The motion of a rigid object is expressed in a way, in which it will hold certain invariant properties with respect to several contextual dependencies of the recorded motion. Frenet Serret and Instantaneous Screw Axis Invariant Descriptors are being used to describe a rigid body trajectory.
The motion recognition algorithms that are proposed, consist of a modified machine learning, distance-based, and a deep learning method. The amount of motions required to train the corresponding methods is kept to minimal and equal for every approach, so that a comparative basis can be established, and the invariance of the investigated motion representations favors the minimal amount of training data usage.
Motion Recognition Algorithms:
- Dynamic Time Warping (DTW-algorithm)
- DTW-based K-Nearest Neighbor
- LSTM network (Long short-term memory network)
Description of Motion Trajectory via:
- Frenet serret Descriptors
- Instantaneous Screw Axis Descriptors
Parameterizations:
- Time-based
- Geometric
- Dimensionless Geometric
Invariant Descriptors Calculation Types:
- Analytical formulas
- Optimized Approach