Online from: 2005
Subject Area: Information and Knowledge Management
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|Title:||ContextAlert: context-aware alert mode for a mobile phone|
|Author(s):||Santi Phithakkitnukoon, (Massachusetts Institute of Technology, Cambridge, Massachusetts, USA), Ram Dantu, (University of North Texas, Denton, Texas, USA)|
|Citation:||Santi Phithakkitnukoon, Ram Dantu, (2010) "ContextAlert: context-aware alert mode for a mobile phone", International Journal of Pervasive Computing and Communications, Vol. 6 Iss: 3, pp.1 - 23|
|Keywords:||Adaptive system theory, Mobile communication systems|
|Article type:||Research paper|
|DOI:||10.1108/17427371011084266 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
|Acknowledgements:||This work is supported by the National Science Foundation under grants CNS-0627754, CNS-0619871, and CNS-0551694.|
Purpose – Mobile computing research has been focused on developing technologies for handheld devices such as mobile phones, notebook computers, and mobile IP. Today, emphasis is increasing on context-aware computing, which aims to build the intelligence into mobile devices to sense and respond to the user's context. The purpose of this paper is to present a context-aware mobile computing model (
Design/methodology/approach – The paper proposes a three-step approach in designing the model based on the embedded sensor data (accelerometer, GPS antenna, and microphone) of a G1 Adriod phone. As adaptivity is essential for context-aware computing, within this model a new learning mechanism is presented to maintain a constant adaptivity rate for new learning while keeping the catastrophic forgetting problem minimal.
Findings – The model has been evaluated in many aspects using data collected from human subjects. The experiment results show that the proposed model performs well and yields a promising result.
Originality/value – This paper is distinguished from other previous papers by: first, using multiple sensors embeded in the mobile phone, which is more realistic for detecting the user's context than having various sensors attached to different parts of user's body; second, by being a novel model that uses sensed contextual information to provide a service that better synchronizes the user's daily life with a context-aware alert mode. With this service, the user can avoid the problems such as forgetting to switch to vibrate mode while in a meeting or a movie theater, and taking the risk of picking up a phone call while driving, and third, being an adaptive learning algorithm that maintains a constant adaptivity rate for new learning while keeping the catastrophic forgetting problem minimal.
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