TY  - Type of reference 
TI  - In‐Depth Learning of Raw Human Activity Data 
AU  - Hamdi Amroun 
AU  - M’Hamed (Hamy) Temkit 
AU  - Mehdi Ammi 
AB  - This paper proposes to study the recognition of certain daily physical activities by using a network of smart objects. The approach consists in the classification of certain participants’ activities, the most common ones and those that are carried out with smart objects:Make a phone call (Call), open the door (Open), close the door (Close) and watch its smartwatch (Watch). The study exploits a network of commonly connected objects: a smart watch and a smartphone, transported by participants during an uncontrolled experiment. The sensors’ data of the two devices were classified by a deep neural network (DNN) algorithm without prior data pre-processing. We show that DNN provides better results than Decision Tree (DT) and Support Vector Machine (SVM) algorithms. The results also show that some participants’ activities were classified with an accuracy of more than 98%, on average. 
DO  - 10.21494/ISTE.OP.2017.0150 
JF  - Internet of Things 
KW  - Activity recognition,  DNN,  Non controlled environnement,  IOT, Reconnaissance de l’activité,  DNN,  environnement non contrôlé,  
L1  - https://openscience.fr/IMG/pdf/iste_idov1n2_4.pdf 
LA  - en 
PB  - ISTE OpenScience 
DA  - 2017/06/19 
SN  - 2514-8273 
TT  - Apprentissage en profondeur des données brutes de l’activité humaine 
UR  - https://openscience.fr/In-Depth-Learning-of-Raw-Human-Activity-Data 
IS  - Issue 2 
VL  - 1 
ER  -