As computers become more ubiquitous, traditional two-dimensional interfaces must be replaced with interfaces based on a three-dimensional metaphor. However, these interfaces must still be as simple and functional as their two-dimensional predecessors. This paper introduces SWiM, a new interface for moving application windows between various screens, such as wall displays, laptop monitors, and desktop displays, in a three-dimensional physical environment. SWiM was designed based on the results of initial "paper and pencil" user tests of three possible interfaces. The results of these tests led to a map-like interface where users select the destination display for their application from various icons. If the destination is a mobile display it is not displayed on the map. Instead users can select the screen's name from a list of all possible destination displays. User testing of SWiM was conducted to discover whether it is easy to learn and use. Users that were asked to use SWiM without any instructions found the interface as intuitive to use as users who were given a demonstration. The results show that SWiM combines simplicity and functionality to create an interface that is easy to learn and easy to use.
Moving application windows among various displays has been the focus of research in multiple ubiquitous computing environments. In i-Land, a room with an interactive electronic wall (DynaWall), computer-enhanced chairs, and an interactive table, three methods were introduced for moving application windows on the DynaWall. @cite_6 @cite_3 Two of these methods, shuffling and throwing, are implemented using gestures. Shuffling is done by drawing a quick left or right stroke above the title bar of a window. This will move the window a distance equal to the width of the window in the gestured direction. Throwing is done by making a short gesture backward, then a longer gesture forward. This will move the window a distance proportional to the ratio between the backward and forward movement. The throwing action requires practice because there is no clear indication of how far something will move prior to using it. The final method for moving windows in i-Land is taking. If a user's hand is placed on a window for approximately half a second, that window shrinks into the size of an icon. The next time the user touches any display, the window will grow behind the hand back to its original size.
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We envision a nomadic model of interaction where the personal computer fits in your pocket. Such a computer is extremely limited in screen space. A technique is described for "spilling" the display of a hand held computer onto a much larger table top display surface. Because our model of nomadic computing frequently involves the use of untrusted display services we restrict interactive input to the hand held. Navigation techniques such as scrolling or turning the display can be expressed through the table top. The orientation and position of the hand held on the table top is detected using three conductive feet that appear to the touch table like three finger touches. An algorithm is given for detecting the three touch positions from the table's sensing mechanism. A problem of matching gestures, where there are one or few samples per class, is considered in this paper. The proposed approach shows that much better results are achieved if the distance between the pattern of frame-wise distances of two gesture sequences with a third (anchor) sequence from the modelbase is considered. Such a measure is called as conditional distance and these distance pattern are referred to as "warp vectors". If these warp vectors are similar, then so are the sequences; if not, they are dissimilar. At the algorithmic core, there are two dynamic time warping processes, one to compute the warp vectors with the anchor sequences and the other to compare these warp vectors. In order to reduce the complexity a speedup strategy is proposed by pre-selecting "good" anchor sequences. Conditional distance is used for individual and sentence level gesture matching. Both single and multiple subject datasets are used. Experiments show improved performance above 82 spanning 179 classes. HighlightsWe propose a new distance measure called conditional distance between two gestures sequences when we have only one or a few samples per gesture class.Conditional distance is the distance between query and model gesture sequences in the presence of a third (anchor) gesture sequence.We propose speedup strategy for computing conditional distances by pre-selecting the anchor.We also propose a condition distance based simultaneous gesture segmentation and recognition called conditional level building.We show results of 82 on a multiple subject dataset spanning 179 classes. This paper presents a set of interaction techniques for hands-free multi-scale navigation through virtual environments. We believe that hands-free navigation, unlike the majority of navigation techniques based on hand motions, has the greatest potential for maximizing the interactivity of virtual environments since navigation modes are offloaded from modal hand gestures to more direct motions of the feet and torso. Not only are the users’ hands freed to perform tasks such as modeling, notetaking and object manipulation, but we also believe that foot and torso movements may inherently be more natural for some navigation tasks. The particular interactions that we developed include a leaning technique for moving small and medium distances, a foot-gesture controlled Step WIM that acts as a floor map for moving larger distances, and a viewing technique that enables a user to view a full 360 degrees in only a three-walled semi-immersive environment by subtly amplifying the mapping between their torso rotation and the virtual world. We formatively designed and evaluated our techniques in existing projects related to archaeological reconstructions, free-form modeling, and interior design. In each case, our informal observations have indicated that motions such as walking and leaning are both appropriate for navigation and are effective in cognitively simplifying complex virtual environment interactions since functionality is more evenly distributed across the body. Existing gesture segmentations use the backward spotting scheme that first detects the end point, then traces back to the start point and sends the extracted gesture segment to the hidden Markov model (HMM) for gesture recognition. This makes an inevitable time delay between the gesture segmentation and recognition and is not appropriate for continuous gesture recognition. To solve this problem, we propose a forward spotting scheme that executes gesture segmentation and recognition simultaneously. The start and end points of gestures are determined by zero crossing from negative to positive (or from positive to negative) of a competitive differential observation probability that is defined by the difference of observation probability between the maximal gesture and the non-gesture. We also propose the sliding window and accumulative HMMs. The former is used to alleviate the effect of incomplete feature extraction on the observation probability and the latter improves the gesture recognition rate greatly by accepting all accumulated gesture segments between the start and end points and deciding the gesture type by a majority vote of all intermediate recognition results. We use the predetermined association mapping to determine the 3D articulation data, which reduces the feature extraction time greatly. We apply the proposed simultaneous gesture segmentation and recognition method to recognize the upper-body gestures for controlling the curtains and lights in a smart home environment. Experimental results show that the proposed method has a good recognition rate of 95.42 for continuously changing gestures.