21,662 research outputs found

    A comparison of surface and motion user-defined gestures for mobile augmented reality.

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    Augmented Reality (AR) technology permits interaction between the virtual and physical worlds. Recent advancements in mobile devices allow for a better mobile AR experience, and in turn, improving user adoption rate and increasing the number of mobile AR applications across a wide range of disciplines. Nevertheless, the majority of mobile AR applications, that we have surveyed, adopted surface gestures as the default interaction method for the AR experience and have not utilised three-dimensional (3D) spatial interaction, as supported by AR interfaces. This research investigates two types of gestures for interacting in mobile AR applications, surface gestures, which have been deployed by mainstream applications, and motion gestures, that take advantages of 3D movement of the handheld device. Our goal is to find out if there exists a gesture-based interaction suitable for handheld devices, that can utilise the 3D interaction of mobile AR applications. We conducted two user studies, an elicitation study and a validation study. In the elicitation study, we elicited two sets of gestures, surface and motion, for mobile AR applications. We recruited twenty-one participants to perform twelve common mobile AR tasks, which yielded a total of five-hundred and four gestures. We classified and illustrated the two sets of gestures, and compared them in terms of goodness, ease of use, and engagement. The elicitation process yielded two separate sets of user-defined gestures; legacy surface gestures, which were familiar and easy to use by the participants, and motion gestures, which found to be more engaging. From the design patterns of the motion gestures, we proposed a novel interaction technique for mobile AR called TMR (Touch-Move-Release). To validate our elicited gestures in an actual application, we conducted a second study. We have developed a mobile AR game similar to Pokémon GO and implemented the selected gestures from the elicitation study. The study was conducted with ten participants, and we found that the motion gesture could provide more engagement and better game experience. Nevertheless, surface gestures were more accurate and easier to use. We discussed the implications of our findings and gave our design recommendations for designers on the usage of the elicited gestures. Our research can be further explored in the future. It can be used as a "prequel" to the design of better gesture-based interaction technique for different tasks in various mobile AR applications

    Tap 'N' Shake: Gesture-based Smartwatch-Smartphone Communications System

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    Smartwatches have recently seen a surge in popularity, and the new technology presents a number of interesting opportunities and challenges, many of which have not been adequately dealt with by existing applications. Current smartwatch messaging systems fail to adequately address the problem of smartwatches requiring two-handed interactions. This paper presents Tap 'n' Shake, a novel gesture-based messaging system for Android smartwatches and smartphones addressing the problem of two-handed interactions by utilising various motion-gestures within the applications. The results of a user evaluation carried out with sixteen subjects demonstrated the usefulness and usability of using gestures over two-handed interactions for smartwatches. Additionally, the study provides insight into the types of gestures that subjects preferred to use for various actions in a smartwatch-smartphone messaging system

    Mobiles and wearables: owner biometrics and authentication

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    We discuss the design and development of HCI models for authentication based on gait and gesture that can be supported by mobile and wearable equipment. The paper proposes to use such biometric behavioral traits for partially transparent and continuous authentication by means of behavioral patterns. © 2016 Copyright held by the owner/author(s)

    Designing gestures for affective input: an analysis of shape, effort and valence

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    We discuss a user-centered approach to incorporating affective expressions in interactive applications, and argue for a design that addresses both body and mind. In particular, we have studied the problem of finding a set of affective gestures. Based on previous work in movement analysis and emotion theory [Davies, Laban and Lawrence, Russell], and a study of an actor expressing emotional states in body movements, we have identified three underlying dimensions of movements and emotions: shape, effort and valence. From these dimensions we have created a new affective interaction model, which we name the affective gestural plane model. We applied this model to the design of gestural affective input to a mobile service for affective messages

    This Far, No Further: Introducing Virtual Borders to Mobile Robots Using a Laser Pointer

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    We address the problem of controlling the workspace of a 3-DoF mobile robot. In a human-robot shared space, robots should navigate in a human-acceptable way according to the users' demands. For this purpose, we employ virtual borders, that are non-physical borders, to allow a user the restriction of the robot's workspace. To this end, we propose an interaction method based on a laser pointer to intuitively define virtual borders. This interaction method uses a previously developed framework based on robot guidance to change the robot's navigational behavior. Furthermore, we extend this framework to increase the flexibility by considering different types of virtual borders, i.e. polygons and curves separating an area. We evaluated our method with 15 non-expert users concerning correctness, accuracy and teaching time. The experimental results revealed a high accuracy and linear teaching time with respect to the border length while correctly incorporating the borders into the robot's navigational map. Finally, our user study showed that non-expert users can employ our interaction method.Comment: Accepted at 2019 Third IEEE International Conference on Robotic Computing (IRC), supplementary video: https://youtu.be/lKsGp8xtyI
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