Abstract: Provided is a method of recognizing patterns based on a hidden conditional random fields model to which full-Gaussian covariance has been applied. The method includes dividing a training input signal and outputting a frame sequence, extracting a feature vector from the frame sequence, calculating a parameter through a conditional random fields model to which Gaussian covariance has been applied using the feature vector, receiving, by the hidden conditional random fields model to which the parameter has been applied, a feature vector extracted from a test input signal measured for an actual pattern to infer a label indicating the actual pattern, and proposing a method of calculating gradient values for a conditional probability vector, a transition probability vector, a Gaussian mixture weight, a mean of Gaussian distributions, and covariance of the Gaussian distributions, as an analysis method.

Abstract: A method of recognizing an activity on the basis of a semi-Markov conditional random field (CRF) model is provided. The method includes segmenting an input signal measured by an accelerometer to output frame sequences, extracting training feature vectors from the frame sequences, building a codebook containing kernel vectors from the training feature vectors; quantizing vector sequences into discrete symbol sequences, using linear chain semi-Markov CRF model to compute the likelihood of a label given its corresponding symbol sequence.