3D Modelling of Human Hand with Motion Constraints

This paper describes the gestures and features of human hand motion, by considering the natural motion constraints of a hand. There are many restrictions that cannot be represented straight forwardly. Our aim is to reduce motion constraints by proposing some learning approaches to analyse and collect the dataset of motion behaviour of hand. The learning model involves application of Heat Map technology, along with usage of machine learning algorithms. We will implement some data mining algorithms to improve the accuracy of results and reduce redundancy in the testing data of hands space. This will result in better forecasting and prediction capability. Also we are using a microprocessor chip which will use sonar/radar for capturing ultrasonic waves, generated due to movement done by hands.