@ARTICLE{10.21494/ISTE.OP.2025.1328, 

TITLE={Automatic classification of emotions using motion sensors and keystroke dynamics on smartphones}, 
  
AUTHOR={Nicolas Simonazzi
, Jean-Marc Salotti
, Caroline Dubois
, Philippe Le Goff, }, 
	
JOURNAL={Cognitive Engineering}, 
	
VOLUME={8}, 

NUMBER={Issue 1}, 
	
YEAR={2025}, 

URL={https://www.openscience.fr/Automatic-classification-of-emotions-using-motion-sensors-and-keystroke}, 
	
DOI={10.21494/ISTE.OP.2025.1328}, 
	
ISSN={2517-6978}, 
	
ABSTRACT={We present the results of a study on a binary classification of emotions, based on data collected through motion sensors and keystrokes of a smartphone and a connected bracelet. To this end, we developed a mobile application to induce emotions through videos and record user interactions. A specific digital self-assessment system was developed based on the Geneva Emotion Wheel to help participants express their emotions. The sensor recordings were labelled according to participants’ statements and video conditions. A method is thus proposed to process the collected temporal data and automatically classify the valence of the declared emotions using machine learning techniques. We tested a general valence classification using all emotions from all individuals and a personalized classification using a subset of emotions from a single individual. The most promising result was obtained with a personalized model, for which we were able to obtain, on average across all participants, two-thirds of correct valence classification, using fused data from different modalities.}}