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.