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We introduce Orbits, a novel gaze interaction technique that enables hands-free input on smart watches. The technique relies on moving controls to leverage the smooth pursuit movements of the eyes and detect whether and at which control the user is looking at. In Orbits, controls include targets that move in a circular trajectory in the face of the watch, and can be selected by following the desired one for a small amount of time. We conducted two user studies to assess the technique's recognition and robustness, which demonstrated how Orbits is robust against false positives triggered by natural eye movements and how it presents a hands-free, high accuracy way of interacting with smart watches using off-the-shelf devices. Finally, we developed three example interfaces built with Orbits: a music player, a notifications face plate and a missed call menu. Despite relying on moving controls-very unusual in current HCI interfaces-these were generally well received by participants in a third and final study.

Research on activity recognition has traditionally focused on discriminating between different activities, i.e. to predict "which" activity was performed at a specific point in time. The quality of executing an activity, the "how (well)", has only received little attention so far, even though it potentially provides useful information for a large variety of applications. In this work we define quality of execution and investigate three aspects that pertain to qualitative activity recognition: specifying correct execution, detecting execution mistakes, providing feedback on the to the user. We illustrate our approach on the example problem of qualitatively assessing and providing feedback on weight lifting exercises. In two user studies we try out a sensor-and a model-based approach to qualitative activity recognition. Our results underline the potential of model-based assessment and the positive impact of real-time user feedback on the quality of execution.

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Abstract. During the last 5 years, research on Human Activity Recognition (HAR) has reported on systems showing good overall recognition performance. As a consequence, HAR has been considered as a potential technology for ehealth systems. Here, we propose a machine learning based HAR classifier. We also provide a full experimental description that contains the HAR wearable devices setup and a public domain dataset comprising 165,633 samples. We consider 5 activity classes, gathered from 4 subjects wearing accelerometers mounted on their waist, left thigh, right arm, and right ankle. As basic input features to our classifier we use 12 attributes derived from a time window of 150ms. Finally, the classifier uses a committee AdaBoost that combines ten Decision Trees. The observed classifier accuracy is 99.4%.

Current digital systems are largely blind to users' cognitive states. Systems that adapt to users' states show great potential for augmenting cognition and for creating novel user experiences. However, most approaches for sensing cognitive states, and cognitive load specifically, involve obtrusive technologies, such as physiological sensors attached to users' bodies. This paper present an unobtrusive indicator of the users' cognitive load based on thermal imaging that is applicable in real-world. We use a commercial thermal camera to monitor a person's forehead and nose temperature changes to estimate their cognitive load. To assess the effect of different levels of cognitive load on facial temperature we conducted a user study with 12 participants. The study showed that different levels of the Stroop test and the complexity of reading texts affect facial temperature patterns, thereby giving a measure of cognitive load. To validate the feasibility for real-time assessments of cognitive load, we conducted a second study with 24 participants, we analyzed the temporal latency of temperature changes. Our system detected temperature changes with an average latency of 0.7 seconds after users were exposed to a stimulus, outperforming latency in related work that used other thermal imaging techniques. We provide empirical evidence showing how to unobtrusively detect changes in cognitive load in real-time. Our exploration of exposing users to different content types gives rise to thermal-based activity tracking, which facilitates new applications in the field of cognition-aware computing. CCS Concepts: • Human-centered computing → Human computer interaction (HCI); • Computing methodologies → Cognitive science; • Hardware → Displays and imagers;

In this paper, we investigate nine different visual representations of gaze in a competitive digital game setting. We evaluate the ability of spectators to infer a player's intentions in the game for each visual representation. Our results show that spectators have a remarkable ability to infer intent accurately using all nine visualizations, but that visualizations with certain characteristics were more comprehensible and more readily revealed the player's intent. The real-time Heatmap visualization was the most highly preferred by participants and the most effective in revealing intent, due to its ability to balance real-time gaze information with a persistent summary of recent gaze behaviour. Our findings show that eye-tracking visualization can enable playful interactions in competitive games based on players' ability to interpret opponents' attention and intention through gaze information.

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