ASP projects

EMoCy – Physiological Signals-Based Stress Detection in Embedded Scenarios

Stress is a psycho-physical condition that has long term social and economic impacts, both on people and on the healthcare system. Continuous stress monitoring can play a key role in improving the quality of life. On one hand, it helps to prevent diseases and enhance efficiency in workplace scenarios, in life-saving jobs and desk-jobs, and in universities and schools.
And at the same time stress monitoring can be considered as an index of the engagement level among people, in entertainment activities.
EMoCy was born through the collaboration of Alta Scuola Politecnica and the NECSTLab, with the vision to improve people’s life through innovation.
EMoCy is a small portable device for automatic and continuous stress detection according to how your heart rate is changing, to the regularity of your breath and to the level of sweating.
The first goal is to develop a machine learning model for stress detection based on physiological signals. We reach an accuracy of 97.2% on the stress/not stress discrimination task. On the other hand, we aim to design a prototype of a portable device (PoC) exploiting commercial sensors, which can be easily replicated and used to collect a comprehensive unbiased dataset. For the dataset acquisition we have developed, supported by a psychologist, an experimental protocol based on the standards in the stress induction field.

Marco Domenico Santambrogio, Deib, Politecnico Di Milano

Alba Cappellieri, Design Department, Politecnico Di Milano
Sara Vinco, Dauin, Politecnico Di Torino
Riccardo Barbieri, Deib, Politecnico Di Milano
Eleonora D’arnese, Deib, Politecnico Di Milano


Ivo Boniolo, E-Novia
Federico Moro, E-Novia

Lorenzo Gecchelin, Design & Engineering, Politecnico di Milano
Noemi Gozzi, Biomedical Engineering, Politecnico di Milano
Armando Bellante, Computer Science & Engineering, Politecnico di Milano
Anisia Lauditi, Biomedical Engineering, Politecnico di Milano
Letizia Bergamasco, ICT for Smart Societies, Politecnico di Torino
Ana Bogdanović, Biomedical Engineering, Politecnico di Milano
Moaad Khamlich, Computational Engineering, Politecnico di Milano