In factories of the future, advanced automation systems (e.g., cobots, exoskeletons, cyber physical systems) will reduce the physical effort of workers and compensate their limitations as well as ensure more flexibility, agility, and competitiveness. However, the activities of the operator 4.0 will entail an increased share of complex cognitive tasks. Therefore, monitoring the mental load will be increasingly important to ensure work environments that promote healthy life and wellbeing for all at all ages. For this aim, this paper proposes a framework to analyze heart rate, galvanic skin response and electrooculogram signals in order to extract features able to detect an excessive stress or cognitive load. Two wearable devices are used: Empatica E4 wristband and J!NS MEME electrooculography glasses. The proposed framework has been experimented through a laboratory test focused on LEGO brick-based simulations of manufacturing activities.
Multi sensors platform for stress monitoring of workers in smart manufacturing context / Leone, A.; Rescio, G.; Siciliano, P.; Papetti, A.; Brunzini, A.; Germani, M.. - (2020), pp. 1-5. (Intervento presentato al convegno 2020 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2020 tenutosi a Dubrovnik; Croatia nel 2020) [10.1109/I2MTC43012.2020.9129288].
Multi sensors platform for stress monitoring of workers in smart manufacturing context
Papetti A.;Brunzini A.;Germani M.
2020-01-01
Abstract
In factories of the future, advanced automation systems (e.g., cobots, exoskeletons, cyber physical systems) will reduce the physical effort of workers and compensate their limitations as well as ensure more flexibility, agility, and competitiveness. However, the activities of the operator 4.0 will entail an increased share of complex cognitive tasks. Therefore, monitoring the mental load will be increasingly important to ensure work environments that promote healthy life and wellbeing for all at all ages. For this aim, this paper proposes a framework to analyze heart rate, galvanic skin response and electrooculogram signals in order to extract features able to detect an excessive stress or cognitive load. Two wearable devices are used: Empatica E4 wristband and J!NS MEME electrooculography glasses. The proposed framework has been experimented through a laboratory test focused on LEGO brick-based simulations of manufacturing activities.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
