Vitor Fortes, Paul Lukowicz


Current activity recognition systems mostly work with static, pre-trained sensor configuration. As a consequence they are not able to leverage new sensors appearing in their environment (e.g. the user buying a new wearable devices). In this work we present a method inspired by semi-supervised graph methods that can add new sensors to an existing system in an unsupervised manner. We have evaluated our method in two well known activity recognition datasets and found that it can take advantage of the information provided by new unknown sensor sources, improving the recognition performance in most cases.   [Download]


@article {Fortes:Label:2017:9494,
	number = {3}, 
	month = {}, 
	year = {2017}, 
	title = {Label Propagation: An Unsupervised Similarity Based Method for Integrating New Sensors in Activity Recognition Systems}, 
	journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)}, 
	volume = {1}, 
	pages = {94:1-94:24}, 
	publisher = {ACM, New York, NY, USA}, 
	author = {Vitor Fortes, Paul Lukowicz}, 
	keywords = {}