Reactive Video: Adaptive Video Playback Based on User Motion for Supporting Physical ActivityVideos are a convenient platform to begin, maintain, or improve a ftness program or physical activity. Traditional video systems allow users to manipulate videos through specifc user interface actions such as button clicks or mouse drags, but have no model of what the user is doing and are unable to adapt in useful ways. We present adaptive video playback, which seamlessly synchronises video playback with the user’s movements, building upon the principle of direct manipulation video navigation. We implement adaptive video playback in Reactive Video, a vision-based system which supports users learning or practising a physical skill. The use of pre-existing videos removes the need to create bespoke content or specially authored videos, and the system can provide real-time guidance and feedback to better support users when learning new movements. Adaptive video playback using a discrete Bayes and particle flter are evaluated on a data set collected of participants performing tai chi and radio exercises. Results show that both approaches can accurately adapt to the user’s movements, however reversing playback can be problematic.2020CCChristopher Clarke et al.Hand Gesture RecognitionHuman Pose & Activity RecognitionFitness Tracking & Physical Activity MonitoringUIST
Celebrating Everyday Success: Improving Engagement and Motivation using a System for Recording Daily HighlightsThe demands of daily work offer few opportunities for workers to take stock of their own progress, big or small, which can lead to lower motivation, engagement, and higher risk of burnout. We present Highlight Matome, a personal online tool that encourages workers to quickly record and rank a single work highlight each day, helping them gain awareness of their own successes. We describe results from a field experiment investigating our tool's effectiveness for improving workers' engagement, perceptions, and affect. Thirty-three knowledge workers in Japan and the U.S. used Highlight Matome for six weeks. Our results show that using our tool for less than one minute each day significantly increased measures of work engagement, dedication, and positivity. A qualitative analysis of the highlights offers a window into participants' emotions and perceptions. We discuss implications for theories of inner work life and worker well-being.2020DADaniel Avrahami et al.FXPALKnowledge Worker Tools & WorkflowsNotification & Interruption ManagementWorkplace Wellbeing & Work StressCHI
Overcoming Distractions during Transitions from Break to Work using a Conversational Website-Blocking SystemWork breaks--both physical and digital--play an important role in productivity and workplace wellbeing. Yet, the growing availability of digital distractions from online content can turn breaks into prolonged "cyberloafing". In this paper, we present UpTime, a system that aims to support workers' transitions from breaks back to work--moments susceptible to digital distractions. Combining a browser extension and chatbot, users interact with UpTime through proactive and reactive chat prompts. By sensing transitions from inactivity, UpTime helps workers avoid distractions by automatically blocking distracting websites temporarily, while still giving them control to take necessary digital breaks. We report findings from a 3-week comparative field study with 15 workers. Our results show that automatic, temporary blocking at transition points can significantly reduce digital distractions and stress without sacrificing workers' sense of control. Our findings, however, also emphasize that overloading users' existing communication channels for chatbot interaction should be done thoughtfully.2019VTVincent W.-S. Tseng et al.Cornell UniversityVoice User Interface (VUI) DesignConversational ChatbotsNotification & Interruption ManagementCHI
Flexible Learning with Semantic Visual Exploration and Sequence-Based Recommendation of MOOC VideosMassive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their rigid, predetermined curricula. To overcome this limitation, this paper contributes a visual recommender system called MOOCex. The system recommends lecture videos across different courses by considering both video contents and sequential inter-topic relationships mined from course syllabi; and more importantly, it allows for interactive visual exploration of the semantic space of recommendations within a learner’s current context. When compared to traditional methods (e.g., content-based recommendation and ranked list representations), MOOCex suggests videos from more diverse perspectives and helps learners make better video playback decisions. Further, feedback from MOOC learners and instructors indicates that the system enhances both learning and teaching effectiveness.2018JZJian Zhao et al.FX Palo Alto LaboratoryRecommender System UXTime-Series & Network Graph VisualizationOnline Learning & MOOC PlatformsCHI
