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Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.7k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 72k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query- by-example search over UIs.

We present Brain Automated Chorales (BACh), an adaptive brain-computer system that dynamically increases the levels of difficulty in a musical learning task based on pianists' cognitive workload measured by functional near-infrared spectroscopy. As users' cognitive workload fell below a certain threshold, suggesting that they had mastered the material and could handle more cognitive information, BACh automatically increased the difficulty of the learning task. We found that learners played with significantly increased accuracy and speed in the brain-based adaptive task compared to our control condition. Participant feedback indicated that they felt they learned better with BACh and they liked the timings of the level changes. The underlying premise of BACh can be applied to learning situations where a task can be broken down into increasing levels of difficulty.

Emerging ultra-small wearables like smartwatches pose a design challenge for touch-based text entry. This is due to the "fat-finger problem," wherein users struggle to select elements much smaller than their fingers. To address this challenge, we developed DriftBoard, a panning-based text entry technique where the user types by positioning a movable qwerty keyboard on an interactive area with respect to a fixed cursor point. In this paper, we describe the design and implementation of DriftBoard and report results of a user study on a watch-size touchscreen. The study compared DriftBoard to two ultra-small keyboards, ZoomBoard (tapping-based) and Swipeboard (swiping-based). DriftBoard performed comparably (no significant difference) to ZoomBoard in the major metrics of text entry speed and error rate, and outperformed Swipeboard, which suggests that panning-based typing is a promising input method for text entry on ultra-small touchscreens.

Decades of research have repeatedly shown that people perform poorly at estimating and understanding conditional probabilities that are inherent in Bayesian reasoning problems. Yet in the medical domain, both physicians and patients make daily, life-critical judgments based on conditional probability. Although there have been a number of attempts to develop more effective ways to facilitate Bayesian reasoning, reports of these findings tend to be inconsistent and sometimes even contradictory. For instance, the reported accuracies for individuals being able to correctly estimate conditional probability range from 6% to 62%. In this work, we show that problem representation can significantly affect accuracies. By controlling the amount of information presented to the user, we demonstrate how text and visualization designs can increase overall accuracies to as high as 77%. Additionally, we found that for users with high spatial ability, our designs can further improve their accuracies to as high as 100%. By and large, our findings provide explanations for the inconsistent reports on accuracy in Bayesian reasoning tasks and show a significant improvement over existing methods. We believe that these findings can have immediate impact on risk communication in health-related fields.

A growing body of recent work has shown the feasibility of brain and body sensors as input to interactive systems. However, the interaction techniques and design decisions for their effective use are not well defined. We present a conceptual framework for considering implicit input from the brain, along with design principles and patterns we have developed from our work. We also describe a series of controlled, offline studies that lay the foundation for our work with functional near-infrared spectroscopy (fNIRS) neuroimaging, as well as our real-time platform that serves as a testbed for exploring brain-based adaptive interaction techniques. Finally, we present case studies illustrating the principles and patterns for effective use of brain data in human--computer interaction. We focus on signals coming from the brain, but these principles apply broadly to other sensor data and in domains such as aviation, education, medicine, driving, and anything involving multitasking or varying cognitive workload.

Dynamic difficulty adjustments can be used in human-computer systems in order to improve user engagement and performance. In this paper, we use functional near-infrared spectroscopy (fNIRS) to obtain passive brain sensing data and detect extended periods of boredom or overload. From these physiological signals, we can adapt a simulation in order to optimize workload in real-time, which allows the system to better fit the task to the user from moment to moment. To demonstrate this idea, we ran a laboratory study in which participants performed path planning for multiple unmanned aerial vehicles (UAVs) in a simulation. Based on their state, we varied the difficulty of the task by adding or removing UAVs and found that we were able to decrease error by 35% over a baseline condition. Our results show that we can use fNIRS brain sensing to detect task difficulty in real-time and construct an interface that improves user performance through dynamic difficulty adjustment.

The bubble cursor is a promising cursor expansion technique, improving a user's movement time and accuracy in pointing tasks. We introduce a brain-based target expansion system, which improves the efficacy of bubble cursor by increasing the expansion of high importance targets at the optimal time based on brain measurements correlated to a particular type of multitasking. We demonstrate through controlled experiments that brain-based target expansion can deliver a graded and continuous level of assistance to a user according to their cognitive state, thereby improving task and speed-accuracy metrics, even without explicit visual changes to the system. Such an adaptation is ideal for use in complex systems to steer users toward higher priority goals during times of increased demand.