Contained Attraction is an interactive kinetic sculpture that explores material agency through mediated human-material interaction. The sculpture uses capacitive sensing to sense human presence, and electromagnetic actuation to computationally choreograph the motion of magnetic fluid.

Visual programming has the potential of providing novice programmers with a low-code experience to build customized processing pipelines.Existing systems typically require users to build pipelines from scratch, implying that novice users are expected to set up and link appropriate nodes from a blank workspace.In this paper, we introduce InstructPipe, an AI assistant for prototyping machine learning (ML) pipelines with text instructions.We contribute two large language model (LLM) modules and a code interpreter as part of our framework.The LLM modules generate pseudocode for a target pipeline, and the interpreter renders the pipeline in the node-graph editor for further human-AI collaboration.Both technical and user evaluation (N=16) shows that InstructPipe empowers users to streamline their ML pipeline workfow, reduce their learning curve, and leverage open-ended commands to spark innovative ideas.

Multimodal large language models (MLLMs), with their expansive world knowledge and reasoning capabilities, present a unique opportunity for end-users to create personalized AI sensors capable of reasoning about complex situations. A user could describe a desired sensing task in natural language (e.g., "alert if my toddler is getting into mischief"), with the MLLM analyzing the camera feed and responding within seconds. In a formative study, we found that users saw substantial value in defining their own sensors, yet struggled to articulate their unique personal requirements and debug the sensors through prompting alone. To address these challenges, we developed Gensors, a system that empowers users to define customized sensors supported by the reasoning capabilities of MLLMs. Gensors 1) assists users in eliciting requirements through both automatically-generated and manually created sensor criteria, 2) facilitates debugging by allowing users to isolate and test individual criteria in parallel, 3) suggests additional criteria based on user-provided images, and 4) proposes test cases to help users "stress test" sensors on potentially unforeseen scenarios. In a user study, participants reported significantly greater sense of control, understanding, and ease of communication when defining sensors using Gensors. Beyond addressing model limitations, Gensors supported users in debugging, eliciting requirements, and expressing unique personal requirements to the sensor through criteria-based reasoning; it also helped uncover users' "blind spots" by exposing overlooked criteria and revealing unanticipated failure modes. Finally, we discuss how unique characteristics of MLLMs--such as hallucinations and inconsistent responses--can impact the sensor-creation process. These findings contribute to the design of future intelligent sensing systems that are intuitive and customizable by everyday users.

Extended reality (XR) has the potential for seamless user interface (UI) transitions across people, objects, and environments. However, the design space, applications, and common practices of 3D UI transitions remain underexplored. To address this gap, we conducted a need-finding study with 11 participants, identifying and distilling a taxonomy based on three types of UI placements — affixed to static, dynamic, or self entities. We further surveyed 113 commercial applications to understand the common practices of 3D UI mobility control, where only 6.2% of these applications allowed users to transition UI between entities. In response, we built interaction prototypes to facilitate UI transitions between entities. We report on results from a qualitative user study (N=14) on 3D UI mobility control using our FingerSwitches technique, which suggests that perceived usefulness is affected by types of entities and environments. We aspire to tackle a vital need in UI mobility within XR.

Remote video conferencing systems (RVCS) are widely adopted in personal and professional communication. However, they often lack the co-presence experience of in-person meetings. This is largely due to the absence of intuitive visual cues and clear spatial relationships among remote participants, which can lead to speech interruptions and loss of attention. This paper presents ChatDirector, a novel RVCS that overcomes these limitations by incorporating space-aware visual presence and speech-aware attention transition assistance. ChatDirector employs a real-time pipeline that converts participants’ RGB video streams into 3D portrait avatars and renders them in a virtual 3D scene. We also contribute a decision tree algorithm that directs the avatar layouts and behaviors based on participants’ speech states. We report on results from a user study (N=16) where we evaluated ChatDirector. The satisfactory algorithm performance and complimentary subject user feedback imply that ChatDirector significantly enhances communication efficacy and user engagement.

