Simulating human movement behaviour based on artificial intelligence technology with the concept of replacing cognitive structure of human with an analogous mechanism in artificial technology systems. Gesture is the most primitive way of communication with human automated systems.
Large volume of microscopic human movement behaviour types were collected and encapsulated in artificial neural network and with the help of the above mentioned mechanism we can optimise the human mental process in terms of energy and time and just by working out on the algorithm it enables us to create the analogous mechanism that‘s nothing but a reflexive structure.
AI systems in real time which were not used before due to high time consumption and the entire study of human automation is based on information metabolism theory (IMT).
This paper proposes a novel gesture recognition and finger-count detection algorithm for automated human interactions. The body gestures are captured and the corresponding output is extracted.
The hand gesture recognition system based on the proposed methodology enables the use of affordable data glove with a small number of sensors. According to the literature, there are no similar solutions that allow efficient recognition of simple and complex static hand gestures, based on a hand gesture.