New publication: Measuring Inviscid Text Entry Using Image Description Tasks

We argue that measuring the Inviscid text entry rate requires new evaluation methods that support freeform text entry and that are not based on the traditional transcription/copy tasks. In this position paper we propose use of image description tasks and share some of our experiences of using this new language agnostic task type for free form text entry.

Dunlop, M., Nicol E., Komninos A., Dona P., & Durga N. (2016).  Measuring Inviscid Text Entry Using Image Description Tasks. CHI’16 Workshop on Inviscid Text Entry and Beyond. San Jose, CA.
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New publication: Gestural Control of Pervasive Systems using a Wireless Sensor Body Area Network

This paper describes the prototype implementation of a pervasive, wearable gestural input and control system based on a full body-motion-capture system using low-power wireless sensors. Body motion is used to implement a whole body gesture-driven interface to afford control over ambient computing devices.

Mandrychenko, ii, O., Barrie P., & Komninos A. (2009).  Gestural Control of Pervasive Systems using a Wireless Sensor Body Area Network. Mobile Interaction with the Real World (MIRW'09), Workshop in conjunction with MobileHCI'09. ..

Smartphone Notifications Dataset available for download

We have released a rich dataset containing 176,195 logged notifications received on the Android devices of 14 users, in the period between 2018-05-07 and 2018-06-05. The dataset accompanies our publication Komninos, A., Frengkou E., & Garofalakis J. (2018). Predicting User Responsiveness to Smartphone Notifications for Edge Computing. 2018 European Conference on Ambient Intelligence (AmI-18). Larnaca, Cyprus, Springer. .

Please free to use for your own research, citing our work if you found the dataset useful.

New publication: Predicting User Responsiveness to Smartphone Notifications for Edge Computing

Edge computing requires the addressing of several challenges in terms of privacy, complexity, bandwidth and battery life. While in the past attempts have been made to predict users’ responsiveness to smartphone notifications, we show that this is possible with a minimal number of just three features synthesized from non-sensor based data. Our approach demonstrates that it is possible to classify user attentiveness to notifications with good accuracy, and predict response time to any type of notification within a margin of 1 minute, without the need for personalized modelling.

Komninos, A., Frengkou E., & Garofalakis J. (2018).  Predicting User Responsiveness to Smartphone Notifications for Edge Computing. 2018 European Conference on Ambient Intelligence (AmI-18). Larnaca, Cyprus, Springer. DOI:10.1007/978-3-030-03062-9_1
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New publication: A glimpse of mobile text entry errors and corrective behaviour in the wild

Research in mobile text entry has long focused on speed and input errors during lab studies. However, little is known about how input errors emerge in real- world situations or how users deal with these. We present findings from an in-the-wild study of everyday text entry and discuss their implications for future studies.

Komninos, A., Dunlop M., Katsaris K., & Garofalakis J. (2018).  A glimpse of mobile text entry errors and corrective behaviour in the wild. Extended Abstracts, Mobile HCI'18. Barcelona, Spain, ACM. DOI:10.1145/3236112.3236143

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