Augmented reality (AR) glasses have an immense potential for enhancing conversations by leveraging speech recognition to display real-time transcription or translation, for example, to assist people with hearing impairments or for people conversing in a non-native language. For deployment in real environments, such systems, however, need to be able to separate the speech of interest from noise and other speakers. In this paper, we evaluate the effectiveness of leveraging a room simulator to generate large amounts of simulated training data for such front-end sound separation models, to complement the ideal, but costly, collection of real-world data recorded on the device. Using both recorded and simulated impulse responses (IRs), we demonstrate that the use of simulation data is an effective method for training models that can ultimately enhance speech recognition performance in real-world settings. Furthermore, we show that performance can be further improved by adding microphone directivity in the room simulation, and by fusing synthetic data with a small amount of real IRs. Our results also suggest that existing room simulators would benefit from incorporating the head shadow effect, given its significant impact on multi-microphone recordings on AR glasses.

In recent years, there has been a proliferation of multimedia applications that leverage machine learning (ML) for interactive experiences. Prototyping ML-based applications is, however, still challenging, given complex workflows that are not ideal for design and experimentation. To better understand these challenges, we conducted a formative study with seven ML practitioners to gather insights about common ML evaluation workflows.

Video conferencing solutions like Zoom, Google Meet, and Microsoft Teams are becoming increasingly popular for facilitating conversations, and recent advancements such as live captioning help people better understand each other. We believe that the addition of visuals based on the context of conversations could further improve comprehension of complex or unfamiliar concepts. To explore the potential of such capabilities, we conducted a formative study through remote interviews (N=10) and crowdsourced a dataset of over 1500 sentence-visual pairs across a wide range of contexts. These insights informed Visual Captions, a real-time system that integrates with a video conferencing platform to enrich verbal communication. Visual Captions leverages a fine-tuned large language model to proactively suggest relevant visuals in open-vocabulary conversations. We present findings from a lab study (N=26) and an in-the-wild case study (N=10), demonstrating how Visual Captions can help improve communication through visual augmentation in various scenarios.

Recent advances have enabled automatic sound recognition systems for deaf and hard of hearing (DHH) users on mobile devices. However, these tools use pre-trained, generic sound recognition models, which do not meet the diverse needs of DHH users. We introduce ProtoSound, an interactive system for customizing sound recognition models by recording a few examples, thereby enabling personalized and fine-grained categories. ProtoSound is motivated by prior work examining sound awareness needs of DHH people and by a survey we conducted with 472 DHH participants. To evaluate ProtoSound, we characterized performance on two real-world sound datasets, showing significant improvement over state-of-the-art (e.g., +9.7% accuracy on the first dataset). We then deployed ProtoSound's end-user training and real-time recognition through a mobile application and recruited 19 hearing participants who listened to the real-world sounds and rated the accuracy across 56 locations (e.g., homes, restaurants, parks). Results show that ProtoSound personalized the model on-device in real-time and accurately learned sounds across diverse acoustic contexts. We close by discussing open challenges in personalizable sound recognition, including the need for better recording interfaces and algorithmic improvements.

Speech is inappropriate in many situations, limiting when voice control can be used. Most unvoiced speech text entry systems can not be used while on-the-go due to movement artifacts. Using a dental retainer with capacitive touch sensors, SilentSpeller tracks tongue movement, enabling users to type by spelling words without voicing. SilentSpeller achieves an average 97% character accuracy in offline isolated word testing on a 1164-word dictionary. Walking has little effect on accuracy; average offline character accuracy was roughly equivalent on 107 phrases entered while walking (97.5%) or seated (96.5%). To demonstrate extensibility, the system was tested on 100 unseen words, leading to an average 94% accuracy. Live text entry speeds for seven participants averaged 37 words per minute at 87% accuracy. Comparing silent spelling to current practice suggests that SilentSpeller may be a viable alternative for silent mobile text entry.

