Document Type

Conference Paper


This item is available under a Creative Commons License for non-commercial use only


Computer Sciences, Information Science

Publication Details

In Proceedings of the AAAI Workshop on Plan, Activity, and Intent Recognition (PAIR). Association for the Advancement of Artificial Intelligence, 2013


While recent Machine Learning (ML) based techniques for activity recognition show great promise, there remain a number of questions with respect to the relative merits of these techniques. To provide a better understanding of the relative strengths of contemporary Activity Recognition methods, in this paper we present a comparative analysis of Hidden Markov Model, Bayesian, and Support Vector Machine based human activity recognition models. The study builds on both pre-existing and newly annotated data which includes interleaved activities. Results demonstrate that while Support Vector Machine based techniques perform well for all data sets considered, simple representations of sensor histories regularly outperform more complex count based models.