title: Dynamic detection of window starting positions and its implementation within an activity recognition framework.
abstract: Activity recognition is an intrinsic component of many pervasive computing and ambient intelligent solutions. This has been facilitated by an explosion of technological developments in the area of wireless sensor network, wearable and mobile computing. Yet, delivering robust activity recognition, which could be deployed at scale in a real world environment, still remains an active research challenge. Much of the existing literature to date has focused on applying machine learning techniques to pre-segmented data collected in controlled laboratory environments. Whilst this approach can provide valuable ground truth information from which to build recognition models, these techniques often do not function well when implemented in near real time applications. This paper presents the application of a multivariate online change detection algorithm to dynamically detect the starting position of windows for the purposes of activity recognition.
authors.affiliation: Department of Telematic and Electronic Engineering, Universidad Politécnica de Madrid, Spain. Electronic address: qin.ni@alumnos.upm.es.
authors.forename: Qin
authors.initials: Q
authors.lastname: Ni
authors.affiliation: School of Computing and Mathematics, University of Ulster, North Ireland, UK. Electronic address: t.patterson@ulster.ac.uk.
authors.forename: Timothy
authors.initials: T
authors.lastname: Patterson
authors.affiliation: School of Computing and Mathematics, University of Ulster, North Ireland, UK. Electronic address: i.cleland@ulster.ac.uk.
authors.forename: Ian
authors.initials: I
authors.lastname: Cleland
authors.affiliation: School of Computing and Mathematics, University of Ulster, North Ireland, UK. Electronic address: cd.nugent@ulster.ac.uk.
authors.forename: Chris
authors.initials: C
authors.lastname: Nugent
mesh_terms: D056044:Actigraphy / Q000379:methods*; D000203:Activities of Daily Living; D000465:Algorithms; D006801:Humans
keywords: Activities of Daily Living (ADLs); Activity recognition; Change detection; Data segmentation; Feature selection