@article {44,
	title = {ContextAlert: Context-aware alert mode for a mobile phone},
	journal = {International Journal of Pervasive Computing and Communications},
	volume = {6},
	year = {2010},
	month = {Sep},
	pages = {1{\textendash}23},
	abstract = {<p>Purpose {\textendash} 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{\textquoteright}s context. The purpose of this paper is to present a context-aware mobile computing model (ContextAlert) that senses the user{\textquoteright}s context and intelligently configures the mobile phone alert mode accordingly. Design/methodology/approach {\textendash} 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 {\textendash} 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 {\textendash} 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{\textquoteright}s context than having various sensors attached to different parts of user{\textquoteright}s body; second, by being a novel model that uses sensed contextual information to provide a service that better synchronizes the user{\textquoteright}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.</p>
},
	issn = {1742-7371},
	doi = {10.1108/17427371011084266},
	author = {Phithakkitnukoon, Santi and Ram Dantu}
}
