I have a big sample of data on a human's everyday life. The snapshots of the life are taken every 5 minutes. The data include time, the location of the human, accelerometer data, gyroscope data, and others.
The data are unlabeled and I know that there have to be 5 main clusters: "sleeping", "eating", "exercising", and "working"...
Before I made assumptions, e.g., "if human's location is x and if the accelerometer value is y and gyroscope value is z, duration is k, then human is sleeping". I know this may create issues as sometimes there may be a different label for those precise values. Therefore, I thought of using time-series clustering for my problem.
How do I approach it? Or am I complicating the solution and I should simply use my "if var1 value = x, var2 value = x2, then the label is N"?
As I mentioned, the data are not labeled, but I know there should be 5 labels.
I appreciate your help to put me on track.