Monthly Archives: February 2014

Comparing activities in ConnectStats

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My activities are often on the same paths, so I wanted a way to compare activities. Of course segment in Strava are really useful in finding out how fast you can run or bike certain route, but I wanted a bit more control in what I was comparing. I also wanted a way that is not over complicated within the UI flow of connectstats. Here is what I came up with.

To compare an activity you need to first mark the activity that will compared to any other activity you will then look at. For that, you slide an activity in the list to the left, which reveals the Mark button.

Markactivity 6

After you press the button and the activity is then marked, note the mark icon on top of the activity icon.

Markedactivity 4

You can then look at any other activity and the graphs of the marked activity will then appear in the background of the current activity. Here I am looking at activities where I skied up the same slope with skins. It’s clear the first one my heart rate was much higher than the compared one. It was pretty much the same path up, so really just show how I adapted to the altitude over the course of the week, I think… Look at the HR histogram

SkiupHRHistogram 3

Or the rolling best plot

Skiupbestrolling 3

Now of course I claim I took the same path up, or did I? Hit the map and the two paths will appear. The compared path is slightly less opaque

Comparepaths 2

Here you see though large part of the ski up was the same, I didn’t start from the same exact same place, but arrived at the same destination.

Data relationships in a single activity

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Here we’ll explore different ways to look at two different attribute together in connectstats.

Let’s say we want to look at how the speed and heart rate relate to each other. The first approach is simply to plot the two series together on the same graph as below.

2fieldsplots 12

The arrows 1 and 2 shows that on the two short interval when the running speed increased, the heart rate increased as well.

One approach to relate the information a bit better is to use a color overlay to show the one information on the a single plot

2fieldsasline

The arrow 1, shows where the line becomes darker and red as the heart rate increased with the speed, the arrow 2 shows the heart rate slowing down as the color goes back to the green

The last and, in my opinion, most interesting way to relate the two series of data is the scatter plot.

Scatterplot 17

Here you can see the points (arrow 1) where the speed/heart rate increased together. Array 2 is the beginning of the run with lower heart rate until it stabilised in the region of arrow 3 for most of the run. The regression line shows how the main relationship between the 2 data goes, here slightly to the downward slopping as expected.

It is also interesting in the lap view to see where in the larger scatter plot a given lap appear as below

Scatterlap 9

The green points show for this mile lap that the beginning was quite fast/high heart rate (arrow 1) and then slow down of both speed and heart rate (arrow 2), and clearly at the lower end of the overall speed of the run.

Data and smoothing used in connectstats

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ConnectStats let us apply basics statistics technics to analyse our fitness data. We use these techniques to answer questions about the activities. How well did I do on this run? Is my stamina improving? Did I push myself more today that last week?

Raw Data

Before doing any analyses, let’s review the raw data we have access to. ConnectStats relies on the collection of the data is done with a garmin device and a collection of sensors.

The raw data varies by activity type and device.
For outdoor activities, the raw data will be gps coordinates, a timestamp, speed and values from different sensors: Heart Rate beat per minutes, cadence (legs or pedal), Power delivered on the bike, and sometimes more advance running dynamics like Vertical Oscillation, ground contact time. For swimming the data collected will be the time for each length and the number of stroke.

Here is an example of what the raw data looks like Blog txt

Depending on the activity or the device the raw data will be collected at fixed intervals or at different intervals trying to reduce the amount of data saved. If you move in a straight line for 5s at constant speed, it isn’t necessary to collect the points every seconds as they can easily be interpolated.

From this raw data, there are two main directions for the analysis we can do. The first is analysing a single activities and the second is analysing summary of each activities over time.

Focus on a single activity

The summary statistics are quite simple. Mostly we will want to look at the average of the different measure, maximum and minimum. Where it gets more interesting is to look at the series of data. As for most statistics analysis a first phase will be cleaning the data.

Data Cleaning

Let’s look at the graph of the data collected by the gps. You can see it is quite noisy, there is also large part of the data that is not really interesting to look at to. Some is gps noise, area with bad reception, pause due to stop lights,

Rawdatabad

To clean the data, we can first use assumption on expected constraints on the data. For example, define a minimum speed considered valid running speed, or a minimum valid heart rate to remove unreasonable spikes. Another technique used by connectstats is to filter points that have unrealistics acceleration.

FilterBadValues

The last smoothing technic we can apply is a moving average of a few points. Usually the app will use 5 points moving average for data, but it can be controlled in the sliding left settings window.

Filterbadnormalsmoothing

In the case below the graph start to be more readable now, especially with the 2 minutes moving average overlay, which shows the two short faster run intervals around 30min and 40min.