In the course of a project on changepoints methods for a time series course, I found that that there is not a lot of literature documenting analysis of heart rate and fitness tracking data. Presumably there are some neat analytics in a product like Strava Premium, but these methods are proprietary. Since the data are relatively straightforward (pace, heartrate, and altitude), it would be neat to explore whether there are some easy insights in the data.
For this project, I looked at heart rate variability (HRV), which is commonly known as a metric for recovery. It seems like it's also a good indicatory of "activity types." See below for a quick demonstration of changepoint in variance methods on heart rate data collected by me on the Mailbox Peak hike in North Bend, WA (8,690 m, 1,220 m elevation gain) using a Suunto Ambit2 HR watch and heart rate belt. Although heart rate data is discrete, I found that lag-1 differenced heart rate is approximately normal for a given activity (e.g., running) with minimal autocorrelation beyond lag 0. Thus the assumption of an i.i.d. Gaussian process appeared to be reasonable for these data.
Applying the PELT method to detect changepoints in variance with a normal likelihood cost appeared to identify different activities and phases in the hike: an initial "try hard" phase where pace was erratic, a cardiovascular phase where heart rate variability was lower as pace settled, a rest phase (around the discontinuity in the data), and the descent phase. The binary segmentation method with a cumulative sums of squares cost identified a few additional changepoints, suggesting possible overfitting. Note that we omitted some outliers corresponding to short breaks (visible in the figure) to clean up the output.