I Should Listen More: Real-time Sensing and Feedback of Non-Verbal Communication in Video TelehealthVideo telehealth is growing to allow more clinicians to see patients from afar. As a result, clinicians, typically trained for in-person visits, must learn to communicate both health information and non-verbal affective signals to patients through a digital medium. We introduce a system called ReflectLive that senses and provides real-time feedback about non-verbal communication behaviors to clinicians so they can improve their communication behaviors. A user evaluation with 10 clinicians showed that the real-time feedback helped clinicians maintain better eye contact with patients and was not overly distracting. Clinicians reported being more aware of their non-verbal communication behaviors and reacted positively to summaries of their conversational metrics, motivating them to want to improve. Using ReflectLive as a probe, we also discuss the benefits and concerns around automatically quantifying the “soft skills” and complexities of clinician-patient communication, the controllability of behaviors, and the design considerations for how to present real-time and summative feedback to clinicians.2018HFHeather Ashley Faucett et al.Collaboration in Clinical ContextsCSCW
Designing the Club of the Future with Data: A Case Study on Collaboration of Creative IndustriesThis paper reflects on the development of a multi-sensory clubbing experience which was deployed during a two-day event within the context of the Amsterdam Dance Event in October 2016 in Amsterdam. We present how the entire experience was developed end-to-end and deployed at the event through the collaboration of several project partners from industries such as art and design, music, food, technology and research. Central to the system are smart textiles, namely wristbands equipped with Bluetooth LE sensors which were used to sense people attending the dance event. We describe the components of the system, the development process, the collaboration between the involved entities and the event itself. To conclude the paper, we highlight insights gained from conducting a real world research deployment across many collaborators and stakeholders with different backgrounds.2018SCSergio Cabrero et al.Centrum voor Wiskunde en InformaticaElectronic Textiles (E-textiles)Digital Art Installations & Interactive PerformanceCHI
Below the Surface: Unobtrusive Activity Recognition for Work Surfaces using RF-radar sensingActivity recognition is a core component of many intelligent and context-aware systems. In this paper, we present a solution for discreetly and unobtrusively recognizing common work activities above a work surface without using cameras. We demonstrate our approach, which utilizes an RF-radar sensor mounted under the work surface, in two work domains; recognizing work activities at a convenience-store counter (useful for post-hoc analytics) and recognizing common office deskwork activities (useful for real-time applications). We classify seven clerk activities with 94.9% accuracy using data collected in a lab environment, and recognize six common deskwork activities collected in real offices with 95.3% accuracy. We show that using multiple projections of RF signal leads to improved recognition accuracy. Finally, we show how smartwatches worn by users can be used to attribute an activity, recognized with the RF sensor, to a particular user in multi-user scenarios. We believe our solution can mitigate some of users’ privacy concerns associated with cameras and is useful for a wide range of intelligent systems.2018DADaniel Avrahami et al.Human Pose & Activity RecognitionContext-Aware ComputingIUI
T-Cal: Understanding Team Conversational Data with Calendar-based VisualizationUnderstanding team communication and collaboration patterns is critical for improving work efficiency in organizations. This paper presents an interactive visualization system, T-Cal, that supports the analysis of conversation data from modern team messaging platforms (e.g., Slack). T-Cal employs a user-familiar visual interface, a calendar, to enable seamless multi-scale browsing of data from different perspectives. T-Cal also incorporates a number of analytical techniques for disentangling interleaving conversations, extracting keywords, and estimating sentiment. The design of T-Cal is based on an iterative user-centered design process including interview studies, requirements gathering, initial prototypes demonstration, and evaluation with domain users. The resulting two case studies indicate the effectiveness and usefulness of T-Cal in real-world applications, including daily conversations within an industry research lab and student group chats in a MOOC.2018SFSiwei Fu et al.Hong Kong University of Science and TechnologyInteractive Data VisualizationKnowledge Management & Team AwarenessCHI