Mobile phones have evolved significantly in recent years from single-purpose communication devices to multipurpose computing devices. Despite this evolution, the interaction model for how incoming calls are handled has barely changed. Current-generation smartphones still use abrupt full-screen notifications to alert users to incoming calls, demanding a decision to either accept or decline the call. These full-screen notifications forcibly interrupt whatever activity the user was already engaged in. This might be undesirable when the user's primary task was more important than the incoming call. This paper explores the design space for how smartphones can alert users to incoming calls. We consider designs that allow users to postpone calls and also to multiplex by way of a smaller partialscreen notification. These design alternatives were evaluated in both a small-scale controlled lab study as well as a large-scale naturalistic in-the-wild study. Results show that a multiplex design solution works best because it allows people to continue working on their primary task while being made aware that there is a caller on the line. The contribution of this work is an enhanced interaction design for handling phone calls, and an understanding of how people use it for handling incoming calls.

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We introduce hEYEbrid, a calibration-free method for spontaneous and long-term eye gaze tracking, with competitive gaze estimation. It is based on a hybrid concept that combines infrared eye images with corneal imaging. For this, two eye cameras are mounted on a glasses frame. In this way, the pupil can be tracked quickly with high precision. This information is translated into the corneal image, which is used to create a connection to the environment, acting like a scene camera. In a user study with 20 participants, we evaluated our approach against an extended version of the system, called 3C-hEYEbrid, and a state-of-the-art head-mounted Pupil Labs eye tracker. We show that hEYEbrid provides accurate gaze estimation in unconstrained environments and is robust against calibration drift (e.g. caused by taking off and putting on the device). In addition, we present a mobile and wearable implementation of hEYEbrid and 3C-hEYEbrid, that is also usable with a monocular Pupil Labs eye tracker. It connects the head-mounted device to a mobile phone, enabling gaze estimation in real time. hEYEbrid represents a significant step towards pervasive gaze-based interfaces.