I have X, Y and Z co-ordinate of the movement patterns of a person for 30 days over some known physical layout. This is unevenly spaced time-series data with maximum frequency of 2Hz while in motion. It is known that a person can depicts one of the four patterns of locomotion i.e. lapping, pacing, random and direct. It has been defined as:
Lapping: Locomotion that has a circular path (closed loop).
Pacing: Back and forth locomotion between two end points.
Random: Locomotion along a haphazard path from source to destination.
Direct: Locomotion along a somewhat straight path from source to destination.
(Note: Source and destination are start and end of an episode. Episode is a smaller simpler navigation path which can have one of the pattern )
My aim is to find the pattern in an episode. What features I should extract from these dataset and which ML classification algorithm will be best suitable to deal with these types of problem.
I have split the movement into smaller simpler episodes where each episodes can be any of the four patters. Right now, I am using some heuristics to identify these patterns but I feel that ML can be very good to predict these patterns.
Thanks in advance :)