Accession Number:

ADA440281

Title:

Learning Hierarchical Models of Activity

Descriptive Note:

Corporate Author:

MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE

Report Date:

2005-01-01

Pagination or Media Count:

7.0

Abstract:

This paper investigates learning hierarchical statistical activity models in indoor environments. The Abstract Hidden Markov Model AHMM is used to represent behaviors in stochastic environments. We train the model using both labeled and unlabeled data and estimate the parameters using Expectation Maximization EM. Results are shown on three datasets data collected in lab, entryway, and home environments. The results show that hierarchical models outperform flat models.

Subject Categories:

  • Information Science
  • Psychology
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE