Analyzing endurance-sports activity data with Spark


July 1, 2014

Presented at Spark Summit (San Francisco, CA)

Spark’s support for efficient execution and rapid interactive prototyping enable novel approaches to understanding data-rich domains that have historically been underserved by analytical techniques. One such field is endurance sports, where athletes are faced with GPS and elevation traces as well as samples from heart rate, cadence, temperature, and wattage sensors. These data streams can be somewhat comprehensible at any given moment, when looking at a small window of samples on one’s watch or cycle computer, but are overwhelming in the aggregate.

In this talk, I’ll present my recent efforts using Spark and MLLib to mine my personal cycling training data for deeper insights and help me design workouts to meet particular fitness goals. This work incorporates analysis of geographic and time-series data, computational geometry, visualization, and domain knowledge of exercise physiology. I’ll show how Spark made this work possible, demonstrate some novel techniques for analyzing fitness data, and discuss how these approaches could be applied to make sense of data from an entire community of cyclists.

Talk video