Activity classification at a higher level: What to do after the classifier does its best?


Conference Paper

© Copyright 2015 ACM. Research in activity classification has focused on the sensors, the classification techniques and the machine learning algorithms used in the classifier. In this work, we study a higher level of activity classification. We present two methods that can take the final observations of a classifier and improve them. The first method uses hidden Markov models to define a probabilistic model that can be used to improve classification accuracy. The second method is a novel method that we developed that uses probabilistic models along with matching costs in order to improve accuracy. Testing showed that both proposed methods presented a significant increase in classification accuracy rates, while also proving that they can both run in real time.

Full Text

Duke Authors

Cited Authors

  • Younes, R; Martin, TL; Jones, M

Published Date

  • September 7, 2015

Published In

  • Iswc 2015 Proceedings of the 2015 Acm International Symposium on Wearable Computers

Start / End Page

  • 83 - 86

International Standard Book Number 13 (ISBN-13)

  • 9781450335782

Digital Object Identifier (DOI)

  • 10.1145/2802083.2808405

Citation Source

  • Scopus