Humans are able to understand meaning intuitively and generalize from a single observation, as opposed to machines which require several examples to learn and recognize a new physical expression. This trait is one of the main roadblocks in natural human-machine interaction. Particularly, in the area of gestures which are an intrinsic part of human communication. In the aim of natural interaction with machines, a framework must be developed to include the adaptability humans portray to understand gestures from a single observation.
This framework includes the human processes associated with gesture perception and production. From the single gesture example, key points in the hands’ trajectories are extracted which have found to be correlated to spikes in visual and motor cortex activation. Those are also used to find inverse kinematic solutions to the human arm model, thus including the biomechanical and kinematic aspects of human production to artificially enlarge the number of gesture examples.
Leveraging these artificial examples, traditional state-of-the-art classification algorithms can be trained and used to recognize future instances of the same gesture class.
