Continuous Gaze Tracking With Implicit Saliency-Aware Calibration on Mobile Devices

Gaze tracking is a useful human-to-computer interface, which plays an increasingly important role in a range of mobile applications. Gaze calibration is an indispensable component of gaze tracking, which transforms the eye coordinates to the screen coordinates. The existing approaches of gaze tracking either have limited accuracy or require the user's cooperation in calibration and in turn hurt the quality of experience. We in this paper propose vGaze, continuous gaze tracking with implicit saliency-aware calibration on mobile devices. The design of vGaze stems from our insight on the temporal and spatial dependent relation between the visual saliency and the user's gaze. vGaze is implemented as a light-weight software that identifies video frames with "useful" saliency information, sensing the user's head movement, performs opportunistic calibration using only those "useful" frames, and leverages historical information for accelerating saliency detection. We implement vGaze on a commercial mobile device and evaluate its performance in various scenarios. The results show that vGaze can work at real time with video playback applications. The average error of gaze tracking is 1.51 cm (2.884 degree) which decreases to 0.99 cm (1.891 degree) with historical information and 0.57 cm (1.089 degree) with an indicator

Eye Tracking Support for Visual Analytics Systems

Visual analytics (VA) research provides helpful solutions for interactive visual data analysis when exploring large and complex datasets. Due to recent advances in eye tracking technology, promising opportunities arise to extend these traditional VA approaches. Therefore, we discuss foundations for eye tracking support in VA systems. We first review and discuss the structure and range of typical VA systems. Based on a widely used VA model, we present five comprehensive examples that cover a wide range of usage scenarios. Then, we demonstrate that the VA model can be used to systematically explore how concrete VA systems could be extended with eye tracking, to create supportive and adaptive analytics systems. This allows us to identify general research and application opportunities, and classify them into research themes. In a call for action, we map the road for future research to broaden the use of eye tracking and advance visual analytics

iBall: Augmenting Basketball Videos with Gaze-moderated Embedded Visualizations

We present iBall, a basketball video-watching system that leverages gaze-moderated embedded visualizations to facilitate game understanding and engagement of casual fans. Video broadcasting and online video platforms make watching basketball games increasingly accessible. Yet, for new or casual fans, watching basketball videos is often confusing due to their limited basketball knowledge and the lack of accessible, on-demand information to resolve their confusion. To assist casual fans in watching basketball videos, we compared the game-watching behaviors of casual and die-hard fans in a formative study and developed iBall based on the fndings. iBall embeds visualizations into basketball videos using a computer vision pipeline, and automatically adapts the visualizations based on the game context and users' gaze, helping casual fans appreciate basketball games without being overwhelmed. We confrmed the usefulness, usability, and engagement of iBall in a study with 16 casual fans, and further collected feedback from 8 die-hard fans.Comment: ACM CHI2

Eye-Tracking in Virtual Reality: A Visceral Notice Approach for Protecting Privacy

Eye-tracking is in our future. Across many fields, eye-tracking is growing in prominence. This paper focuses on eye-tracking in virtual reality as a case study to illuminate novel privacy risks and propose a governance response to them: a design shift that provides users with an experientially resonant means of understanding privacy threats. It is a strategy that Ryan Calo calls “visceral notice.” To make our case for visceral notice, we proceed as follows. First, we provide a concise account of how eye-tracking works, emphasizing its threat to autonomy and privacy.  Second, we discuss the sensitive personal information that eye-tracking reveals, complications that limit what eye-tracking studies establish, and the comparative advantage large technology companies may have when tracking our eyes.  Third, we explain why eye-tracking will likely be crucial for developing virtual reality technology. Fourth, we review Calo’s conception of visceral notice and offer suggestions for applying it to virtual reality to help users better appreciate eye-tracking risks. Finally, we consider seven objections to our proposals and provide counterpoints to them. &nbsp

Proceedings of the 1st joint workshop on Smart Connected and Wearable Things 2016

These are the Proceedings of the 1st joint workshop on Smart Connected and Wearable Things (SCWT'2016, Co-located with IUI 2016). The SCWT workshop integrates the SmartObjects and IoWT workshops. It focusses on the advanced interactions with smart objects in the context of the Internet-of-Things (IoT), and on the increasing popularity of wearables as advanced means to facilitate such interactions

