So, this max contributor was the body acceleration, the mean

body acceleration in the frequency domain for the z direction.

And so this was a, kind of the, the body acceleration.

For the z direction where they applied and you transform

and they give you the kind of frequency components from that.

So that's kind of interesting.

We can try another clustering technique here which is K-means clustering.

Ah,and one

of the things about k-means clustering that you have

to be a little bit careful about is that you

can get kind of different answers depending on, you

know how many times,starting values you've tried and how and

how often you run it so whenever you, when

you start k-means it has to chose a starting point

for where the cluster centers are often it will

just chose, most algorithms will chose a random starting point.

So if you chose a random starting point

you may get to a solution that is suboptimal.

So if you chose a different starting point you may get

to an even better solution.

And so it's usually good to set the nstart argument to be more than one so you can

start at many different starting points, just so you

can get the optimal, or, a more optimal solution.

So here is one clustering that we've done with k-means.

And you can see that the, I've specified six

centers, so I know that there are six clusters.

So I'll just specify them right away.

And you can see that the,

some of the clusters kind of jumble together.

So you can see cluster three is

a combination of laying, sitting, and standing.

Whereas cluster one is walking, cluster, clearly walking.

Cluster two is walking down.

Cluster four is walking up. Cluster five is just walking.

And again, and cluster six is a mixture of laying, sitting and standing.

And so you can see there, k-means here had a little bit, had trouble separating

out also the laying, sitting and standing from

the, the three, the in, in, in the clusters.