Method and system for gesture category recognition and training using a feature vector

In an electronic system having a processor, a memory unit, an alphanumeric input device and a cursor directing device, a method of determining a feature vector representing a gesture comprising the computer implemented steps of:

• a) accessing gesture data representing a gesture formed by tracking movement of a cursor moved by a user with said cursor directing device, said gesture data comprising coordinate positions and timing information and having one or more individual strokes;
• b) normalizing said gesture data;
• c) dividing each stroke of said gesture data into a plurality of segments, N;
• d) determining first feature elements for a respective stroke of said gesture data based on an end point of said respective stroke and a start point of a next stroke, wherein step d) is performed for each stroke of said gesture data;
• and
• e) determining second feature elements for each segment of each stroke of said gesture data based on an orientation of each segment with respect to a reference line, wherein said feature vector comprises:
• a value indicating the number of strokes of said gesture data;
• said first feature elements for each stroke;
• and said second feature elements for each segment of each stroke.

In an electronic system having a processor, a memory unit, an alphanumeric input device and a cursor directing device, a method of training said system to recognize gestures comprising the computer implemented steps of:

• a) defining a new gesture category name;
• b) accessing gesture data representing a gesture formed by tracking movement of a cursor moved by a user with said cursor directing device, said gesture data comprising coordinate positions and timing information and having one or more individual strokes;
• c) generating a feature vector based on said gesture data;
• d) using said feature vector to generate a bounded area within a predefined space associated with a radial basis function neural network;
• e) associating said bounded area with said new gesture category name within a predefined plurality of gesture categories stored in said memory unit;
• and
• f) associating a set of predetermined commands to said new gesture category name, said set of predetermined commands for application to said electronic system upon said new gesture category name being recognized by said radial basis function neural network.

In an electronic system having a processor, a memory unit, an alphanumeric input device and a cursor directing device, a method of training said system to recognize gestures comprising the computer implemented steps of:

• a) identifying an existing gesture category name within a predefined set of gesture categories stored within said memory unit;
• b) accessing gesture data representing a gesture formed by tracking movement of a cursor moved by a user with said cursor directing device, said gesture data comprising coordinate positions and timing information and having one or more individual strokes;
• c) generating a feature vector based on said gesture data;
• and
• d) using said feature vector to modify a pre-existing bounded area within a predefined space associated with a radial basis function neural network, said pre-existing bounded area being associated with said existing gesture category.

An electronic system comprising:

• a processor coupled to a bus;
• an alphanumeric input device and a cursor directing device coupled to said bus;
• and
• a memory unit coupled to said bus, said memory unit containing instructions that when executed implement a method of providing a user interface comprising the steps of:
• a) accessing gesture data representing a gesture formed by tracking movement of a cursor moved by a user with said cursor directing device, said gesture data comprising coordinate positions and timing information and having one or more individual strokes;
• b) generating a multi-dimensional feature vector based on said gesture data;
• c) providing said multi-dimensional feature vector to a radial basis function neural network for recognition, said radial basis function neural network associating said multi-dimensional feature vector with a gesture category from a predefined plurality of gesture categories and supplying said gesture category as an output value;
• and
• d) applying a set of predetermined commands to said electronic system, said set of predetermined commands being associated with said gesture category output from said radial basis function neural network.