In this paper a Stress Detection System based on Machine Learning Algorithms (MLAs), keyboard and mouse data is presented. The development of this system is composed by three steps. Firstly, each user performs some tasks while a web application framework collects data from keyboard and mouse. At the end of each task, he/she communicates the stress level in order to create the stress class. Secondly, from collected data, features extraction and features selection procedures through a Neighborhood Component Analysis (NCA) are implemented. Lastly, three MLAs, trained with features as input and stress classes as output, are implemented to detect stress.
A stress detection system based on multimedia input peripherals / Ciabattoni, L.; Foresi, G.; Lamberti, F.; Monteriu, A.; Sabatelli, A.. - ELETTRONICO. - 2020-:(2020), pp. 1-2. (Intervento presentato al convegno 2020 IEEE International Conference on Consumer Electronics, ICCE 2020 tenutosi a usa nel 2020) [10.1109/ICCE46568.2020.9042990].
A stress detection system based on multimedia input peripherals
Ciabattoni L.;Foresi G.;Monteriu A.;Sabatelli A.
2020-01-01
Abstract
In this paper a Stress Detection System based on Machine Learning Algorithms (MLAs), keyboard and mouse data is presented. The development of this system is composed by three steps. Firstly, each user performs some tasks while a web application framework collects data from keyboard and mouse. At the end of each task, he/she communicates the stress level in order to create the stress class. Secondly, from collected data, features extraction and features selection procedures through a Neighborhood Component Analysis (NCA) are implemented. Lastly, three MLAs, trained with features as input and stress classes as output, are implemented to detect stress.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
