Indoor activity recognition by combining one-vs.-all neural network classifiers exploiting wearable and depth sensors

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

Activity recognition has recently gained a lot of interest and appears to be a promising approach to help the elderly population pursue an independent living. There already exist several methods to detect human activities based either on wearable sensors or on cameras but few of them combine the two modalities. This paper presents a strategy to enhance the robustness of indoor human activity recognition by combining wearable and depth sensors. To exploit the data captured by those sensors, we used an ensemble of binary one-vs-all neural network classifiers. Each activity-specific model was configured to maximize its performance. The performance of the complete system is comparable to lazy learning methods (k-NN) that require the whole dataset.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
Publisher:
Puerto de la Cruz, Tenerife, Spain, 12-14 June 2013
Date:
2013-06
Puerto de la Cruz, Tenerife, Spain
12-14 June 2013
Pagination:
pp. 216-223
Published in:
Lecture Notes in Computer Science ; Proceedings of IWANN 2013 : 12th International Work-Conference on Artificial Neural Networks : Advances in Computational Intelligence, 12-14 June 2013, Puerto de la Cruz, Tenerife, Spain
DOI:
ISSN:
0302-9743
ISBN:
978-3-642-38681-7
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2020-09-15, last modified 2020-10-27

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