I'm trying to recreate the results of a machine learning applied to the DSP classification in the article: link.
I have a signal (activity measurements from a smartwatch) per patient, so about 30 signals in total, quite long. For machine learning algorithm authors take traditional approach: extract statistical values about the signal, create columns (1 column per feature, 1 row per patient/signal) and plug this into a classifier. I'm confused about the spectral density used as feature.
If I take PSD of a signal, I get another signal (time series) with values, not a single value, am I right? Therefore I would get not 1 column with spectral density feature per patient, but a full signal.
Suppose the article is imprecise (it is in other places) and authors made some "mental shortcut" while writing this. What else could they mean by "spectral density" feature? It should be 1 column, so 1 number extracted from each signal from patient. They mention it is in frequency domain, so PSD would be right, but it would not produce a single number.