Determining useful sensors for automatic recognition of activities of daily living in health smart home

François Portet 1 Anthony Fleury 2 Michel Vacher 1 Norbert Noury 2
2 AFIRM
TIMC-IMAG - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications [Grenoble]
Abstract : To face the rapid growth of the world elderly population, health smart homes with sensing technology are emerging to automatically detect early loss of autonomy using objective criterion such as the Activity of Daily Living grid. The paper presents data mining techniques to classify 7 seven activities in a health smart home using only the most relevant attributes. The evaluation has shown that a correct classification of 84.5% can be reached with a dataset reduced to 16% related to less than 34% of the current sensors. Results also showed the importance of microphones as complementary data source.
Type de document :
Communication dans un congrès
Intelligent Data International Workshop on Analysis in Medicine and Pharmacology (IDAMAP2009), Jul 2009, Verona, Italy. pp.63-64, 2009
Liste complète des métadonnées

https://hal.inria.fr/hal-00953578
Contributeur : Michel Vacher <>
Soumis le : vendredi 28 février 2014 - 13:42:53
Dernière modification le : samedi 20 janvier 2018 - 01:16:23

Identifiants

  • HAL Id : hal-00953578, version 1

Citation

François Portet, Anthony Fleury, Michel Vacher, Norbert Noury. Determining useful sensors for automatic recognition of activities of daily living in health smart home. Intelligent Data International Workshop on Analysis in Medicine and Pharmacology (IDAMAP2009), Jul 2009, Verona, Italy. pp.63-64, 2009. 〈hal-00953578〉

Partager

Métriques

Consultations de la notice

186