Reducing user intervention in incremental activityrecognition for assistive technologies

Rebetez, Julien (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Satizabal, Hector F. (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Perez-Uribe, Andres (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland)

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%.


Conference Type:
short paper
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
Publisher:
Zurich, Switzerland, 9-12 September 2013
Date:
2013-09
Zurich, Switzerland
9-12 September 2013
Pagination:
pp. 29-32
Published in:
Proceedings of the 2013 International Symposium on Wearable Computers, 9-12 September 2013, Zurich, Switzerland
DOI:
ISBN:
9781450321273
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2020-09-22, last modified 2020-10-27

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