@ARTICLE{10.21494/ISTE.OP.2017.0137, 

TITLE={The transitions of smartphone positions what consequences on the recognition of the human activities?}, 
  
AUTHOR={Hamdi Amroun, Nizar Ouarti, M’Hamed (Hamy) Temkit, Mehdi Ammi, }, 
	
JOURNAL={Internet of Things}, 
	
VOLUME={1}, 

NUMBER={Issue 1}, 
	
YEAR={2017}, 

URL={https://openscience.fr/The-transitions-of-smartphone-positions-what-consequences-on-the-recognition-of}, 
	
DOI={10.21494/ISTE.OP.2017.0137}, 
	
ISSN={2514-8273}, 
	
ABSTRACT={This paper presents an experiment that shows the impact of the transitions of smartphone positions (from one position to another on the human body) on the recognition of the human activity, in an uncontrolled environment. The studied activities are walking, laying, standing and sitting. In this environment, users are free to switch smartphone position, for instance from hand to pocket. Our methodology includes the combination of different sensors and a classification with a Deep Neural Network algorithm. Two datasets were considered, the only difference between the two datasets is that the transition are removed in the second dataset. Results show an improvement of accuracy of the classification of activities 93.87% for the control dataset compare to 95.07% for the dataset with removed transitions. We also show that with no transitions the convergence is quicker and more robust. Consequently, our method including removing information corresponding to transitions should save memory space and computing time while offering a high classification precision.}}