Abstract: A method for learning a person movement model includes obtaining a data stream having time series of measured multi-dimensional movement data of the person from at least one sensor; segmenting the data into time segments, segment corresponding to one movement step; storing each segment into a first storage section with a predetermined memory size, performing clustering of the segments by computing distances between the segments, learning a two-dimensional non-linear topology preserving embedded feature vector based on the computed distances, clustering the vectors, aligning segments of each cluster and averaging the aligned segments of for generating a prototype segment for each cluster; generating and storing a movement model by comparing the determined number of clusters of a current clustering with the determined number of clusters from the previous clustering stored in a second storage section.

Abstract: A method for learning a person movement model includes obtaining a data stream having time series of measured multi-dimensional movement data of the person from at least one sensor; segmenting the data into time segments, segment corresponding to one movement step; storing each segment into a first storage section with a predetermined memory size, performing clustering of the segments by computing distances between the segments, learning a two-dimensional non-linear topology preserving embedded feature vector based on the computed distances, clustering the vectors, aligning segments of each cluster and averaging the aligned segments of for generating a prototype segment for each cluster; generating and storing a movement model by comparing the determined number of clusters of a current clustering with the determined number of clusters from the previous clustering stored in a second storage section.