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What is interesting is how quickly the classification of movements as correct increases.
Based on this analysis, an algorithm was developed for automatic classification of movements in the small intestine [ 29].
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Tests and clinical presentation allow a further classification of movement control dysfunctions according to direction in extension, flexion, frontal plane and multi-directional MCIs.
An algorithm was applied for classification of fast movements, slow movements, and very slow movements and by comparing small intestinal contraction frequencies.
In particular, we derive equi-affine differential invariants and signature curves of the observed movements which can be used for the identification and classification of motion primitives and for movement segmentation.
The objective of the work is to investigate the classification of different movements based on the SEMG pattern recognition method.
The goal of the Morph Server is to provide visualization and classification of protein movements.
The prefrontal area was sufficient to allow classification of different movements through the area's premovement signals (−2.0 s to 0 s) in four subjects.
We preferred experimental nuclear over membrane label because knowledge of the exact positions of the nucleus as center of gravity of the cells facilitates analysis and classification of cell movements.
In addition, at a low temporal resolution, boundaries of mitochondrial movement states were often obscure, leading to incorrect classification of mitochondrial movement states.
Furthermore, the classification of attack movement in cases such as whether the motion was a straight punch motion or an upper cut motion is possible.
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