Consumer electronics are increasingly using everyday materials to blend into home environments, often using LEDs or symbol displays under textile meshes. Our surveys (n=1499 and n=1501) show interest in interactive graphical displays for hidden interfaces — however, covering such displays significantly limits brightness, material possibilities and legibility.

E-textile microinteractions advance cord-based interfaces by enabling the simultaneous use of precise continuous control and casual discrete gestures. We leverage the recently introduced I/O Braid sensing architecture to enable a series of user studies and experiments which help design suitable interactions and a real-time gesture recognition pipeline. Informed by a gesture elicitation study with 36 participants, we developed a user-dependent classifier for eight discrete gestures with 94% accuracy for 12 participants. In a formal evaluation we show that we can enable precise manipulation with the same architecture. Our quantitative targeting experiment suggests that twisting is faster than existing headphone button controls and is comparable in speed to a capacitive touch surface. Qualitative interview feedback indicates a preference for I/O Braid's interaction over that of in-line headphone controls. Our applications demonstrate how continuous and discrete gestures can be combined to form new, integrated e-textile microinteraction techniques for real-time continuous control, discrete actions and mode switching.

Today's wearable and mobile devices typically use separate hardware components for sensing and actuation. In this work, we introduce new opportunities for the Linear Resonant Actuator (LRA), which is ubiquitous in such devices due to its capability for providing rich haptic feedback. By leveraging strategies to enable active and passive sensing capabilities with LRAs, we demonstrate their benefits and potential as self-contained I/O devices. Specifically, we use the back-EMF voltage to classify if the LRA is tapped, touched, as well as how much pressure is being applied. The back-EMF sensing is already integrated into many motor and LRA drivers. We developed a passive low-power tap sensing method that uses just 37.7 uA. Furthermore, we developed active touch and pressure sensing, which is low-power, quiet (2 dB), and minimizes vibration. The sensing method works with many types of LRAs. We show applications, such as pressure-sensing side-buttons on a mobile phone. We have also implemented our technique directly on an existing mobile phone's LRA to detect if the phone is handheld or placed on a soft or hard surface. Finally, we show that this method can be used for haptic devices to determine if the LRA makes good contact with the skin. Our approach can add rich sensing capabilities to the ubiquitous LRA actuators without requiring additional sensors or hardware.

Mobile solutions can help transform speech and sound into visual representations for people who are deaf or hard-of-hearing (DHH). However, where handheld phones present challenges, head-worn displays (HWDs) could further communication through privately transcribed text, hands-free use, improved mobility, and socially acceptable interactions.

Adding electronics to textiles can be time-consuming and requires technical expertise. We introduce SensorSnaps, low-power wireless sensor nodes that seamlessly integrate into caps of fabric snap fasteners. SensorSnaps provide a new technique to quickly and intuitively augment any location on the clothing with sensing capabilities. SensorSnaps securely attach and detach from ubiquitous commercial snap fasteners. Using inertial measurement units, the SensorSnaps detect tap and rotation gestures, as well as track body motion. We optimized the power consumption for SensorSnaps to work continuously for 45 minutes and up to 4 hours in capacitive touch standby mode. We present applications in which the SensorSnaps are used as gestural interfaces for a music player controller, cursor control, and motion tracking suit. The user study showed that SensorSnap could be attached in around 71 seconds, similar to attaching off-the-shelf snaps, and participants found the gestures easy to learn and perform. SensorSnaps could allow anyone to effortlessly add sophisticated sensing capacities to ubiquitous snap fasteners.

1D Eyewear uses 1D arrays of LEDs and pre-recorded holographic symbols to enable minimal head-worn displays. Our approach uses computer-generated holograms (CGHs) to create diffraction gratings which project a pre-recorded static image when illuminated with coherent light. Specifically, we develop a set of transmissive, reflective, and steerable optical configurations that can be embedded in conventional eyewear designs. This approach enables high resolution symbolic display in discreet digital eyewear.

