From Offline to Real-Time Distributed Activity Recognition in Wireless Sensor Networks for Healthcare: A Review.

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

This review presents the state of the art and a global overview of research challenges of real-time distributed activity recognition in the field of healthcare. Offline activity recognition is discussed as a starting point to establish the useful concepts of the field, such as sensor types, activity labeling and feature extraction, outlier detection, and machine learning. New challenges and obstacles brought on by real-time centralized activity recognition such as communication, real-time activity labeling, cloud and local approaches, and real-time machine learning in a streaming context are then discussed. Finally, real-time distributed activity recognition is covered through existing implementations in the scientific literature, and six main angles of optimization are defined: Processing, memory, communication, energy, time, and accuracy. This survey is addressed to any reader interested in the development of distributed artificial intelligence as well activity recognition, regardless of their level of expertise.

Authors

  • Rani Baghezza
    Département D'informatique et de Mathématique, Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, Canada.
  • Kévin Bouchard
    Département D'informatique et de Mathématique, Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, Canada.
  • Abdenour Bouzouane
    Département D'informatique et de Mathématique, Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, Canada.
  • Charles Gouin-Vallerand
    Departement of Information Systems and Quantitative Methods in Management, École de Gestion, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada.