Figure 1: Grasping Microgestures enable direct and subtle interactions with computer systems while holding an everyday object. This paper presents empirical results from an elicitation study with varied objects, investigating the effect of grasp and object size on user's choice of microgestures, preferred locations, and fingers used.

Figure 1: SmartSleeve is a wearable textile that can detect 2D surface and 2.5D deformation gestures, like twist (a). We use an unobtrusive and robust sewn-based connection (b), which withstands high deformation gestures (c). The force distribution values of the gestures (d) are further processed for real-time classification with a hybrid gesture detection algorithm (e) to control a media player (f), for example.

No abstract

No abstract

The number of vehicles is increasing rapidly in urban areas and metropolitan cities with in crease in population. To manage such a huge amount of traffic in an efficient way is the need of the hour. The conventional traffic signalling system are not adaptable to change in the amount of traffic. This paper solves the problem by making traffic signals smart enough to distinguish between the lanes which have different densities of traffic present in them. This paper makes use of image processing with the help of stored data set of respective lane images and a microprocessor to compute an output.