Memorias de investigación
Ponencias en congresos:
Mars: a personalised mobile activity recognition system
Año:2012
Áreas de investigación
  • Análisis de datos
Datos
Descripción
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 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 quick model adaptation. One of the stand out features of MARS is that training/updating the model takes less than 30 seconds per activity. MARS has been implemented on the Android platform to demonstrate that it can achieve accurate mobile activity recognition. Moreover, we can show in practice 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.
Internacional
Si
Nombre congreso
IEEE 13th International Conference on Mobile Data Management (MDM), 2012
Tipo de participación
960
Lugar del congreso
india
Revisores
Si
ISBN o ISSN
978-0-7695-4713-8
DOI
10.1109/MDM.2012.33
Fecha inicio congreso
23/07/2012
Fecha fin congreso
26/07/2012
Desde la página
316
Hasta la página
319
Título de las actas
13th International Conference on Mobile Data Management
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Joao Bartolo Gomes UPM
  • Autor: Shonali Krishnaswamy
  • Autor: Mohamed Medhat Gaber
  • Autor: Pedro Sousa
  • Autor: Ernestina Menasalvas Ruiz UPM
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Data Mining Engineering (DaME) Ingeniería de Minería de datos