@inproceedings{563d1cbf3f9848cbbad475798c889786,
title = "Accuracy and timeliness in ML based activity recognition",
abstract = "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.",
keywords = "Machine Learning, activity recognition, Hidden Markov Model, Bayesian, Support Vector Machine, sensor histories, interleaved activities",
author = "Ross, {Robert J.} and John Kelleher",
year = "2013",
doi = "10.21427/tgp7-v241",
language = "English",
isbn = "9781577356240",
series = "AAAI Workshop - Technical Report",
publisher = "AI Access Foundation",
pages = "39--46",
booktitle = "Plan, Activity, and Intent Recognition - Papers from the 2013 AAAI Workshop, Technical Report",
note = "2013 AAAI Workshop ; Conference date: 15-07-2013 Through 15-07-2013",
}