After exploring some visualizations of kayaking, I was eager to see how a similar approach could work for walking. On a trip to the Norwegian mountains, specifically at Haugastøl, situated halfway between Oslo and Bergen, I strapped a GoPro Hero Black 10 on my chest and walked up and down a nearby hill called Storevarden. The walk was approximately 25 minutes up and down, and a fast-forward version of the video can be seen here:

What can one get from the audio and video of such a trip? Here are some results generated with various functions from the Musical Gestures Toolbox for Python.

Static visualizations

The first trial was to create some static visualizations from the video recording.

A keyframe image display shows nine sampled images from the video. The first ones mainly show the path since I was leaning forward while walking upward, and the last show the scenery.

An average image of the whole video does not tell much in this case, and I guess it shows that (on average) I looked up most of the time. Hence the horizon can be seen toward the bottom of the image.

The average image is not particularly interesting in this case. Then it may be better to create a history video that averages images over a shorter period, such as in this video:

A history video is averaging over several seconds of video footage. Still quite shaky, but it creates an interesting soft-focus rendition of the video. This may resemble how I perceived the scenery as I walked up and down.

Videograms

A better visualization, then, are the videograms, which give more information about the spatiotemporal features of the video recording.

A horizontal videogram of the 25-minute walking sequence reveals the spatiotemporal differences in the recording: first walking upward facing the ground, then having a short break on the top, and then walking downward facing the scenery.

A vertical videogram is less interesting in this case.

Motiongrams

The videograms are based on collapsing the original images in the video sequence. Motiongrams, on the other hand, collapse the motion image sequence, clearly showing what changed between frames.

A horizontal motiongram reveals the same information as the videogram and clearly shows the break I took in the middle. (the black part in the middle).

A vertical motiongram is not particularly relevant.

Audio analysis

What can one get out of the audio recording of walking? The waveform does not tell much, except that the average levels look higher in the second half (where I was walking down).

A waveform of the audio that I recorded during the 25-minute walking.

The sonogram shows a lot of energy throughout the energy spectrum, and my break at the top can be seen a little over halfway through. A peculiar black line at 8.7 kHz has to come from the GoPro, and the camera also cuts all sound above approximately 13 kHz.

The tempogram also reveals the break in the middle and estimates a tempo of my walking of almost 120 BPM.

It is fascinating how the estimated tempo of my walking was almost 120 BPM, which happens to be similar to the 2 Hz frequency found in many studies of walking and everyday activities. It will be interesting to try a similar approach for other walking videos.