Reducing user intervention in incremental activityrecognition for assistive technologies

Autor:	Andres Perez-Uribe, Julien Rebetez, Héctor F. Satizábal
Rok vydání:	2013
Předmět:	Activity recognition Neural gas Computer science business.industry Active learning (machine learning) Intervention (counseling) A priori and a posteriori Wearable computer Artificial intelligence business Machine learning computer.software_genre computer
Zdroj:	ISWC
DOI:	10.1145/2493988.2494350
Popis:	Activity recognition has recently gained a lot of interest and there already exist several methods to detect human activites based on wearable sensors. Most of the existing methods rely on a database of labelled activities that is used to train an offline activity recognition system. This paper presents an approach to build an online activity recognition system that do not require any a priori labelled data. The system incrementally learns activities by actively querying the user for labels. To choose when the user should be queried, we compare a method based on random sampling and another that uses a Growing Neural Gas (GNG). The use of GNG helps reducing the number of user queries by 20% to 30%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::16021f1f098862e33b317388223a092d https://doi.org/10.1145/2493988.2494350 Zobrazit plný text záznamu