Around-Body Interaction: Leveraging Limb Movements for Interacting in a Digitally Augmented Physical World

Recent technological advances have made head-mounted displays (HMDs) smaller and untethered, fostering the vision of ubiquitous interaction with information in a digitally augmented physical world. For interacting with such devices, three main types of input - besides not very intuitive finger gestures - have emerged so far: 1) Touch input on the frame of the devices or 2) on accessories (controller) as well as 3) voice input. While these techniques have both advantages and disadvantages depending on the current situation of the user, they largely ignore the skills and dexterity that we show when interacting with the real world: Throughout our lives, we have trained extensively to use our limbs to interact with and manipulate the physical world around us. This thesis explores how the skills and dexterity of our upper and lower limbs, acquired and trained in interacting with the real world, can be transferred to the interaction with HMDs. Thus, this thesis develops the vision of around-body interaction, in which we use the space around our body, defined by the reach of our limbs, for fast, accurate, and enjoyable interaction with such devices. This work contributes four interaction techniques, two for the upper limbs and two for the lower limbs: The first contribution shows how the proximity between our head and hand can be used to interact with HMDs. The second contribution extends the interaction with the upper limbs to multiple users and illustrates how the registration of augmented information in the real world can support cooperative use cases. The third contribution shifts the focus to the lower limbs and discusses how foot taps can be leveraged as an input modality for HMDs. The fourth contribution presents how lateral shifts of the walking path can be exploited for mobile and hands-free interaction with HMDs while walking.Comment: thesi

Design and semantics of form and movement (DeSForM 2006)

Design and Semantics of Form and Movement (DeSForM) grew from applied research exploring emerging design methods and practices to support new generation product and interface design. The products and interfaces are concerned with: the context of ubiquitous computing and ambient technologies and the need for greater empathy in the pre-programmed behaviour of the ‘machines’ that populate our lives. Such explorative research in the CfDR has been led by Young, supported by Kyffin, Visiting Professor from Philips Design and sponsored by Philips Design over a period of four years (research funding £87k). DeSForM1 was the first of a series of three conferences that enable the presentation and debate of international work within this field: • 1st European conference on Design and Semantics of Form and Movement (DeSForM1), Baltic, Gateshead, 2005, Feijs L., Kyffin S. & Young R.A. eds. • 2nd European conference on Design and Semantics of Form and Movement (DeSForM2), Evoluon, Eindhoven, 2006, Feijs L., Kyffin S. & Young R.A. eds. • 3rd European conference on Design and Semantics of Form and Movement (DeSForM3), New Design School Building, Newcastle, 2007, Feijs L., Kyffin S. & Young R.A. eds. Philips sponsorship of practice-based enquiry led to research by three teams of research students over three years and on-going sponsorship of research through the Northumbria University Design and Innovation Laboratory (nuDIL). Young has been invited on the steering panel of the UK Thinking Digital Conference concerning the latest developments in digital and media technologies. Informed by this research is the work of PhD student Yukie Nakano who examines new technologies in relation to eco-design textiles

Evaluation of AI-Supported Input Methods in Augmented Reality Environment

Augmented Reality (AR) solutions are providing tools that could improve applications in the medical and industrial fields. Augmentation can provide additional information in training, visualization, and work scenarios, to increase efficiency, reliability, and safety, while improving communication with other devices and systems on the network. Unfortunately, tasks in these fields often require both hands to execute, reducing the variety of input methods suitable to control AR applications. People with certain physical disabilities, where they are not able to use their hands, are also negatively impacted when using these devices. The goal of this work is to provide novel hand-free interfacing methods, using AR technology, in association with AI support approaches to produce an improved Human-Computer interaction solution

FlexType: Flexible Text Input with a Small Set of Input Gestures

In many situations, it may be impractical or impossible to enter text by selecting precise locations on a physical or touchscreen keyboard. We present an ambiguous keyboard with four character groups that has potential applications for eyes-free text entry, as well as text entry using a single switch or a brain-computer interface. We develop a procedure for optimizing these character groupings based on a disambiguation algorithm that leverages a long-span language model. We produce both alphabetically-constrained and unconstrained character groups in an offline optimization experiment and compare them in a longitudinal user study. Our results did not show a significant difference between the constrained and unconstrained character groups after four hours of practice. As expected, participants had significantly more errors with the unconstrained groups in the first session, suggesting a higher barrier to learning the technique. We therefore recommend the alphabetically-constrained character groups, where participants were able to achieve an average entry rate of 12.0 words per minute with a 2.03% character error rate using a single hand and with no visual feedback

Dynamic motion coupling of body movement for input control

Touchless gestures are used for input when touch is unsuitable or unavailable, such as when interacting with displays that are remote, large, public, or when touch is prohibited for hygienic reasons. Traditionally user input is spatially or semantically mapped to system output, however, in the context of touchless gestures these interaction principles suffer from several disadvantages including memorability, fatigue, and ill-defined mappings. This thesis investigates motion correlation as the third interaction principle for touchless gestures, which maps user input to system output based on spatiotemporal matching of reproducible motion. We demonstrate the versatility of motion correlation by using movement as the primary sensing principle, relaxing the restrictions on how a user provides input. Using TraceMatch, a novel computer vision-based system, we show how users can provide effective input through investigation of input performance with different parts of the body, and how users can switch modes of input spontaneously in realistic application scenarios. Secondly, spontaneous spatial coupling shows how motion correlation can bootstrap spatial input, allowing any body movement, or movement of tangible objects, to be appropriated for ad hoc touchless pointing on a per interaction basis. We operationalise the concept in MatchPoint, and demonstrate the unique capabilities through an exploration of the design space with application examples. Finally, we explore how users synchronise with moving targets in the context of motion correlation, revealing how simple harmonic motion leads to better synchronisation. Using the insights gained we explore the robustness of algorithms used for motion correlation, showing how it is possible to successfully detect a user's intent to interact whilst suppressing accidental activations from common spatial and semantic gestures. Finally, we look across our work to distil guidelines for interface design, and further considerations of how motion correlation can be used, both in general and for touchless gestures

