Abstract Designing of touchless user interface is gaining popularity in various contexts. Users can interact with electronic devices using such interfaces even when their hands are dirty or non-conductive. Also, users with partial physical disability can interact with electronic devices with the help of touchless interfaces. In this paper, we propose a Leap Motion controller-based methodology to facilitate rendering of 2D and 3D shapes on display devices. The proposed method tracks finger movements while users perform natural gestures within the field of view of the motion sensor. Then, trajectories are analyzed to extract extended Npen++ features in 3D. These features capture finger movements during the gestures and they are fed to unidirectional left-to-right Hidden Markov Model (HMM) for training. A one-to-one mapping between gestures and shapes, is proposed. Finally, the shapes corresponding to these gestures are rendered over the display using a typical MuPad supported interface. We have created a dataset of 5400 samples recorded by 10 volunteers. Our dataset contains 18 geometric and 18 non-geometric shapes such as “circle”, “rectangle”, “flower”, “cone”, “sphere”, etc. The proposed method has achieved 92.87 accuracy using a 5-fold cross validation scheme. Experiments reveal that the extended 3D features perform better than the existing 3D features when applied for shape representation and classification. The method can be used for developing diverse HCI applications suitable for smart display devices.
@cite_41: This paper describes a computer-aided design system for sketching free-form polygonal surfaces such as terrains and other natural objects. The user manipulates two 3D position and orientation trackers with three buttons, one for each hand. Each hand has a distinct role to play, with the dominant hand being responsible for picking and manipulation, and the less dominant hand being responsible for context setting of various kinds. The less dominant hand holds the workpiece, sets which refinement level that can be picked by the dominant hand, sets the constraint mode and the reshape operator, and generally acts as a counterpoint to the dominant hand. In this paper, the architecture of the system is outlined, the interaction techniques are presented, and a simple surface is shown.
@cite_29: 
@cite_2: While many sophisticated 3D rendering methods become available to produce realistic output, 3D data input still is a tedious and time-consuming task. The paper proposes a method for creating 3D objects using hand gestures. Two types of 3D shape forming tasks are considered. One is to create a completely new object from scratch and the other is to make it by changing an existing one. In the first case, a 3D modeling method is designed and implemented to provide functions like creation, blending and deformation of multiple 3D shapes easily controlled by intuitive bimanual gestures. The implicit superquadric functions are applied to building a gesture-based 3D object modeler with extensions of blending and deformations. In the second case, a 3D data management function is proposed to share and reuse existing shapes created by using the 3D modeler mentioned above. All 3D data are kept small in size and retrieved by picturing partial shapes with the user's hands. As a consequence, the proposed method allows users to consistently deal with all object modeling tasks including creation, storage and reuse of 3D objects through natural and intuitive hand gestures. Some 3D objects and a miniature solid model are shown to present the expressive power and usefulness of the proposed method.
Lately, gesture based 3D shape creation and recognition has been a topic of research. The VIDEODESK system proposed by @cite_29 can be considered as the pioneering work in this field. They have designed a system that allows a user to control an object's shape by using his her own hands. Whereas utilization of both hands of the user is of good advantage, however, their system uses predefined points of a user's hands. Thus, the system fails to take advantage of full expressive power of both hands. Researchers have shown that, the accuracy can be improved using both hands @cite_41 . Though the improved framework can assign different responsibility to each hand and the method suggests to use bi-manual actions than Krueger's method in the context of 3D shape modeling, the object deformation is only controlled by position and orientation of both hands. Therefore, shape of the hand is not used in true sense. This has been improved by @cite_2 . They have shown that, representation of 3D objects can be improved through bi-manual actions. However, above mentioned methodologies assume that, palm including fingers are detected and tracked precisely for gesture recognition to be effective.