Real world activity recognition with multiple goals

  • Authors:

  • Derek Hao Hu;Sinno Jialin Pan;Vincent Wenchen Zheng;Nathan Nan Liu;Qiang Yang

  • Affiliations:

  • Hong Kong University of Science and Technology;Hong Kong University of Science and Technology;Hong Kong University of Science and Technology;Hong Kong University of Science and Technology;Hong Kong University of Science and Technology

  • Venue:
  • UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
  • Year:
  • 2008

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Abstract

Recognizing and understanding the activities of people from sensor readings is an important task in ubiquitous computing. Activity recognition is also a particularly difficult task because of the inherent uncertainty and complexity of the data collected by the sensors. Many researchers have tackled this problem in an overly simplistic setting by assuming that users often carry out single activities one at a time or multiple activities consecutively, one after another. However, so far there has been no formal exploration on the degree in which humans perform concurrent or interleaving activities, and no thorough study on how to detect multiple goals in a real world scenario. In this article, we ask the fundamental questions of whether users often carry out multiple concurrent and interleaving activities or single activities in their daily life, and if so, whether such complex behavior can be detected accurately using sensors. We define several classes of complexity levels under a goal taxonomy that describe different granularities of activities, and relate the recognition accuracy with different complexity levels or granularities. We present a theoretical framework for recognizing multiple concurrent and interleaving activities, and evaluate the framework in several real-world ubiquitous computing environments.