Towards a Task-based Guidance in Exploratory Visual Analytics

Exploring large datasets and identifying meaningful information is still an active topic in many application fields. Dealing with large datasets is currently not only a matter of simply collecting and structuring data for retrieval, but sometimes it also requires the provision of adequate means for guiding the user through the exploration process. Visualizations have shown to be an effective method in this context, the reason being that since they are grounded on visual cognition, people understand them and can naturally perform visual operations such as clustering, filtering and comparing quantities. However, systems which help us to create visualizations often require specific knowledge in data analysis, which ordinary users typically do not possess. To address this gap, we propose a system that guides the user in the data analysis process. To achieve this, the system observes current user behavior, tries to infer the task of the user and recommends the next analysis steps to help her to carry out the task

Proficiency-aware systems

In an increasingly digital world, technological developments such as data-driven algorithms and context-aware applications create opportunities for novel human-computer interaction (HCI). We argue that these systems have the latent potential to stimulate users and encourage personal growth. However, users increasingly rely on the intelligence of interactive systems. Thus, it remains a challenge to design for proficiency awareness, essentially demanding increased user attention whilst preserving user engagement. Designing and implementing systems that allow users to become aware of their own proficiency and encourage them to recognize learning benefits is the primary goal of this research. In this thesis, we introduce the concept of proficiency-aware systems as one solution. In our definition, proficiency-aware systems use estimates of the user's proficiency to tailor the interaction in a domain and facilitate a reflective understanding for this proficiency. We envision that proficiency-aware systems leverage collected data for learning benefit. Here, we see self-reflection as a key for users to become aware of necessary efforts to advance their proficiency. A key challenge for proficiency-aware systems is the fact that users often have a different self-perception of their proficiency. The benefits of personal growth and advancing one's repertoire might not necessarily be apparent to users, alienating them, and possibly leading to abandoning the system. To tackle this challenge, this work does not rely on learning strategies but rather focuses on the capabilities of interactive systems to provide users with the necessary means to reflect on their proficiency, such as showing calculated text difficulty to a newspaper editor or visualizing muscle activity to a passionate sportsperson. We first elaborate on how proficiency can be detected and quantified in the context of interactive systems using physiological sensing technologies. Through developing interaction scenarios, we demonstrate the feasibility of gaze- and electromyography-based proficiency-aware systems by utilizing machine learning algorithms that can estimate users' proficiency levels for stationary vision-dominant tasks (reading, information intake) and dynamic manual tasks (playing instruments, fitness exercises). Secondly, we show how to facilitate proficiency awareness for users, including design challenges on when and how to communicate proficiency. We complement this second part by highlighting the necessity of toolkits for sensing modalities to enable the implementation of proficiency-aware systems for a wide audience. In this thesis, we contribute a definition of proficiency-aware systems, which we illustrate by designing and implementing interactive systems. We derive technical requirements for real-time, objective proficiency assessment and identify design qualities of communicating proficiency through user reflection. We summarize our findings in a set of design and engineering guidelines for proficiency awareness in interactive systems, highlighting that proficiency feedback makes performance interpretable for the user.In einer zunehmend digitalen Welt schaffen technologische Entwicklungen - wie datengesteuerte Algorithmen und kontextabhängige Anwendungen - neuartige Interaktionsmöglichkeiten mit digitalen Geräten. Jedoch verlassen sich Nutzer oftmals auf die Intelligenz dieser Systeme, ohne dabei selbst auf eine persönliche Weiterentwicklung hinzuwirken. Wird ein solches Vorgehen angestrebt, verlangt dies seitens der Anwender eine erhöhte Aufmerksamkeit. Es ist daher herausfordernd, ein entsprechendes Design für Kompetenzbewusstsein (Proficiency Awareness) zu etablieren. Das primäre Ziel dieser Arbeit ist es, eine Methodik für das Design und die Implementierung von interaktiven Systemen aufzustellen, die Nutzer dabei unterstützen über ihre eigene Kompetenz zu reflektieren, um dadurch Lerneffekte implizit wahrnehmen können. Diese Arbeit stellt ein Konzept für fähigkeitsbewusste Systeme (proficiency-aware systems) vor, welche die Fähigkeiten von Nutzern abschätzen, die Interaktion entsprechend anpassen sowie das Bewusstsein der Nutzer über deren Fähigkeiten fördern. Hierzu sollten die Systeme gesammelte Daten von Nutzern einsetzen, um Lerneffekte sichtbar zu machen. Die Möglichkeit der Anwender zur Selbstreflexion ist hierbei als entscheidend anzusehen, um als Motivation zur Verbesserung der eigenen Fähigkeiten zu dienen. Eine zentrale Herausforderung solcher Systeme ist die Tatsache, dass Nutzer - im Vergleich zur Abschätzung des Systems - oft eine divergierende Selbstwahrnehmung ihrer Kompetenz haben. Im ersten Moment sind daher die Vorteile einer persönlichen Weiterentwicklung nicht unbedingt ersichtlich. Daher baut diese Forschungsarbeit nicht darauf auf, Nutzer über vorgegebene Lernstrategien zu unterrichten, sondern sie bedient sich der Möglichkeiten interaktiver Systeme, die Anwendern die notwendigen Hilfsmittel zur Verfügung stellen, damit diese selbst über ihre Fähigkeiten reflektieren können. Einem Zeitungseditor könnte beispielsweise die aktuelle Textschwierigkeit angezeigt werden, während einem passionierten Sportler dessen Muskelaktivität veranschaulicht wird. Zunächst wird herausgearbeitet, wie sich die Fähigkeiten der Nutzer mittels physiologischer Sensortechnologien erkennen und quantifizieren lassen. Die Evaluation von Interaktionsszenarien demonstriert die Umsetzbarkeit fähigkeitsbewusster Systeme, basierend auf der Analyse von Blickbewegungen und Muskelaktivität. Hierbei kommen Algorithmen des maschinellen Lernens zum Einsatz, die das Leistungsniveau der Anwender für verschiedene Tätigkeiten berechnen. Im Besonderen analysieren wir stationäre Aktivitäten, die hauptsächlich den Sehsinn ansprechen (Lesen, Aufnahme von Informationen), sowie dynamische Betätigungen, die die Motorik der Nutzer fordern (Spielen von Instrumenten, Fitnessübungen). Der zweite Teil zeigt auf, wie Systeme das Bewusstsein der Anwender für deren eigene Fähigkeiten fördern können, einschließlich der Designherausforderungen , wann und wie das System erkannte Fähigkeiten kommunizieren sollte. Abschließend wird die Notwendigkeit von Toolkits für Sensortechnologien hervorgehoben, um die Implementierung derartiger Systeme für ein breites Publikum zu ermöglichen. Die Forschungsarbeit beinhaltet eine Definition für fähigkeitsbewusste Systeme und veranschaulicht dieses Konzept durch den Entwurf und die Implementierung interaktiver Systeme. Ferner werden technische Anforderungen objektiver Echtzeitabschätzung von Nutzerfähigkeiten erforscht und Designqualitäten für die Kommunikation dieser Abschätzungen mittels Selbstreflexion identifiziert. Zusammengefasst sind die Erkenntnisse in einer Reihe von Design- und Entwicklungsrichtlinien für derartige Systeme. Insbesondere die Kommunikation, der vom System erkannten Kompetenz, hilft Anwendern, die eigene Leistung zu interpretieren

