Identifying Important Action Primitives for High Level Activity Recognition

  • Atif Manzoor
  • Claudia Villalonga
  • Alberto Calatroni
  • Hong-Linh Truong
  • Daniel Roggen
  • Schahram Dustdar
  • Gerhard Tröster
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6446)

Abstract

Smart homes have a user centered design that makes human activity as the most important type of context to adapt the environment according to people’s needs. Sensor systems that include a variety of ambient, vision based, and wearable sensors are used to collect and transmit data to reasoning algorithms to recognize human activities at different levels of abstraction. Despite various types of action primitives are extracted from sensor data and used with state of the art classification algorithms there is little understanding of how these action primitives affect high level activity recognition. In this paper we utilize action primitives that can be extracted from data collected by sensors worn on human body and embedded in different objects and environments to identify how various types of action primitives influence the performance of high level activity recognition systems. Our experiments showed that wearable sensors in combination with object sensors clearly play a crucial role in recognizing high level activities and it is indispensable to use wearable sensors in smart homes to improve the performance of activity recognition systems.

Keywords

Activity recognition smart homes action primitives 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Atif Manzoor
    • 1
  • Claudia Villalonga
    • 2
  • Alberto Calatroni
    • 2
  • Hong-Linh Truong
    • 1
  • Daniel Roggen
    • 2
  • Schahram Dustdar
    • 1
  • Gerhard Tröster
    • 2
  1. 1.Distributed Systems GroupVienna University of TechnologyViennaAustria
  2. 2.Wearable Computing LaboratoryETHZürichSwitzerland

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