With low expectations, I thought I'd throw a simple classifier at the data records to see if it could differentiate between sleep, mundane activity, and strenuous activity.
The results surprised me!
Top: GoWearFit activity graph, Middle: k-means classifier, Bottom, GoWearFit Sleep Graph. Click graphic for full resolution.
I used a simple k-means clustering function out of the Open Source "R" statistical package (via the Python RPy interface). I did _NO_ normalization on the data at all. I just read in the tab-delimited data, stripped off the date fields, and fed it to the classifier.
Each 1-minute data record became a point in 27-dimensional space, and I told the algorithm to divide them into 3 classes (hoping they would end up being "sleep", "vigorous", and "moderate"). And it simply worked!
The black-grey-and-white graph above is the output of the classifier for each minute in a ~18 hour period. The blue graphs are the graphs provided by the GoWearFit web report, aligned manually.
The peaks at 10:45am and Noon were bike rides/walks. I took an afternoon nap, and went to sleep around 11:30pm.