9th International Conference on Body Area Networks

Research Article

Towards Big Data for Activity Recognition: A Novel Database Fusion Strategy

  • @INPROCEEDINGS{10.4108/icst.bodynets.2014.256946,
        author={Dominik Schuldhaus and Heike Leutheuser and Bjoern Eskofier},
        title={Towards Big Data for Activity Recognition: A Novel Database Fusion Strategy},
        proceedings={9th International Conference on Body Area Networks},
        keywords={activity recognition big data database fusion data mining decision-level fusion inertial sensors},
  • Dominik Schuldhaus
    Heike Leutheuser
    Bjoern Eskofier
    Year: 2014
    Towards Big Data for Activity Recognition: A Novel Database Fusion Strategy
    DOI: 10.4108/icst.bodynets.2014.256946
Dominik Schuldhaus1,*, Heike Leutheuser1, Bjoern Eskofier1
  • 1: Digital Sports Group, Pattern Recognition Lab, Friedrich-Alexander-Universitaet Erlangen-Nuernberg (FAU), Germany
*Contact email: dominik.schuldhaus@fau.de


Activity recognition is mandatory in order to provide feedback about the individual quality of life. Usually, activity recognition algorithms are evaluated on one specific database which is limited in the number of subjects, sensors and type of activities. In this paper, a novel database fusion strategy was proposed which fused three different publicly available databases to one large database consisting of 42 subjects. The fusion of databases addresses the two attributes high volume and high variety of the term "'big data"'. Furthermore, an algorithm was developed which can deal with multiple databases varying in the number of sensors and activities. Nine features were computed in sliding windows of inertial data of several sensor positions. Decision-level fusion was performed in order to combine the information of different sensor positions. The proposed classification system achieved an overall mean classification rate of 85.8 % and allows an easy integration of new databases. Using big data is necessary to develop robust and stable activity recognition algorithms in the future.