We introduce I/O Braid, an interactive textile cord with embedded sensing and visual feedback. I/O Braid senses proximity, touch, and twist through a spiraling, repeating braiding topology of touch matrices. This sensing topology is uniquely scalable, requiring only a few sensing lines to cover the whole length of a cord. The same topology allows us to embed fiber optic strands to integrate co-located visual feedback. We provide an overview of the enabling braiding techniques, design considerations, and approaches to gesture detection. These allow us to derive a set of interaction techniques, which we demonstrate with different form factors and capabilities. Our applications illustrate how I/O Braid can invisibly augment everyday objects, such as touch-sensitive headphones and interactive drawstrings on garments, while enabling discoverability and feedback through embedded light sources.

Interactions and connectivity is increasingly expanding to shared objects and environments, such as furniture, vehicles, lighting, and entertainment systems. For transparent personalization in such contexts, we see an opportunity for embedded recognition, to complement traditional, explicit authentication. We introduce Zensei, an implicit sensing system that leverages bio-sensing, signal processing and machine learning to classify uninstrumented users by their body's electrical properties. Zensei could allow many objects to recognize users. E.g., phones that unlock when held, cars that automatically adjust mirrors and seats, or power tools that restore user settings. We introduce wide-spectrum bioimpedance hardware that measures both amplitude and phase. It extends previous approaches through multi-electrode sensing and high-speed wireless data collection for embedded devices. We implement the sensing in devices and furniture, where unique electrode configurations generate characteristic profiles based on user's unique electrical properties. Finally, we discuss results from a comprehensive longitudinal 22-day data collection experiment with 46 subjects. Our analysis shows promising classification accuracy and low false acceptance rate.

Currently, virtual (i.e. touchscreen) controls are dynamic, but lack the advantageous tactile feedback of physical controls. Similarly, devices may also have dedicated physical controls, but they lack the flexibility to adapt for different contexts and applications. On mobile and wearable devices in particular, space constraints further limit our input and output capabilities. We propose utilizing reconfigurable tactile elements around the edge of a mobile device to enable dynamic physical controls and feedback. These tactile elements can be used for physical touch input and output, and can reposition according to the application both around the edge of and hidden within the device. We present shiftIO, two implementations of such a system which actuate physical controls around the edge of a mobile device using magnetic locomotion. One version utilizes PCB-manufactured electromagnetic coils, and the other uses switchable permanent magnets. We perform a technical evaluation of these prototypes and compare their advantages in various applications. Finally, we demonstrate several mobile applications which leverage shiftIO to create novel mobile interactions.

The increased interest in interactive soft materials, such as smart clothing and responsive furniture, means that there is a need for flexible and deformable electronics. In this paper, we focus on stitch-based elastic sensors, which have the benefit of being manufacturable with textile craft tools that have been used in homes for centuries. We contribute to the understanding of stitch-based stretch sensors through four experiments and one user study that investigate conductive yarns from textile and technical perspectives, and analyze the impact of different stitch types and parameters. The insights informed our design of new stretch-based interaction techniques that emphasize eyes-free or causal interactions. We demonstrate with StretchEBand how soft, continuous sensors can be rapidly fabricated with different parameters and capabilities to support interaction with a wide range of performance requirements across wearables, mobile devices, clothing, furniture, and toys.

The body provides many recognizable landmarks due to the underlying skeletal structure and variations in skin texture, elasticity, and color. The visual and spatial cues of such body landmarks can help in localizing on-body interfaces, guide input on the body, and allow for easy recall of mappings. Our main contribution are SkinMarks, novel skin-worn I/O devices for precisely localized input and output on fine body landmarks. SkinMarks comprise skin electronics on temporary rub-on tattoos. They conform to fine wrinkles and are compatible with strongly curved and elastic body locations. We identify five types of body landmarks and demonstrate novel interaction techniques that leverage SkinMarks' unique touch, squeeze and bend sensing with integrated visual output. Finally, we detail on the conformality and evaluate sub-millimeter electrodes for touch sensing. Taken together, SkinMarks expands the on-body interaction space to more detailed, highly curved and challenging areas on the body.

