DocumentCode :
139539
Title :
RingLearn: Long-term mitigation of disruptive smartphone interruptions
Author :
Smith, Johan ; Dulay, Naranker
Author_Institution :
Imperial Coll. London, London, UK
fYear :
2014
fDate :
24-28 March 2014
Firstpage :
27
Lastpage :
35
Abstract :
Mitigating the consequences of disruptive smartphone interruptions remains a challenging problem for smartphone designers. Proposed solutions often incorporate machine-learning techniques with remedies that include delaying user notifications until an opportune moment or changing the intensity and/or mode of the notification (fewer rings, vibration mode). This paper describes a new machine-learning approach that aims to maintain the quality of mitigation under concept drift - unforeseen changes in context or the user´s behaviour over time. We demonstrate our approach by developing an application for Android phones to mitigate disruptive phone calls (RingLearn). We report on a field trial of the application conducted over 2 months with 10 users and suggest that long-term mitigation can be practical with careful design that addresses concept drift.
Keywords :
human computer interaction; learning (artificial intelligence); mobile computing; smart phones; Android phones; HCI; RingLearn; concept drift; disruptive phone calls mitigation; disruptive smartphone interruptions; long-term mitigation; machine-learning approach; mitigation quality; smartphone designers; user behaviour; Calendars; Context; Context modeling; Delays; Human computer interaction; Sensors; Smart phones; concept drift; interruptions; lazy learning; online learning; smartphone;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on
Conference_Location :
Budapest
Type :
conf
DOI :
10.1109/PerComW.2014.6815160
Filename :
6815160
Link To Document :
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