@article {seiter2014rehabilitation,
	title = {Daily Life Activity Routine Discovery in Hemiparetic Rehabilitation Patients Using Topic Models},
	journal = {Methods of Information in Medicine},
	year = {In Press},
	issn = {1574-1192},
	author = {Julia Seiter and Adrian Derungs and Corina Schuster-Amft and Oliver Amft and Gerhard Tr{\"o}ster}
}
@article {seiter2014discovery,
	title = {Discovery of activity composites using topic models: An analysis of unsupervised methods},
	journal = {Pervasive and Mobile Computing},
	volume = {15},
	year = {2014},
	note = {Special Issue on Data Mining in Pervasive Environments},
	pages = {215 - 227},
	abstract = {Abstract In this work we investigate unsupervised activity discovery approaches using three topic model~(TM) approaches, based on Latent Dirichlet Allocation~(LDA), n -gram TM~(NTM), and correlated TM~(CTM). While \{LDA\} structures activity primitives, \{NTM\} adds primitive sequence information, and \{CTM\} exploits co-occurring topics. We use an activity composite/primitive abstraction and analyze three public datasets with different properties that affect the discovery, including primitive rate, activity composite specificity, primitive sequence similarity, and composite-instance ratio. We compare the activity composite discovery performance among the \{TM\} approaches and against a baseline using k -means clustering. We provide guidelines for method and optimal \{TM\} parameter selection, depending on data properties and activity primitive noise. Results indicate that \{TMs\} can outperform k -means clustering up to 17\%, when composite specificity is low. LDA-based \{TMs\} showed higher robustness against noise compared to other \{TMs\} and k -means.},
	keywords = {Activity routines},
	issn = {1574-1192},
	doi = {http://dx.doi.org/10.1016/j.pmcj.2014.05.007},
	url = {http://www.sciencedirect.com/science/article/pii/S1574119214000832},
	author = {Julia Seiter and Oliver Amft and Mirco Rossi and Gerhard Tr{\"o}ster}
}
@article {200,
	title = {Smart Textiles: From Niche to Mainstream},
	journal = {IEEE Pervasive Computing},
	volume = {12},
	year = {2013},
	month = {July-Sept},
	pages = {81{\textendash}84},
	abstract = {<p>Current technology supports only special-purpose, low-volume textiles, garments, and electronics. Moreover, the textile, electronic, and software industries have different product cycles, cultures, and price models, creating scores of practical problems for smart textiles. Mass producing smart cloth will require decoupling the textile production from concrete sensing apps and moving the complexity to generic electronics and software\&ndash;creating wearable sensing as an app.</p>
},
	doi = {10.1109/MPRV.2013.55},
	author = {Jingyuan Cheng and Paul Lukowicz and Niels Henze and Albrecht Schmidt and Oliver Amft and Giovanni A. Salvatore and Gerhard Tr{\"o}ster}
}