We introduce WatchThru, an interactive method for extended wrist-worn display on commercially-available smartwatches. To address the limited visual and interaction space, WatchThru expands the device into 3D through a transparent display. This enables novel interactions that leverage and extend smartwatch glanceability. We describe three novel interaction techniques, Pop-up Visuals, Second Perspective and Peek-through, and discuss how they can complement interaction on current devices. We also describe two types of prototypes that helped us to explore standalone interactions, as well as, proof-of-concept AR interfaces using our platform.

Ungrounded haptic devices for virtual reality (VR) applications lack the ability to convincingly render the sensations of a grasped virtual object's rigidity and weight. We present Grabity, a wearable haptic device designed to simulate kinesthetic pad opposition grip forces and weight for grasping virtual objects in VR. The device is mounted on the index finger and thumb and enables precision grasps with a wide range of motion. A unidirectional brake creates rigid grasping force feedback. Two voice coil actuators create virtual force tangential to each finger pad through asymmetric skin deformation. These forces can be perceived as gravitational and inertial forces of virtual objects. The rotational orientation of the voice coil actuators is passively aligned with the real direction of gravity through a revolute joint, causing the virtual forces to always point downward. This paper evaluates the performance of Grabity through two user studies, finding promising ability to simulate different levels of weight with convincing object rigidity. The first user study shows that Grabity can convey various magnitudes of weight and force sensations to users by manipulating the amplitude of the asymmetric vibration. The second user study shows that users can differentiate different weights in a virtual environment using Grabity.

Over the last decades, there have been numerous efforts in wearable computing research to enable interactive textiles. Most work focus, however, on integrating sensors for planar touch gestures, and thus do not fully take advantage of the flexible, deformable and tangible material properties of textile. In this work, we introduce SmartSleeve, a deformable textile sensor, which can sense both surface and deformation gestures in real-time. It expands the gesture vocabulary with a range of expressive interaction techniques, and we explore new opportunities using advanced deformation gestures, such as, Twirl, Twist, Fold, Push and Stretch. We describe our sensor design, hardware implementation and its novel non-rigid connector architecture. We provide a detailed description of our hybrid gesture detection pipeline that uses learning-based algorithms and heuristics to enable real-time gesture detection and tracking. Its modular architecture allows us to derive new gestures through the combination with continuous properties like pressure, location, and direction. Finally, we report on the promising results from our evaluations which demonstrate real-time classification.

Today's commercially available prosthetic limbs lack tactile sensation and feedback. Recent research in this domain focuses on sensor technologies designed to be directly embedded into future prostheses. We present a novel concept and prototype of a prosthetic-sensing wearable that offers a non-invasive, self-applicable and customizable approach for the sensory augmentation of present-day and future low to mid-range priced lower-limb prosthetics. From consultation with eight lower-limb amputees, we investigated the design space for prosthetic sensing wearables and developed novel interaction methods for dynamic, user-driven creation and mapping of sensing regions on the foot to wearable haptic feedback actuators. Based on a pilot-study with amputees, we assessed the utility of our design in scenarios brought up by the amputees and we summarize our findings to establish future directions for research into using smart textiles for the sensory enhancement of prosthetic limbs.

Surface and object recognition is of significant importance in ubiquitous and wearable computing. While various techniques exist to infer context from material properties and appearance, they are typically neither designed for real-time applications nor for optically complex surfaces that may be specular, textureless, and even transparent. These materials are, however, becoming increasingly relevant in HCI for transparent displays, interactive surfaces, and ubiquitous computing. We present SpecTrans, a new sensing technology for surface classification of exotic materials, such as glass, transparent plastic, and metal. The proposed technique extracts optical features by employing laser and multi-directional, multi-spectral LED illumination that leverages the material's optical properties. The sensor hardware is small in size, and the proposed classification method requires significantly lower computational cost than conventional image-based methods, which use texture features or reflectance analysis, thereby providing real-time performance for ubiquitous computing. Our evaluation of the sensing technique for nine different transparent materials, including air, shows a promising recognition rate of 99.0%. We demonstrate a variety of possible applications using SpecTrans' capabilities.

