I am unsure about which approach is "better" (less time good accuracy) for classifying gestures:
The system is a doppler radar that returns I and Q signals
Approach 1: Have several subject perform gestures and label all gestures Then train a ML model (KNN, SVM) cross-validate etc... This approach requires that I collect a lot of data from several subjects
Approach 2: Use peak analysis, finding the number of peaks that represent a gesture I also use the distance between peaks as a characteristic of a gesture This requires no training data but rather looking at the raw data determining peaks and distances between peaks and mapping that to some criteria for a gesture This saves a lot of time and I don't have to collect so much data from subjects
It seems as though the solution is always to use machine learning, but I could easily do this by observing peaks.
How can I determine what approach to use?