We present MRTouch, a novel multitouch input solution for head-mounted mixed reality systems. Our system enables users to reach out and directly manipulate virtual interfaces affixed to surfaces in their environment, as though they were touchscreens. Touch input offers precise, tactile and comfortable user input, and naturally complements existing popular modalities, such as voice and hand gesture. Our research prototype combines both depth and infrared camera streams together with real-time detection and tracking of surface planes to enable robust finger-tracking even when both the hand and head are in motion. Our technique is implemented on a commercial Microsoft HoloLens without requiring any additional hardware nor any user or environmental calibration. Through our performance evaluation, we demonstrate high input accuracy with an average positional error of 5.4 mm and 95% button size of 16 mm, across 17 participants, 2 surface orientations and 4 surface materials. Finally, we demonstrate the potential of our technique to enable on-world touch interactions through 5 example applications.

Increasingly natural, sensed, and touch-based input is being integrated into devices. Along the way, both custom and more general solutions have been developed for dealing with the uncertainty that is associated with these forms of input. However, it is difficult to provide dynamic, flexible, and continuous feedback about uncertainty using traditional interactive infrastructure. Our contribution is a general architecture with the goal of providing support for continual feedback about uncertainty. Our architecture is based on prior work in modeling uncertainty using Monte Carlo sampling, and tracks multiple interfaces -- one for each plausible and differentiable sequence of input that the user may have intended. Importantly, it considers how the presentation of uncertainty can be organized and implemented in a general way. Our primary contribution is a method for reducing the number of alternative interfaces and fusing possible interfaces into a single interface that both communicates uncertainty and allows for disambiguation. We demonstrate the value of this result through a collection of 11 new and existing feedback techniques along with two applications demonstrating the use of the feedback architecture.

The average person can skillfully manipulate a plethora of tools, from hammers to tweezers. However, despite this remarkable dexterity, gestures on today's touch devices are simplistic, relying primarily on the chording of fingers: one-finger pan, two-finger pinch, four-finger swipe and similar. We propose that touch gesture design be inspired by the manipulation of physical tools from the real world. In this way, we can leverage user familiarity and fluency with such tools to build a rich set of gestures for touch interaction. With only a few minutes of training on a proof-of-concept system, users were able to summon a variety of virtual tools by replicating their corresponding real-world grasps.

Vision-based interfaces, such as those made popular by the Microsoft Kinect, suffer from the Midas Touch problem: every user motion can be interpreted as an interaction. In response, we developed an algorithm that combines facial features, body pose and motion to approximate a user's intention to interact with the system. We show how this can be used to determine when to pay attention to a user's actions and when to ignore them. To demonstrate the value of our approach, we present results from a 30-person lab study conducted to compare four engagement algorithms in single and multi-user scenarios. We found that combining intention to interact with a 'raise an open hand in front of you' gesture yielded the best results. The latter approach offers a 12% improvement in accuracy and a 20% reduction in time to engage over a baseline 'wave to engage' gesture currently used on the Xbox 360.

Tablet computers are often called upon to emulate classical pen-and-paper input. However, touchscreens typically lack the means to distinguish between legitimate stylus and finger touches and touches with the palm or other parts of the hand. This forces users to rest their palms elsewhere or hover above the screen, resulting in ergonomic and usability problems. We present a probabilistic touch filtering approach that uses the temporal evolution of touch contacts to reject palms. Our system improves upon previous approaches, reducing accidental palm inputs to 0.016 per pen stroke, while correctly passing 98% of stylus inputs.

The space around the body provides a large interaction volume that can allow for big interactions on small mobile devices. However, interaction techniques making use of this opportunity are underexplored, primarily focusing on distributing information in the space around the body. We demonstrate three types of around-body interaction including canvas, modal and context-aware interactions in six demonstration applications. We also present a sensing solution using standard smartphone hardware: a phone's front camera, accelerometer and inertia measurement units. Our solution allows a person to interact with a mobile device by holding and positioning it between a normal field of view and its vicinity around the body. By leveraging a user's proprioceptive sense, around-body Interaction opens a new input channel that enhances conventional interaction on a mobile device without requiring additional hardware.

We present Air+Touch, a new class of interactions that interweave touch events with in-air gestures, offering a unified input modality with expressiveness greater than each input modality alone. We demonstrate how air and touch are highly complementary: touch is used to designate targets and segment in-air gestures, while in-air gestures add expressivity to touch events. For example, a user can draw a circle in the air and tap to trigger a context menu, do a finger 'high jump' between two touches to select a region of text, or drag and in-air 'pigtail' to copy text to the clipboard. Through an observational study, we devised a basic taxonomy of Air+Touch interactions, based on whether the in-air component occurs before, between or after touches. To illustrate the potential of our approach, we built four applications that showcase seven exemplar Air+Touch interactions we created.

We present Phone as a Pixel: a scalable, synchronization-free, platform-independent system for creating large, ad-hoc displays from a collection of smaller devices. In contrast to most tiled-display systems, the only requirement for participation is for devices to have an internet connection and a web browser. Thus, most smartphones, tablets, laptops and similar devices can be used. Phone as a Pixel uses a color-transition encoding scheme to identify and locate displays. This approach has several advantages: devices can be arbitrarily arranged (i.e., not in a grid) and infrastructure consists of a single conventional camera. Further, additional devices can join at any time without re-calibration. These are desirable properties to enable collective displays in contexts like sporting events, concerts and political rallies. In this paper we describe our system, show results from proof-of-concept setups, and quantify the performance of our approach on hundreds of displays.

Twitter is now used to distribute substantive content such as breaking news, increasing the importance of assessing the credibility of tweets. As users increasingly access tweets through search, they have less information on which to base credibility judgments as compared to consuming content from direct social network connections. We present survey results regarding users' perceptions of tweet credibility. We find a disparity between features users consider relevant to credibility assessment and those currently revealed by search engines. We then conducted two experiments in which we systematically manipulated several features of tweets to assess their impact on credibility ratings. We show that users are poor judges of truthfulness based on content alone, and instead are influenced by heuristics such as user name when making credibility assessments. Based on these findings, we discuss strategies tweet authors can use to enhance their credibility with readers (and strategies astute readers should be aware of!). We propose design improvements for displaying social search results so as to better convey credibility.

The presence (and, sometimes, prominence) of incorrect and misleading content on the Web can have serious consequences for people who increasingly rely on the internet as their information source for topics such as health, politics, and financial advice. In this paper, we identify and collect several page features (such as popularity among specialized user groups) that are currently difficult or impossible for end users to assess, yet provide valuable signals regarding credibility. We then present visualizations designed to augment search results and Web pages with the most promising of these features. Our lab evaluation finds that our augmented search results are particularly effective at increasing the accuracy of users'" credibility assessments, highlighting the potential of data aggregation and simple interventions to help people make more informed decisions as they search for information online.

We present TapSense, an enhancement to touch interaction that allows conventional surfaces to identify the type of object being used for input. This is achieved by segmenting and classifying sounds resulting from an object's impact. For example, the diverse anatomy of a human finger allows different parts to be recognized including the tip, pad, nail and knuckle - without having to instrument the user. This opens several new and powerful interaction opportunities for touch input, especially in mobile devices, where input is extremely constrained. Our system can also identify different sets of passive tools. We conclude with a comprehensive investigation of classification accuracy and training implications. Results show our proof-of-concept system can support sets with four input types at around 95% accuracy. Small, but useful input sets of two (e.g., pen and finger discrimination) can operate in excess of 99% accuracy.

Current input handling systems provide effective techniques for modeling, tracking, interpreting, and acting on user input. However, new interaction technologies violate the standard assumption that input is certain. Touch, speech recognition, gestural input, and sensors for context often produce uncertain estimates of user inputs. Current systems tend to remove uncertainty early on. However, information available in the user interface and application can help to resolve uncertainty more appropriately for the end user. This paper presents a set of techniques for tracking the state of interactive objects in the presence of uncertain inputs. These techniques use a Monte Carlo approach to maintain a probabilistically accurate description of the user interface that can be used to make informed choices about actions. Samples are used to approximate the distribution of possible inputs, possible interactor states that result from inputs, and possible actions (callbacks and feedback) interactors may execute. Because each sample is certain, the developer can specify most of the behavior of interactors in a familiar, non-probabilistic fashion. This approach retains all the advantages of maintaining information about uncertainty while minimizing the need for the developer to work in probabilistic terms. We present a working implementation of our framework and illustrate the power of these techniques within a paint program that includes three different kinds of uncertain input.

A cord, although simple in form, has many interesting physical affordances that make it powerful as an input device. Not only can a length of cord be grasped in different locations, but also pulled, twisted and bent---four distinct and expressive dimensions that could potentially act in concert. Such an input mechanism could be readily integrated into headphones, backpacks, and clothing. Once grasped in the hand, a cord can be used in an eyes-free manner to control mobile devices, which often feature small screens and cramped buttons. In this note, we describe a proof-of-concept cord-based sensor, which senses three of the four input dimensions we propose. In addition to a discussion of potential uses, we also present results from our preliminary user study. The latter sought to compare the targeting performance and selection accuracy of different cord-based input modalities. We conclude with brief set of design recommendations drawn upon results from our study.

New input technologies (such as touch), recognition based input (such as pen gestures) and next-generation interactions (such as inexact interaction) all hold the promise of more natural user interfaces. However, these techniques all create inputs with some uncertainty. Unfortunately, conventional infrastructure lacks a method for easily handling uncertainty, and as a result input produced by these technologies is often converted to conventional events as quickly as possible, leading to a stunted interactive experience. We present a framework for handling input with uncertainty in a systematic, extensible, and easy to manipulate fashion. To illustrate this framework, we present several traditional interactors which have been extended to provide feedback about uncertain inputs and to allow for the possibility that in the end that input will be judged wrong (or end up going to a different interactor). Our six demonstrations include tiny buttons that are manipulable using touch input, a text box that can handle multiple interpretations of spoken input, a scrollbar that can respond to inexactly placed input, and buttons which are easier to click for people with motor impairments. Our framework supports all of these interactions by carrying uncertainty forward all the way through selection of possible target interactors, interpretation by interactors, generation of (uncertain) candidate actions to take, and a mediation process that decides (in a lazy fashion) which actions should become final.

In this paper we show that financial information can be used to sense many aspects of human activity. This simple technique gives people information about their daily lives, is easily accessible to many at no extra cost, requires little setup, and does not require the manufacture of any external devices. We will focus on how financial data can be used to show users where they spend their time, when they accomplish certain habits, and what the impact of their activities is on the environment. We validate our idea by implementing three demonstration applications intended for personal use. Finally, this paper discusses limitations of sensing using financial data and possible solutions.