Classifier for Activities with Variations.


Journal Article

Most activity classifiers focus on recognizing application-specific activities that are mostly performed in a scripted manner, where there is very little room for variation within the activity. These classifiers are mainly good at recognizing short scripted activities that are performed in a specific way. In reality, especially when considering daily activities, humans perform complex activities in a variety of ways. In this work, we aim to make activity recognition more practical by proposing a novel approach to recognize complex heterogeneous activities that could be performed in a wide variety of ways. We collect data from 15 subjects performing eight complex activities and test our approach while analyzing it from different aspects. The results show the validity of our approach. They also show how it performs better than the state-of-the-art approaches that tried to recognize the same activities in a more controlled environment.

Full Text

Duke Authors

Cited Authors

  • Younes, R; Jones, M; Martin, TL

Published Date

  • October 18, 2018

Published In

Volume / Issue

  • 18 / 10

PubMed ID

  • 30340436

Pubmed Central ID

  • 30340436

Electronic International Standard Serial Number (EISSN)

  • 1424-8220

International Standard Serial Number (ISSN)

  • 1424-8220

Digital Object Identifier (DOI)

  • 10.3390/s18103529


  • eng