Privacy Intelligence: A Survey on Image Sharing on Online Social Networks

Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion. The recent image leaks from popular OSN services and the abuse of personal photos using advanced algorithms (e.g. DeepFake) have prompted the public to rethink individual privacy needs when sharing images on OSNs. However, OSN image sharing itself is relatively complicated, and systems currently in place to manage privacy in practice are labor-intensive yet fail to provide personalized, accurate and flexible privacy protection. As a result, an more intelligent environment for privacy-friendly OSN image sharing is in demand. To fill the gap, we contribute a systematic survey of 'privacy intelligence' solutions that target modern privacy issues related to OSN image sharing. Specifically, we present a high-level analysis framework based on the entire lifecycle of OSN image sharing to address the various privacy issues and solutions facing this interdisciplinary field. The framework is divided into three main stages: local management, online management and social experience. At each stage, we identify typical sharing-related user behaviors, the privacy issues generated by those behaviors, and review representative intelligent solutions. The resulting analysis describes an intelligent privacy-enhancing chain for closed-loop privacy management. We also discuss the challenges and future directions existing at each stage, as well as in publicly available datasets.Comment: 32 pages, 9 figures. Under revie

XAIR: A Framework of Explainable AI in Augmented Reality

Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses "when", "what", and "how" to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR's utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.Comment: Proceedings of the 2023 CHI Conference on Human Factors in Computing System