Gaze gesture based human robot interaction for laparoscopic surgery

While minimally invasive surgery offers great benefits in terms of reduced patient trauma, bleeding, as well as faster recovery time, it still presents surgeons with major ergonomic challenges. Laparoscopic surgery requires the surgeon to bimanually control surgical instruments during the operation. A dedicated assistant is thus required to manoeuvre the camera, which is often difficult to synchronise with the surgeon’s movements. This article introduces a robotic system in which a rigid endoscope held by a robotic arm is controlled via the surgeon’s eye movement, thus forgoing the need for a camera assistant. Gaze gestures detected via a series of eye movements are used to convey the surgeon’s intention to initiate gaze contingent camera control. Hidden Markov Models (HMMs) are used for real-time gaze gesture recognition, allowing the robotic camera to pan, tilt, and zoom, whilst immune to aberrant or unintentional eye movements. A novel online calibration method for the gaze tracker is proposed, which overcomes calibration drift and simplifies its clinical application. This robotic system has been validated by comprehensive user trials and a detailed analysis performed on usability metrics to assess the performance of the system. The results demonstrate that the surgeons can perform their tasks quicker and more efficiently when compared to the use of a camera assistant or foot switches

Designing a Foot Input System for Productive Work at a Standing Desk

In this thesis we present Tap-Kick-Click, a foot interaction system for controlling common desktop applications. This system enables computer workers to take healthy and productive breaks from using a keyboard and mouse and demonstrates foot interaction techniques which could be applied in other contexts. Our work supplements the existing literature on foot based interaction, as no published work has combined foot input with a standing desk or attempted control of conventional desktop applications. We describe two experiments to investigate questions about the human performance characteristics of foot input relevant to our application which were unanswered in the existing literature. These experiments investigated the effect of target size, direction and distance; the difference between dominant and non-dominant foot; the use of tapping and kicking interaction; and the impact of displaying or hiding a foot cursor. Based on our results we present a set of design guidelines including a suggested minimum target size; a recommendation to ignore foot dominance; and a preference ranking for direction and foot action. These design guidelines informed the design of Tap-Kick-Click, which we describe in detail. It uses a sensing technique using a Microsoft Kinect depth camera and a pair of augmented slippers capable of robustly sensing foot position, kicking and tapping. The primary interaction technique is based on combinations of foot action and directional tapping in a low-density target layout, supported by feedback and instructions presented in an always visible sidebar. This technique is supplemented with a system for selecting elements in a GUI, a high-density target layout for selecting items from a menu, and a help screen. We illustrate the usefulness of Tap-Kick-Click by describing how it can be used to control a web browser, a citation manager and a debugger. Finally, we present the results of a study conducted to evaluate whether new users could learn and use the system in a web browser context. The study demonstrated that users are successfully able to learn and use the system, along with providing areas for improvement.4 month

Motion-based Interaction for Head-Mounted Displays

Recent advances in affordable sensing technologies have enabled motion-based interaction (MbI) for head-mounted displays (HMDs). Unlike traditional input devices like the mouse and keyboard, which often offer comparatively limited interaction possibilities (e.g., single-touch interaction), MbI does not have these constraints and is more natural because they reflect more closely people do things in real life. However, several issues exist in MbI for HMDs due to the technical limitations of the sensing and tracking devices, higher degrees of freedom afforded to users, and limited research in the area due to the rapid advancement of HMDs and tracking technologies. This thesis first outlines four core challenges in the design space of MbI for HMDs: (1) boundary awareness for hand-based interaction, (2) efficient hands-free head-based interface for HMDs, (3) efficient and feasible full-body interaction for general tasks with HMDs, and (4) accessible full-body interaction for applications in HMDs. Then, this thesis presents an investigation into the contributions of these challenges in MbI for HMDs. The first challenge is addressed by providing visual feedback during interaction tailored for such technologies. The second challenge is addressed by using a circular layout with a go-and-hit selection style for head-based interaction using text entry as the scenario. In addition, this thesis explores additional interaction mechanisms that leverage the affordances of these techniques, and in doing so, we propose directional full-body motions as an interaction approach to perform general tasks with HDMs as an example to address the third challenge. The last challenge is addressed by (1) exploring the differences between performing full-body interaction for HMDs and common displays (i.e., TV) and (2) providing a set of design guidelines that are specific to current and future HMDs. The results of this thesis show that: (1) visual methods for boundary awareness can help with mid-air hand-based interaction in HMDs; (2) head-based interaction and interfaces that take advantages of MbI, such as a circular interface, can be very efficient and low error hands-free input method for HMDs; (3) directional full-body interaction can be a feasible and efficient interaction approach for general tasks involving HMDs; (4) full-body interaction for applications in HMDs should be designed differently than for traditional displays. In addition to these results, this thesis provides a set of design recommendations and takeaway messages for MbI for HMDs