METHOD AND APPARATUS FOR DETECTING MODE OF MOTION WITH PRINCIPAL COMPONENT ANALYSIS AND HIDDEN MARKOV MODEL

摘要

A method, computer-readable storage device and apparatus for determining a mode of motion are disclosed. For example, a method receives training data comprising gait information associated with a plurality of different modes of motion. The method performs principal component analysis on the training data to extract principal components from the training data and generates a hidden markov model for each of a plurality of different modes of motion based upon the training data. The method receives testing data comprising gait information, transforms the testing data based upon the principal components and calculates a likelihood of the testing data based upon each hidden markov model for each of the plurality of different modes of motion. The method determines the mode of motion of the testing data, where the mode of motion is one of the plurality of different modes of motion for which a highest likelihood is calculated.

主权项

1. A method for determining a mode of motion, comprising:
receiving, by a processor, training data, wherein the training data comprises gait information associated with a plurality of different modes of motion; performing, by the processor, principal component analysis on the training data to extract principal components from the training data; generating, by the processor, a hidden markov model for each of a plurality of different modes of motion based upon the training data; receiving, by the processor, testing data comprising gait information; transforming, by the processor, the testing data based upon the principal components; calculating, by the processor, a likelihood of the testing data based upon each hidden markov model for each of the plurality of different modes of motion; and determining, by the processor, the mode of motion of the testing data, wherein the mode of motion is one of the plurality of different modes of motion for which a highest likelihood is calculated.