Mars: a personalised mobile activity recognition system

Menasalvas Ruiz, Ernestina and Bártolo Gomes, Joao Paulo and Krishnaswamy, Shonali and Gaber, Mohamed M. and Sousa, Pedro (2012). Mars: a personalised mobile activity recognition system. In: "13th International Conference on Mobile Data Management", 23/07/2012 - 26/07/2012, Bengaluru, Karnataka, India. ISBN 978-0-7695-4713-8. pp. 316-319. https://doi.org/10.1109/MDM.2012.33.

Description

Title: Mars: a personalised mobile activity recognition system
Author/s:
  • Menasalvas Ruiz, Ernestina
  • Bártolo Gomes, Joao Paulo
  • Krishnaswamy, Shonali
  • Gaber, Mohamed M.
  • Sousa, Pedro
Item Type: Presentation at Congress or Conference (Article)
Event Title: 13th International Conference on Mobile Data Management
Event Dates: 23/07/2012 - 26/07/2012
Event Location: Bengaluru, Karnataka, India
Title of Book: Proceedings of the 2012 IEEE 13th International Conference on Mobile Data Management (mdm 2012)
Date: 2012
ISBN: 978-0-7695-4713-8
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the sensory data that is available on today’s smart phones. The state of the art in mobile activity recognition uses traditional classification learning techniques. Thus, the learning process typically involves: i) collection of labelled sensory data that is transferred and collated in a centralised repository; ii) model building where the classification model is trained and tested using the collected data; iii) a model deployment stage where the learnt model is deployed on-board a mobile device for identifying activities based on new sensory data. In this paper, we demonstrate the Mobile Activity Recognition System (MARS) where for the first time the model is built and continuously updated on-board the mobile device itself using data stream mining. The advantages of the on-board approach are that it allows model personalisation and increased privacy as the data is not sent to any external site. Furthermore, when the user or its activity profile changes MARS enables promptly adaptation. MARS has been implemented on the Android platform to demonstrate that it can achieve accurate mobile activity recognition. Moreover, we can show in practise that MARS quickly adapts to user profile changes while at the same time being scalable and efficient in terms of consumption of the device resources.

More information

Item ID: 21065
DC Identifier: http://oa.upm.es/21065/
OAI Identifier: oai:oa.upm.es:21065
DOI: 10.1109/MDM.2012.33
Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6341409
Deposited by: Memoria Investigacion
Deposited on: 11 Nov 2013 17:51
Last Modified: 21 Apr 2016 11:10
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