The ongoing demographic transition, characterized by a projected rise in the old-age dependency ratio from 28% to 50% by the year 2060, indicates that age-related illnesses will provide a significant issue in the future. In this perspective, developing technological solutions able to support older individuals and people with special needs in their autonomous mobility could be crucial. However, when delivering such solutions, it is fundamental to monitor the affective state of the users to observe their acceptance of the technology. The approach proposed in this study integrates a smart wheelchair with a physiological computational module, composed of co-registered RGB and Infrared Cameras, with integrated artificial intelligence algorithms for affective computing, delivering a classification of the stress and engagement condition of the user. This study aims to showcase the technical viability of such an approach, monitoring and comparing the stress and engagement states of individuals during autonomous and manual smart wheelchair navigation. The results did not deliver significant differences in stress and engagement condition between the two driving modalities, demonstrating the acceptability of the proposed framework.
Thermal Imaging for Real-Time Monitoring of Stress and Engagement in Autonomous and Manual Smart Wheelchair Navigation
Perpetuini D.Primo
;Cardone D.;Merla A.;
2024-01-01
Abstract
The ongoing demographic transition, characterized by a projected rise in the old-age dependency ratio from 28% to 50% by the year 2060, indicates that age-related illnesses will provide a significant issue in the future. In this perspective, developing technological solutions able to support older individuals and people with special needs in their autonomous mobility could be crucial. However, when delivering such solutions, it is fundamental to monitor the affective state of the users to observe their acceptance of the technology. The approach proposed in this study integrates a smart wheelchair with a physiological computational module, composed of co-registered RGB and Infrared Cameras, with integrated artificial intelligence algorithms for affective computing, delivering a classification of the stress and engagement condition of the user. This study aims to showcase the technical viability of such an approach, monitoring and comparing the stress and engagement states of individuals during autonomous and manual smart wheelchair navigation. The results did not deliver significant differences in stress and engagement condition between the two driving modalities, demonstrating the acceptability of the proposed framework.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
