Publications

From 2017 (Clear Search)

2017
Publication Details
  • The Handbook of Multimodal-Multisensor Interfaces
  • May 9, 2017

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Observe at a person pointing out and describing something. Where is that person looking? Chances are good that this person also looks at what she is talking about and pointing at. Gaze is naturally coordinated with our speech and hand movements. By utilizing this tendency, we can create a natural interaction with computing devices and environments. In this chapter, we will first briefly discuss some basic properties of the gaze signal we can get from eye trackers, followed by a review of a multimodal system utilizing the gaze signal as one input modality. In Multimodal Gaze Interaction, data from eye trackers is used as an active input mode where for instance gaze is used as an alternative, or complimentary, pointing modality along with other input modalities. Using gaze as an active or explicit input method is challenging for several reasons. One of them being that eyes are primarily used for perceiving our environment, so knowing when a person selects an item with gaze versus just looking around is an issue. Researchers have tried to solve this by combining gaze with various input methods, such as manual pointing, speech, touch, etc. However, gaze information can also be used in interactive systems, for other purposes than explicit pointing since a user's gaze is a good indication of the user's attention. In passive gaze interaction, the gaze is not used as the primary input method, but as a supporting input method. In these kinds of systems, gaze is mainly used for inferring and reasoning about the user's cognitive state or activities in a way that can support the interaction. These kinds of multimodal systems often combine gaze with a multitude of input modalities. In this chapter we focus on interactive systems, exploring the design space for gaze-informed multimodal interaction spanning from gaze as active input mode to passive and if the usage scenario is stationary (at e.g. a desk) or mobile. There are a number of studies aimed at describing, detecting or modeling specific behaviors or cognitive states. We will touch on some of these works since they can guide us in how to build gaze-informed multimodal interaction.

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Work breaks can play an important role in the mental and physical well-being of workers and contribute positively to productivity. In this paper we explore the use of activity-, physiological-, and indoor-location sensing to promote mobility during work-breaks. While the popularity of devices and applications to promote physical activity is growing, prior research highlights important constraints when designing for the workplace. With these constraints in mind, we developed BreakSense, a mobile application that uses a Bluetooth beacon infrastructure, a smartphone and a smartwatch to encourage mobility during breaks with a game-like design. We discuss constraints imposed by design for work and the workplace, and highlight challenges associated with the use of noisy sensors and methods to overcome them. We then describe a short deployment of BreakSense within our lab that examined bound vs. unbound augmented breaks and how they affect users’ sense of completion and readiness to work.

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Users often use social media to share their interest in products. We propose to identify purchase stages from Twitter data following the AIDA model (Awareness, Interest, Desire, Action). In particular, we define a task of classifying the purchase stage of each tweet in a user's tweet sequence. We introduce RCRNN, a Ranking Convolutional Recurrent Neural Network which computes tweet representations using convolution over word embeddings and models a tweet sequence with gated recurrent units. Also, we consider various methods to cope with the imbalanced label distribution in our data and show that a ranking layer outperforms class weights.
Publication Details
  • IEEE PerCom 2017
  • Mar 13, 2017

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We present Lift, a visible light-enabled finger tracking and object localization technique that allows users to perform freestyle multi-touch gestures on any object’s surface in an everyday environment. By projecting encoded visible patterns onto an object’s surface (e.g. paper, display, or table), and localizing the user’s fingers with light sensors, Lift offers users a richer interactive space than the device’s existing interfaces. Additionally, everyday objects can be augmented by attaching sensor units onto their surface to accept multi-touch gesture input. We also present two applications as a proof of concept. Finally, results from our experiments indicate that Lift can localize ten fingers simultaneously with accuracy of 0.9 mm and 1.8 mm on two axes respectively and an average refresh rate of 84 Hz with 16.7ms delay on WiFi and 12ms delay on serial, making gesture recognition on noninstrumented objects possible.
Publication Details
  • TRECVID Workshop
  • Mar 1, 2017

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This is a summary of our participation in the TRECVID 2016 video hyperlinking task (LNK). We submitted four runs in total. A baseline system combined on established vectorspace text indexing and cosine similarity. Our other runs explored the use of distributed word representations in combination with fine-grained inter-segment text similarity measures.