title: Classification of periodic activities using the Wasserstein distance.
abstract: In this paper, we introduce a novel nonparametric classification technique based on the use of the Wasserstein distance. The proposed scheme is applied in a biomedical context for the analysis of recorded accelerometer data: the aim is to retrieve three types of periodic activities (walking, biking, and running) from a time-frequency representation of the data. The main interest of the use of the Wasserstein distance lies in the fact that it is less sensitive to the location of the frequency peaks than to the global structure of the frequency pattern, allowing us to detect activities almost independently of their speed or incline. Our system is tested on a 24-subject corpus: results show that the use of Wasserstein distance combined with some supervised learning techniques allows us to compare with some more complex classification systems.
authors.affiliation: TELECOM ParisTech, Paris F-75014, France. laurent.oudre@telecom-paristech.fr
authors.forename: Laurent
authors.initials: L
authors.lastname: Oudre
authors.forename: Jérémie
authors.initials: J
authors.lastname: Jakubowicz
authors.forename: Pascal
authors.initials: P
authors.lastname: Bianchi
authors.forename: Chantal
authors.initials: C
authors.lastname: Simon
mesh_terms: D000054:Acceleration; D056044:Actigraphy / Q000379:methods*; D000465:Algorithms; D001683:Biological Clocks / Q000502:physiology*; D006801:Humans; D009043:Motor Activity / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity