02408nas a2200157   4500008004100000022001400041245006400055210006300119260000800182300001100190490000600201520196300207100002802170700001502198856003702213       2010                        eng d  a1742-737100aContextAlert: Context‐aware alert mode for a mobile phone0 aContextAlert Context‐aware alert mode for a mobile phone  cSep  a1–230 v63 a<p>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 (ContextAlert) that senses the user’s context and intelligently configures the mobile phone alert mode accordingly. 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.</p>
1 aPhithakkitnukoon, Santi1 aDantu, Ram  uhttps://nsl.cse.unt.edu/node/221