Combining multiple hypotheses for identifying human activities

Young-Woo Seo and Katia Sycara
tech. report CMU-RI-TR-06-31, Robotics Institute, Carnegie Mellon University, May, 2006


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Abstract
Dempster-Shafer theory is one of the predominant methods for combining evidence from different sensors. However, it has been observed that Dempster's rule of combination may yield inaccurate results in some situations. In this paper, we examine the properties and the performance of five different combination rules on a set of real world data. The data was obtained through biometric sensors from a number of human subjects. The problem we study is the prediction of the activity state of a human, given time series readings from the biometric sensors.

Keywords
information fusion, human activity recognition

Notes
Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems
Associated Lab(s) / Group(s): Advanced Agent - Robotics Technology Lab
Number of pages: 17

Text Reference
Young-Woo Seo and Katia Sycara, "Combining multiple hypotheses for identifying human activities," tech. report CMU-RI-TR-06-31, Robotics Institute, Carnegie Mellon University, May, 2006

BibTeX Reference
@techreport{Seo_2006_5448,
   author = "Young-Woo Seo and Katia Sycara",
   title = "Combining multiple hypotheses for identifying human activities",
   booktitle = "",
   institution = "Robotics Institute",
   month = "May",
   year = "2006",
   number= "CMU-RI-TR-06-31",
   address= "Pittsburgh, PA",
}