Automatic body movement recognition and association system including smoothing, segmentation, similarity, pooling, and dynamic modeling

1 . A method for smoothing a sequence of body movement data of a user comprising:

receiving the sequence of body movement data from one of a camera, a video, and a sensor, wherein the sequence of body movement data comprises one of a set of two-dimensional coordinates of a plurality of points of a skeleton of the user and a set of three-dimensional coordinates of a plurality of points of a skeleton of the user;

fine tuning the sequence of body movement data into a smoothed sequence of body movement data on a condition that the sequence does not contain errors;

outputting the smoothed sequence of body movement data;

smoothing the sequence based on a weighted moving average with a smallest window size to determine a first sequence;

determining a reverse sequence based on the sequence;

smoothing the reverse sequence based on the weighted moving average with the smallest window size;

reversing the reverse sequence to determine a second sequence;

determining an average sequence based on the first sequence and the second sequence; and

outputting a fine tuned smoothed sequence based on the average sequence.

2 . A method for segmenting a sequence of body movement data of a user comprising:

receiving the sequence of body movement data from one of a camera, a video, and a sensor, wherein the sequence of body movement data comprises one of a set of two-dimensional coordinates of a plurality of points of a skeleton of the user and a set of three-dimensional coordinates of a plurality of points of a skeleton of the user;

processing, using a preprocessing component, the sequence of body movement data into a preprocessed sequence of body movement data;

determining a sequence curvature across time based on the preprocessed sequence of body movement data;

initializing a curvature model into an initialized curvature model;

fitting initialized curvature model on sequence curvature across time;

labeling a plurality of curvature frames across time based on the initialized curvature model;

initializing a previous gradient direction to a neutral direction;

determining the previous gradient direction and a current gradient direction based on the labeled plurality of curvature frames; and

identifying the plurality of initial segments of body movement data by checking for gradient direction shift from increasing to decreasing gradient.

3 . The method of claim 2 , further comprising:

receiving one of the initial segments of body movement data and the plurality of initial segments of body movement data;

identifying a dynamic forward model based on one of the initial segments of body movement data and the plurality of initial segments of body movement data;

segmenting one of the initial segments of body movement data and the plurality of initial segments of body movement data based on the dynamic forward model; and

identifying one of a segment of body movement data and a plurality of segments of body movement data.