Remote and Deviceless Manipulation of Virtual Objects in Mixed Reality

Deviceless manipulation of virtual objects in mixed reality (MR) environments is technically achievable with the current generation of Head-Mounted Displays (HMDs), as they track finger movements and allow you to use gestures to control the transformation. However, when the object manipulation is performed at some distance, and when the transform includes scaling, it is not obvious how to remap the hand motions over the degrees of freedom of the object. Different solutions have been implemented in software toolkits, but there are still usability issues and a lack of clear guidelines for the interaction design. We present a user study evaluating three solutions for the remote translation, rotation, and scaling of virtual objects in the real environment without using handheld devices. We analyze their usability on the practical task of docking virtual cubes on a tangible shelf from varying distances. The outcomes of our study show that the usability of the methods is strongly affected by the use of separate or integrated control of the degrees of freedom, by the use of the hands in a symmetric or specialized way, by the visual feedback, and by the previous experience of the users

Barehand Mode Switching in Touch and Mid-Air Interfaces

Raskin defines a mode as a distinct setting within an interface where the same user input will produce results different to those it would produce in other settings. Most interfaces have multiple modes in which input is mapped to different actions, and, mode-switching is simply the transition from one mode to another. In touch interfaces, the current mode can change how a single touch is interpreted: for example, it could draw a line, pan the canvas, select a shape, or enter a command. In Virtual Reality (VR), a hand gesture-based 3D modelling application may have different modes for object creation, selection, and transformation. Depending on the mode, the movement of the hand is interpreted differently. However, one of the crucial factors determining the effectiveness of an interface is user productivity. Mode-switching time of different input techniques, either in a touch interface or in a mid-air interface, affects user productivity. Moreover, when touch and mid-air interfaces like VR are combined, making informed decisions pertaining to the mode assignment gets even more complicated. This thesis provides an empirical investigation to characterize the mode switching phenomenon in barehand touch-based and mid-air interfaces. It explores the potential of using these input spaces together for a productivity application in VR. And, it concludes with a step towards defining and evaluating the multi-faceted mode concept, its characteristics and its utility, when designing user interfaces more generally

To Draw or Not to Draw: Recognizing Stroke-Hover Intent in Gesture-Free Bare-Hand Mid-Air Drawing Tasks

Over the past several decades, technological advancements have introduced new modes of communication with the computers, introducing a shift from traditional mouse and keyboard interfaces. While touch based interactions are abundantly being used today, latest developments in computer vision, body tracking stereo cameras, and augmented and virtual reality have now enabled communicating with the computers using spatial input in the physical 3D space. These techniques are now being integrated into several design critical tasks like sketching, modeling, etc. through sophisticated methodologies and use of specialized instrumented devices. One of the prime challenges in design research is to make this spatial interaction with the computer as intuitive as possible for the users. Drawing curves in mid-air with fingers, is a fundamental task with applications to 3D sketching, geometric modeling, handwriting recognition, and authentication. Sketching in general, is a crucial mode for effective idea communication between designers. Mid-air curve input is typically accomplished through instrumented controllers, specific hand postures, or pre-defined hand gestures, in presence of depth and motion sensing cameras. The user may use any of these modalities to express the intention to start or stop sketching. However, apart from suffering with issues like lack of robustness, the use of such gestures, specific postures, or the necessity of instrumented controllers for design specific tasks further result in an additional cognitive load on the user. To address the problems associated with different mid-air curve input modalities, the presented research discusses the design, development, and evaluation of data driven models for intent recognition in non-instrumented, gesture-free, bare-hand mid-air drawing tasks. The research is motivated by a behavioral study that demonstrates the need for such an approach due to the lack of robustness and intuitiveness while using hand postures and instrumented devices. The main objective is to study how users move during mid-air sketching, develop qualitative insights regarding such movements, and consequently implement a computational approach to determine when the user intends to draw in mid-air without the use of an explicit mechanism (such as an instrumented controller or a specified hand-posture). By recording the user’s hand trajectory, the idea is to simply classify this point as either hover or stroke. The resulting model allows for the classification of points on the user’s spatial trajectory. Drawing inspiration from the way users sketch in mid-air, this research first specifies the necessity for an alternate approach for processing bare hand mid-air curves in a continuous fashion. Further, this research presents a novel drawing intent recognition work flow for every recorded drawing point, using three different approaches. We begin with recording mid-air drawing data and developing a classification model based on the extracted geometric properties of the recorded data. The main goal behind developing this model is to identify drawing intent from critical geometric and temporal features. In the second approach, we explore the variations in prediction quality of the model by improving the dimensionality of data used as mid-air curve input. Finally, in the third approach, we seek to understand the drawing intention from mid-air curves using sophisticated dimensionality reduction neural networks such as autoencoders. Finally, the broad level implications of this research are discussed, with potential development areas in the design and research of mid-air interactions

Taking time to understand: articulating relationships between technologies and organizations

Dynamic relationships between technologies and organizations are investigated through research on digital visualization technologies and their use in the construction sector. Theoretical work highlights mutual adaptation between technologies and organizations but does not explain instances of sustained, sudden, or increasing maladaptation. By focusing on the technological field, I draw attention to hierarchical structuring around inter-dependent levels of technology; technological priorities of diverse groups; power asymmetries and disjunctures between contexts of development and use. For complex technologies, such as digital technologies, I argue these field-level features explain why organizations peripheral to the field may experience difficulty using emerging technology