I am using inertial sensors to capture motion from the wrist/ ankles of subjects and synthesising this data into a feature set to be able to classify the activity performed by the subject (i.e. standing, walking, sitting).
On top of the metrics output by the sensors I would also like to be able to use the orientation of the limb segments for classification. I have been using an existing algorithm to determine the quaternions and can also convert these into Euler angles if need be.
My question is how best process these quaternions in order to feed them into a classifier to provide information about limb segment orientation to the classifier. The options for feeding the data into the classifier as far as I'm aware are as follows:
1) Feed each element of the quaterion as a feature: My concern is that each element of the quaternion is clearly non-linear and the classifier I use may not be sophisticated enough to be able to determine useful orientation information from this metric
2) Convert to Euler angles: This again suffers from a non-linear relationship with orientation relative to a world space depending on order of successive rotations (may be solved using extrinsic euler angles?)
3) Calculate Quaternion modulus from the non-unit quaternion: I have seen this method suggested in a paper on classification of EEG signals but I'm not sure of it's usefulness for this application. To be honest I'm not entirely sure on the what Quaternion modulus actually represents in real term other than for it's application in normalising quaternions. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4813911/#B40-sensors-16-00336
Many thanks in advance for any help!