Unobtrusiveness has been highlighted as an important design principle in Human-Computer Interaction (HCI).However, the understanding of unobtrusiveness in the literature varies.Researchers often claim unobtrusiveness for their interaction method based on their understanding of what unobtrusiveness means.This lack of a shared definition hinders effective communication in research and impedes comparability between approaches.In this article, we approach the question "What is unobtrusive interaction?" with a systematic and extensive literature review of 335 papers and an online survey with experts.We found that not a single definition of unobtrusiveness is universally agreed upon.Instead, we identify five working definitions from the literature and experts' responses.We summarize the properties of unobtrusive interaction into a design framework with five dimensions and classify the reviewed papers with regard to these dimensions.The article aims to provide researchers with a more unified context to compare their work and identify opportunities for future research.

One challenge of providing guidance for search tasks consists in guiding the user’s visual attention to certain objects in a potentially large search space. Previous work has tried to guide the user’s attention by providing visual, audio, or haptic cues. The state-of-the-art methods either provide hints pointing towards the approximate direction of the target location for a fast but less accurate search or require the user to perform a fine-grained search from the beginning for a precise yet less efficient search. To combine the advantage of both methods, we propose an interaction concept called Two-Step Gaze Guidance. The first-step guidance focuses on quick guidance toward the approximate direction, and the second-step guidance focuses on fine-grained guidance toward the exact location of the target. A between-subject study (N = 69) with five conditions was carried out to compare the two-step gaze guidance method with the single-step gaze guidance method. Results revealed that the proposed method outperformed the single-step gaze guidance method. More precisely, the introduction of Two-Step Gaze Guidance slightly improves the searching accuracy, and the use of spatial audio as the first-step guidance significantly helps in enhancing the searching efficiency. Our results also indicated several design suggestions for designing gaze guidance methods.

Exploring a city panorama from a vantage point is a popular tourist activity. Typical audio guides that support this activity are limited by their lack of responsiveness to user behavior and by the difficulty of matching audio descriptions to the panorama. These limitations can inhibit the acquisition of information and negatively affect user experience. This paper proposes Gaze-Guided Narratives as a novel interaction concept that helps tourists find specific features in the panorama (gaze guidance) while adapting the audio content to what has been previously looked at (content adaptation). Results from a controlled study in a virtual environment (n=60) revealed that a system featuring both gaze guidance and content adaptation obtained better user experience, lower cognitive load, and led to better performance in a mapping task compared to a classic audio guide. A second study with tourists situated at a vantage point (n=16) further demonstrated the feasibility of this approach in the real world.

User experience could be enhanced if the computer could understand human interaction intention. For instance, it could react to intercept and prevent interaction errors. This paper presents an approach to predicting users intention in interaction tasks based on past mouse movements. We adopt a long short-term memory (LSTM) model to predict the users» intention via their next mouse click interaction, upon being trained with past mouse interaction behaviors. To evaluate, we consider two scenarios in daily computer usage: a more structured crowdsourcing annotation task and a more free-form, open-ended web search task. Our results indicate that we could predict the next interaction event with reasonable accuracy. We also conducted a pilot study to investigate the possibility of applying our model for non-intentional mouse click detection. We believe that our findings would be beneficial towards the development of better intelligent agents.

This paper presents an investigation into user intention prediction in two common web-based tasks: crowdsourcing annotation and web search, based on human-mouse interaction information. User experience is gaining importance within the research area of human-centered computing, and is particularly useful for complex, multi-step tasks. To enhance user experience, the computer should be intelligent enough to be able to predict the user intention. For instance, an intelligent agent might be able to anticipate when the user is about to press a button, and helpfully enlarge or highlight it in advance. In this paper, we propose two prediction models on user intention: a classical model that considers only historical mouse activity sequence, and a multimodal model that utilizes mouse interaction signals as well as features extracted from mouse trajectory and clicking events. We evaluate our models and find that they achieve reasonable accuracy. Our preliminary results indicate that we can dynamically learn a multimodal model that can effectively predict a user's next activity from historical activity sequence and mouse interaction signals.

We present PACE, a Personalized, Automatically Calibrating Eye-tracking system that identifies and collects data unobtrusively from user interaction events on standard computing systems without the need for specialized equipment. PACE relies on eye/facial analysis of webcam data based on a set of robust geometric gaze features and a two-layer data validation mechanism to identify good training samples from daily interaction data. The design of the system is founded on an in-depth investigation of the relationship between gaze patterns and interaction cues, and takes into consideration user preferences and habits. The result is an adaptive, data-driven approach that continuously recalibrates, adapts and improves with additional use. Quantitative evaluation on 31 subjects across different interaction behaviors shows that training instances identified by the PACE data collection have higher gaze point-interaction cue consistency than those identified by conventional approaches. An in-situ study using real-life tasks on a diverse set of interactive applications demonstrates that the PACE gaze estimation achieves an average error of 2.56º, which is comparable to state-of-the-art, but without the need for explicit training or calibration. This demonstrates the effectiveness of both the gaze estimation method and the corresponding data collection mechanism.

We present a vision-based, data-driven approach to identifying and measuring refractive errors in human subjects with low-cost, easily available equipment and no specialist training. Vision problems, such as refractive error (e.g. nearsightedness, astigmatism, etc) are common ocular problems, which, if uncorrected, may lead to serious visual impairment. The diagnosis of such defects conventionally requires expensive specialist equipment and trained personnel, which is a barrier in many parts of the developing world. Our approach aims to democratize optometric care by utilizing the computational power inherent in consumer-grade devices and the advances made possible by multimedia computing. We present results that show our system is able to match and outperform state-of-the-art medical devices under certain conditions.

Affect exchange is essential for healthy physical and social development [7], and friends and family communicate their emotions to each other instinctively. In particular, watching movies has always been a popular mode of socialization and video sharing is increasingly viewed as an effective way to facilitate communication of feelings and affects, even when the parties are not in the same location. We present an asynchronous video-sharing platform that uses Emotars to facilitate affect sharing in order to create and enhance the sense of togetherness through the experience of asynchronous movie watching. We investigate its potential impact and benefits, including a better viewing experience, supporting relationships, and strengthening engagement, connectedness and emotion awareness among individuals.

Most eye gaze estimation systems rely on explicit calibration, which is inconvenient to the user, limits the amount of possible training data and consequently the performance. Since there is likely a strong correlation between gaze and interaction cues, such as cursor and caret locations, a supervised learning algorithm can learn the complex mapping between gaze features and the gaze point by training on incremental data collected implicitly from normal computer interactions. We develop a set of robust geometric gaze features and a corresponding data validation mechanism that identifies good training data from noisy interaction-informed data collected in real-use scenarios. Based on a study of gaze movement patterns, we apply behavior-informed validation to extract gaze features that correspond with the interaction cue, and data-driven validation provides another level of crosschecking using previous good data. Experimental evaluation shows that the proposed method achieves an average error of 4.06º, and demonstrates the effectiveness of the proposed gaze estimation method and corresponding validation mechanism.