I have a dataset containing raw 3-axis accelerometer data collected from a users lower leg and I want to create a classification model (as simple as possible) that detects if the user is sat down or standing. I have around 50,000 events (with more coming in) and these events are labeled with the correct posture and have the raw acceleration for the duration of the posture. I've created a handful of features from the raw data (e.g signal mean, range, frequency content etc) for each event but none of these clearly distinguish the events from each other (when visualising the dataset).
Is there a way of automatically generating useful features from labeled raw data that enables good seperation between outcomes?
If this is not possible, is it best to create a feature set with all the features you can come up with and then try to find the ones that explain the most variance between the outcomes? How do you do this? I've looked at PCA and LDA but they don't appear to 'pick' the best features, they just combine them to new components.
Finally, does anyone have any ideas on features that could explain the differences in standing and sitting lower leg movement. I assume that the lower leg can move in more extreme angles when sitting compared to standing but how do you describe this in features.