Recognizing water-based activities in the home through infrastructure-mediated sensing

  • Authors:

  • Edison Thomaz;Vinay Bettadapura;Gabriel Reyes;Megha Sandesh;Grant Schindler;Thomas Plötz;Gregory D. Abowd;Irfan Essa

  • Affiliations:

  • School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia;School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia;School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia;School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia;School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia;Newcastle University, UK;School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia;School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia

  • Venue:
  • Proceedings of the 2012 ACM Conference on Ubiquitous Computing
  • Year:
  • 2012

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Abstract

Activity recognition in the home has been long recognized as the foundation for many desirable applications in fields such as home automation, sustainability, and healthcare. However, building a practical home activity monitoring system remains a challenge. Striking a balance between cost, privacy, ease of installation and scalability continues to be an elusive goal. In this paper, we explore infrastructure-mediated sensing combined with a vector space model learning approach as the basis of an activity recognition system for the home. We examine the performance of our single-sensor water-based system in recognizing eleven high-level activities in the kitchen and bathroom, such as cooking and shaving. Results from two studies show that our system can estimate activities with overall accuracy of 82.69% for one individual and 70.11% for a group of 23 participants. As far as we know, our work is the first to employ infrastructure-mediated sensing for inferring high-level human activities in a home setting.