We propose a new approach to Physical Telepresence, based on shared workspaces with the ability to capture and remotely render the shapes of people and objects. In this paper, we describe the concept of shape transmission, and propose interaction techniques to manipulate remote physical objects and physical renderings of shared digital content. We investigate how the representation of user's body parts can be altered to amplify their capabilities for teleoperation. We also describe the details of building and testing prototype Physical Telepresence workspaces based on shape displays. A preliminary evaluation shows how users are able to manipulate remote objects, and we report on our observations of several different manipulation techniques that highlight the expressive nature of our system.

We present the theoretical framework and design of a technologically-mediated informal learning experience aimed at assisting students of all ages playfully engage in a language game for fun. The game's activities center around rhyming over a beat with a robotic companion. We ground the work in developmental psychology literature on the importance of early rhyming skills for children and provide technical details of a working prototype. Our work advances an informal learning intervention for children at-risk of developing reading disabilities. We propose a study using validated, evaluation measures and conclude with a detailed report on extensions to the system currently in progress.

SpaceTop is a concept that fuses spatial 2D and 3D interactions in a single workspace. It extends the traditional desktop interface with interaction technology and visualization techniques that enable seamless transitions between 2D and 3D manipulations. SpaceTop allows users to type, click, draw in 2D, and directly manipulate interface elements that float in the 3D space above the keyboard. It makes it possible to easily switch from one modality to another, or to simultaneously use two modalities with different hands. We introduce hardware and software configurations for co-locating these various interaction modalities in a unified workspace using depth cameras and a transparent display. We describe new interaction and visualization techniques that allow users to interact with 2D elements floating in 3D space. We present the results from a preliminary user study that indicates the benefit of such hybrid workspaces.

Recent research in 3D user interfaces pushes towards immersive graphics and actuated shape displays. Our work explores the hybrid of these directions, and we introduce sublimation and deposition, as metaphors for the transitions between physical and virtual states. We discuss how digital models, handles and controls can be interacted with as virtual 3D graphics or dynamic physical shapes, and how user interfaces can rapidly and fluidly switch between those representations. To explore this space, we developed two systems that integrate actuated shape displays and augmented reality (AR) for co-located physical shapes and 3D graphics. Our spatial optical see-through display provides a single user with head-tracked stereoscopic augmentation, whereas our handheld devices enable multi-user interaction through video seethrough AR. We describe interaction techniques and applications that explore 3D interaction for these new modalities. We conclude by discussing the results from a user study that show how freehand interaction with physical shape displays and co-located graphics can outperform wand-based interaction with virtual 3D graphics.

Past research on shape displays has primarily focused on rendering content and user interface elements through shape output, with less emphasis on dynamically changing UIs. We propose utilizing shape displays in three different ways to mediate interaction: to facilitate by providing dynamic physical affordances through shape change, to restrict by guiding users with dynamic physical constraints, and to manipulate by actuating physical objects. We outline potential interaction techniques and introduce Dynamic Physical Affordances and Constraints with our inFORM system, built on top of a state-of-the-art shape display, which provides for variable stiffness rendering and real-time user input through direct touch and tangible interaction. A set of motivating examples demonstrates how dynamic affordances, constraints and object actuation can create novel interaction possibilities.

The ability to guide human motion through automatically generated feedback has significant potential for applications in areas, such as motor learning, human-computer interaction, telepresence, and augmented reality.

Malleable and organic user interfaces have the potential to enable radically new forms of interactions and expressiveness through flexible, free-form and computationally controlled shapes and displays. This work, specifically focuses on particle jamming as a simple, effective method for flexible, shape-changing user interfaces where programmatic control of material stiffness enables haptic feedback, deformation, tunable affordances and control gain. We introduce a compact, low-power pneumatic jamming system suitable for mobile devices, and a new hydraulic-based technique with fast, silent actuation and optical shape sensing. We enable jamming structures to sense input and function as interaction devices through two contributed methods for high-resolution shape sensing using: 1) index-matched particles and fluids, and 2) capacitive and electric field sensing. We explore the design space of malleable and organic user interfaces enabled by jamming through four motivational prototypes that highlight jamming's potential in HCI, including applications for tabletops, tablets and for portable shape-changing mobile devices.

Image-guided surgery (IGS) often depends on X-ray imaging, since pre-operative MRI, CT and PET scans do not provide an up-to-date internal patient view during the operation. X-rays introduce hazardous radiation, but long exposures for monitoring are often necessary to increase accuracy in critical situations. Surgeons often also take multiple X-rays from different angles, as X-rays only provide a distorted 2D perspective from the current viewpoint. We introduce a prototype IGS system that augments 2D X-ray images with spatiotemporal information using a motion tracking system, such that the use of X-rays can be reduced. In addition, an interactive visualization allows exploring 2D X-rays in timeline views and 3D clouds where they are arranged according to the viewpoint at the time of acquisition. The system could be deployed and used without time-consuming calibration, and has the potential to improve surgeons' spatial awareness, while increasing efficiency and patient safety.

Mobile devices are currently difficult to customize for the usability needs of elderly users. The elderly are instead referred to specially designed "senior phones" or software add-ons. These tend to compromise in functionality as they attempt to solve many disabilities in a single solution.

Mobile devices are now able to connect to a variety of sensors and provide personalized information to help people reflect on and improve their health. For example, pedometers, heart-rate sensors, glucometers, and other sensors can all provide real-time data to a variety of devices. Collecting and interacting with personal health or well-being data is a growing research area. This workshop will focus on the ways in which our mobile devices can aggregate and visualize these types of data and how these data streams can be presented to encourage interaction, increased awareness and positive behavior change.

Motion sensing is of fundamental importance for user interfaces and input devices. In applications, where optical sensing is preferred, traditional camera-based approaches can be prohibitive due to limited resolution, low frame rates and the required computational power for image processing. We introduce a novel set of motion-sensing configurations based on laser speckle sensing that are particularly suitable for human-computer interaction. The underlying principles allow these configurations to be fast, precise, extremely compact and low cost. We provide an overview and design guidelines for laser speckle sensing for user interaction and introduce four general speckle projector/sensor configurations. We describe a set of prototypes and applications that demonstrate the versatility of our laser speckle sensing techniques.

While touch-screen displays are becoming increasingly popular, many factors affect user experience and performance. Surface quality, parallax, input resolution, and robustness, for instance, can vary with sensing technology, hardware configurations, and environmental conditions.

Displays are the most visible part of most computer applications. Novel display technologies strongly influence and inspire new forms of computer use and interaction. We are particularly interested in the interplay of novel displays and interaction for ubiquitous computing or ambient media environments, as emerging display technologies may become game-changers in how we define and use computers, possibly changing the context of computing fundamentally. We present some of our experiments and lessons learnt with a new category of displays, the “immaterial” FogScreen. It can be described as a novel media platform, exhibiting some fundamental differences to and advantages over other displays. It also enables novel kinds of user interfaces and experiences. In this paper we give insights about the special properties and strengths of the FogScreen by looking at a set of successfully demonstrated interfaces and applications. We also discuss its future potential for user interface design.

We introduce two families of techniques, rubbing and tapping, that use zooming to make possible precise interaction on passive touch screens, and describe examples of each. Rub-Pointing uses a diagonal rubbing gesture to integrate pointing and zooming in a single-handed technique. In contrast, Zoom-Tapping is a two-handed technique in which the dominant hand points, while the non-dominant hand taps to zoom, simulating multi-touch functionality on a single-touch display. Rub-Tapping is a hybrid technique that integrates rubbing with the dominant hand to point and zoom, and tapping with the non-dominant hand to confirm selection. We describe the results of a formal user study comparing these techniques with each other and with the well-known Take-Off and Zoom-Pointing selection techniques. Rub-Pointing and Zoom-Tapping had significantly fewer errors than Take-Off for small targets, and were significantly faster than Take-Off and Zoom-Pointing. We show how the techniques can be used for fluid interaction in an image viewer and in Google Maps.

