- Quantify error (Accuracy, F1 score, Kappa, etc)
- Interpret error !!!

23. GoDataDriven
First things first: labels
- Supervised learning, needs examples
-Strava didn’t label our rides!
-Manually added “road”/“offroad” labels to ~500 ride activities

24. GoDataDriven
Overview Data…
No nice bimodal distributions, trying box plots…

25. GoDataDriven
- Speed separates ok
-Lots of overlap, probably need better features for good prediction

26. GoDataDriven
The 3 feature model
precision recall f1-score support
offroad 0.88 0.97 0.92 30
road 0.98 0.94 0.96 65
avg / total 0.95 0.95 0.95 95
- Gradient Boosting algorithm
-Precision: fraction of actual X in set of rides predicted as X
-Recall: fraction of X predicted as X
-F1 score: harmonic mean of precision and recall
-Seems ok, but beware of uneven distribution road/offroad

27. Refining raw data
Extract new features

28. How to quantify:
VS
Easily detectable for a human eye
Needs to be quantified for an algorithm

29. GoDataDriven
GPS stream = sequence of vectors
- Need to transform lat/long to x/y
-Each point in time is a vector (dx,dy) from previous point in time

30. GoDataDriven
There’s a cosine for vectors
cos = 1: vectors in same direction
cos = 0: vectors orthogonal
cos = -1: opposite directions

31. GoDataDriven
“Technicality” of the course
•time series of (dx,dy) vectors
•ts of cosine (range [-1, 1])
•transform to [0, 1]
•0 = straight ahead
•1 = 180 degree turn
•take sum
•divide by distance
•“corners per km”

32. GoDataDriven
Adding “technicality” as feature
Many outliers!
GPS freakouts (in mountains)
Track rides

33. GoDataDriven
The 4 feature model
precision recall f1-score support
offroad 0.97 0.97 0.97 30
road 0.98 0.98 0.98 65
avg / total 0.98 0.98 0.98 95
“Corners per km” feature adds value :-)

34. Lessons Learned

35. GoDataDriven
Never, ever, trust human input
- Subjective
-Error-prone

36. GoDataDriven
Optimizing the model
•Good features add predictive accuracy
•Same for a sufficient amount of (correctly)
labelled observations
•Preferably “borderline” cases
•Model selection and tuning is marginal compared
to above

37. GoDataDriven
Near Future
•Creating an app with the “bait” features
•Detect races
•Detect interval exercise types
•Ask users for feedback on predictions - meta
learning
•Open-sourcing part of the code

38. GoDataDriven
We’re hiring / Questions? / Thank you!
github.com/jsamoocha
jonatansamoocha@godatadriven.com
Jonatan Samoocha
Number Cruncher