An Introduction to New Features Being Tested for LEOMO’s Activity Dashboard

Hello, this is Saita from LEOMO. Today I’d like to introduce several new features currently being tested for our activity dashboard. As we have mentioned in previous blog entries, MPI characteristics differ greatly from cyclist to cyclist. They also undergo significant change across power, cadence, standing and other scenarios.

If we can automatically determine and extract MPI characteristics and changes for different cyclists depending on the scenario, this will greatly increase the efficiency of our analysis.

With this in mind, we are running several tests. Let’s begin by looking at the test dashboard screen.

On the upper part of the Ant+ data and GPS graph screen (inside the red box), three lines are displayed (Figure 1). These three lines indicate the following three items.

Figure 1: Enlarged view of detected data graph
  1. Orange: Standing detection
  2. Blue: Cornering detection
  3. Red: Interval detection

1. Standing Detection

The orange-lined portions represent the ranges in which standing was detected.

Figure 2: Example of standing detection

As we introduced in a previous blog entry (https://blog.leomo.io/using-pelvic-tilt-to-differentiate-between-sitting-and-standing-27171871c906), Pelvic Angle decreases while Pelvic Rotation and Pelvic Rock increase.
Looking at the sections in Figure 2 lined in orange, we can see where Pelvic Angle drops and where an increase in Pelvic Rotation and Pelvic Rock are detected.

2. Cornering Detection

The blue-lined portions represent the ranges in which cornering was detected. As we can see by comparing the data with the GPS points, the sections detected are those where cornering occurs.

Figure 3: Example of cornering detection

3. Interval Detection

The interval detection functionality detects instances such as the increased use of power during practice and races as well as interval practice. It is shown with a red line. We can see that, regardless of whether the cyclist is sitting or standing, the sections with significant power output are being detected (Figure 2).

By making use of these new features, we believe it will be possible to automatically detect and conduct MPI analysis for situations such as “during a TT, seated but not cornering” or “standing while climbing.” We’re looking forward to delivering these capabilities to all of you TYPE-R users!


Leave a comment

Please note, comments must be approved before they are published