The vision of spatially aware handheld interaction devices has been hard to realize. The difficulties in solving the general tracking problem for small devices have been addressed by several research groups and examples of issues are performance, hardware availability and platform independency. We present LightSense, an approach that employs commercially available components to achieve robust tracking of cell phone LEDs, without any modifications to the device. Cell phones can thus be promoted to interaction and display devices in ubiquitous installations of systems such as the ones we present here. This could enable a new generation of spatially aware handheld interaction devices that would unobtrusively empower and assist us in our everyday tasks.

We present an interactive wall-sized immaterial display that introduces a number of interesting possibilities for advanced interface design. The immaterial nature of a thin sheet of fog allows users to penetrate and even walk through the screen, while its dual-sided nature allows for new possibilities in multi-user faceto- face collaboration and pseudo-3D visualization.

We present a novel autostereoscopic optical see-through system for augmented reality (AR). It uses a transparent holographic optical element (HOE) to separate the views produced by two, or more, digital projectors. It is a minimally intrusive AR system that does not require the user to wear special glasses or any other equipment, since the user would see different images depending on the point of view. The HOE itself is a thin glass plate or plastic film that can easily be incorporated into other surfaces, such as a window. The technology offers great flexibility, allowing the projectors to be placed where they are the least intrusive. ASTOR's capability of sporadic AR visualization is currently ideal for smaller physical workspaces, such as our prototype setup in an industrial environment.

Information in hybrid user interfaces can be spread over a variety of different, but complementary, displays, with which users interact through a potentially equally varied range of interaction devices. Since the exact configuration of these displays and devices may not be known in advance, it is desirable for users to be able to reconfigure at runtime the dataflow between interaction devices and objects on the displays. To make this possible, we present the design and implementation of a prototype mixed reality system that allows users to immersively reconfigure a running hybrid user interface.

We describe several approaches for using prosodic features of speech and audio localization to control interactive applications. This information can be applied to parameter control, as well as to speech disambiguation. We discuss how characteristics of spoken sentences can be exploited in the user interface; for example, by considering the speed with which a sentence is spoken and the presence of extraneous utterances. We also show how coarse audio localization can be used for low-fidelity gesture tracking, by inferring the speaker's head position.

We describe POLAR, a portable, optical see-through, low-cost augmented reality system, which allows a user to see annotated views of small to medium-sized physical objects in an unencumbered way. No display or tracking equipment needs to be worn. We describe the system design, including a hybrid IR/vision head-tracking solution, and present examples of simple augmented scenes. POLAR's compactness could allow it to be used as a lightweight and portable PC peripheral for providing mobile users with on-demand AR access in field work.

We describe an approach to 3D multimodal interaction in immersive augmented and virtual reality environments that accounts for the uncertain nature of the information sources. The resulting multimodal system fuses symbolic and statistical information from a set of 3D gesture, spoken language, and referential agents. The referential agents employ visible or invisible volumes that can be attached to 3D trackers in the environment, and which use a time-stamped history of the objects that intersect them to derive statistics for ranking potential referents. We discuss the means by which the system supports mutual disambiguation of these modalities and information sources, and show through a user study how mutual disambiguation accounts for over 45% of the successful 3D multimodal interpretations. An accompanying video demonstrates the system in action.

We introduce a set of statistical geometric tools designed to identify the objects being manipulated through speech and gesture in a multimodal augmented reality system. SenseShapes are volumetric regions of interest that can be attached to parts of the user's body to provide valuable information about the user's interaction with objects. To assist in object selection, we generate a rich set of statistical data and dynamically choose which data to consider based on the current situation.