CMU report: Combining multiple hypotheses for identifying human activities

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

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. (pdf)