Indoor Activity Recognition by Combining One-vs.-All Neural Network Classifiers Exploiting Wearable and Depth Sensors

Autor:	Andres Perez-Uribe, Hector Fabio Satizabal Mejia, Julien Rebetez, Benoît Delachaux
Rok vydání:	2013
Předmět:	Activity recognition Lazy learning Artificial neural network Robustness (computer science) Home automation business.industry Computer science Wearable computer Artificial intelligence Machine learning computer.software_genre business computer
Zdroj:	Advances in Computational Intelligence ISBN: 9783642386817 IWANN (2)
DOI:	10.1007/978-3-642-38682-4_25
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f6f7e0c80d177889f936a31f87125106 https://doi.org/10.1007/978-3-642-38682-4_25 Zobrazit plný text záznamu