Social Attentive User Interfaces For The Age Of Interruption

Grant number: DE180100315 | Funding period: 2018 - 2021

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Abstract

This proposal aims to enable the development of social attentive user interfaces—those that employ sensors such as eye trackers and thermal cameras to monitor the locus and level of users' attention and adapt their behaviour accordingly. The project lies in the field of Human-Computer Interaction, drawing from machine learning methods to design novel user experiences. Expected outcomes include insights into how people manage their attention, new methods for attention estimation and classification, and novel systems for e-learning and work productivity that demonstrate these new capabilities. As a result, this project will provide the benefit of enabling system to no longer be blind to users’..

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Related publications (11)

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Exploring the usage of thermal imaging for understanding video lecture designs and students' experiences

N Srivastava, S Nawaz, JM Lodge, E Velloso, S Erfani, J Bailey

2020-03-23

Video is becoming a dominant medium for the delivery of educational material. Despite the widespread use of video for learning, th..

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A Survey of Context Simulation for Testing Mobile Context-Aware Applications

Chu Luo, Jorge Goncalves, Eduardo Velloso, Vassilis Kostakos

2020-02-05

Equipped with an abundance of small-scale microelectromechanical sensors, modern mobile devices such as smartphones and smartwatch..

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Towards context-free semantic localisation

Gabriele Marini, Jorge Gonçalves, Eduardo Velloso, Raja Jurdak, Vassilis Kostakos

2019-09-09

We propose a context-free semantic localisation approach to visualise and analyse indoor movements. We focus on settings where ind..

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A Tale of Two Perspectives: A Conceptual Framework of User Expectations and Experiences of Instructional Fitness Apps

Ahed Aladwan, R Kelly, Steven Baker, Eduardo Velloso

2019-01-01

We present a conceptual framework grounded in both users’ reviews and HCI theories, residing between practices and theories as a f..

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Cognitive Aid: Task Assistance Based On Mental Workload Estimation

Qiushi Zhou, Joshua Newn, Namrata Srivastava, Tilman Dingier, Jorge Goncalves, Eduardo Velloso

2019-01-01

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Combining implicit gaze and AI for real-time intention projection

Joshua Newn, Ronal Singh, Eduardo Velloso, Frank Vetere

2019-01-01

Intention recognition is the process of using behavioural cues to infer an agent's goals or future behaviour. In face-to-face comm..

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Continuous Evaluation of Video Lectures from Real-Time Difficulty Self-Report

Namrata Srivastava, Eduardo Velloso, Jason M Lodge, Sarah Erfani, James Bailey

2019-01-01

With the increased reach and impact of video lectures, it is crucial to understand how they are experienced. Whereas previous stud..

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Measuring the Effects of Stress on Mobile Interaction

Zhanna Sarsenbayeva, Niels van Berkel, Danula Hettiachchi, Weiwei Jiang, Tilman Dingler, Eduardo Velloso, Vassilis Kostakos, Jorge Goncalves

2019-01-01

Research shows that environmental factors such as ambient noise and cold ambience can render users situationally impaired, adverse..

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SpinalLog: Visuo-Haptic Feedback in Musculoskeletal Manipulation Training

D Antony Chacon, Eduardo Velloso, Katrin Wolf, Thuong Hoang

2019-01-01

Current techniques for teaching spinal mobilisation follow the traditional classroom approach: an instructor demonstrates a techni..

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Combining Low and Mid-Level Gaze Features for Desktop Activity Recognition

Namrata Srivastava, Joshua Newn, E Velloso

2018-01-01

Human activity recognition (HAR) is an important research area due to its potential for building context-aware interactive systems..

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GazeGrip: Improving Mobile Device Accessibility with Gaze & Grip Interaction

Q Zhou, E Velloso

2017-01-01