Recognizing Home Activities with IMS

Today I am in Pittsburgh for Ubicomp 2012, presenting our work in recognizing activities in the home using infrastructure-mediated sensing:

Recognizing Water-Based Activities in the Home Through Infrastructure-Mediated Sensing

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. How- ever, building a practical home activity monitoring system remains a challenge. Striking a balance between cost, pri- vacy, 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 ap- proach 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 activi- ties in the kitchen and bathroom, such as cooking and shav- ing. Results from two studies show that our system can es- timate activities with overall accuracy of 82.69% for one in- dividual 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.

Authors
Edison Thomaz, Vinay Bettadapura, Gabriel Reyes, Megha Sandesh, Grant Schindler, Thomas Plo ╠łtz, Gregory D. Abowd, Irfan Essa

Download the full paper and leave your questions, comments and suggestions here.



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