Smartphones and tablets are often used in dynamic environments that force users to break focus and attend to their surroundings, creating a form of "situational impairment." Current mobile devices have no ability to sense when users divert or restore their attention, let alone provide support for resuming tasks. We therefore introduce SwitchBack, a system that allows mobile device users to resume tasks more efficiently. SwitchBack is built upon Focus and Saccade Tracking (FAST), which uses the front-facing camera to determine when the user is looking and how their eyes are moving across the screen. In a controlled study, we found that FAST can identify how many lines the user has read in a body of text within a mean absolute percent error of just 3.9%. We then tested SwitchBack in a dual focus-of-attention task, finding that SwitchBack improved average reading speed by 7.7% in the presence of distractions.

We present SurfaceLink, a system where users can make natural surface gestures to control association and information transfer among a set of devices that are placed on a mutually shared surface (e.g., a table). SurfaceLink uses a combination of on-device accelerometers, vibration motors, speakers and microphones (and, optionally, an off-device contact microphone for greater sensitivity) to sense gestures performed on the shared surface. In a controlled evaluation with 10 participants, SurfaceLink detected the presence of devices on the same surface with 97.7% accuracy, their relative arrangement with 89.4% accuracy, and various single- and multi-touch surface gestures with an average accuracy of 90.3%. A usability analysis showed that SurfaceLink has advantages over current multi-device interaction techniques in a number of situations.

In this paper, we present a significant improvement over past work on non-contact end-user deployable sensor for real time whole home power consumption. The technique allows users to place a single device consisting of magnetic pickups on the outside of a power or breaker panel to infer whole home power consumption without the need for professional installation of current transformers (CTs). The new approach does not require precise placement on the breaker panel, a key requirement in previous approaches. This is enabled through a self-calibration technique using a neural network that dynamically learns the transfer function despite the placement of the sensor and the construction of the breaker panel itself. We also demonstrate the ability to actually infer true power using this technique, unlike past solutions that have only been able to capture apparent power. We have evaluated our technique in six homes and one industrial building, including one seven-day deployment. Our results show we can estimate true power consumption with an average accuracy of 95.0% during naturalistic energy use in the home.

Secure authentication with devices or services that store sensitive and personal information is highly important. However, traditional password and pin-based authentication methods compromise between the level of security and user experience. AirAuth is a biometric authentication technique that uses in-air gesture input to authenticate users. We evaluated our technique on a predefined (simple) gesture set and our classifier achieved an average accuracy of 96.6% in an equal error rate (EER-)based study. We obtained an accuracy of 100% when exclusively using personal (complex) user gestures. In a further user study, we found that AirAuth is highly resilient to video-based shoulder surfing attacks, with a measured false acceptance rate of just 2.2%. Furthermore, a longitudinal study demonstrates AirAuth's repeatability and accuracy over time. AirAuth is relatively simple, robust and requires only a low amount of computational power and is hence deployable on embedded or mobile hardware. Unlike traditional authentication methods, our system's security is positively aligned with user-rated pleasure and excitement levels. In addition, AirAuth attained acceptability ratings in personal, office, and public spaces that are comparable to an existing stroke-based on-screen authentication technique. Based on the results presented in this paper, we believe that AirAuth shows great promise as a novel, secure, ubiquitous, and highly usable authentication method.

Current smartphone inputs are limited to physical buttons, touchscreens, cameras or built-in sensors. These approaches either require a dedicated surface or line-of-sight for interaction. We introduce SideSwipe, a novel system that enables in-air gestures both above and around a mobile device. Our system leverages the actual GSM signal to detect hand gestures around the device. We developed an algorithm to convert the discrete and bursty GSM pulses to a continuous wave that can be used for gesture recognition. Specifically, when a user waves their hand near the phone, the hand movement disturbs the signal propagation between the phone's transmitter and added receiving antennas. Our system captures this variation and uses it for gesture recognition. To evaluate our system, we conduct a study with 10 participants and present robust gesture recognition with an average accuracy of 87.2% across 14 hand gestures.

Mobile and embedded electronics are pervasive in today's environment. As such, it is necessary to have a natural and intuitive way for users to indicate the intent to connect to these devices from a distance. We present DopLink, an ultrasonic-based device selection approach. It utilizes the already embedded audio hardware in smart devices to determine if a particular device is being pointed at by another device (i.e., the user waves their mobile phone at a target in a pointing motion). We evaluate the accuracy of DopLink in a controlled user study, showing that, within 3 meters, it has an average accuracy of 95% for device selection and 97% for finding relative device position. Finally, we show three applications of DopLink: rapid device pairing, home automation, and multi-display synchronization.