No abstract

This study demonstrates that appropriate measurement procedures can detect differences in head movement in a near reading task when using three different progressive addition lenses (PALs). The movements were measured using an anatomical reference system with a biomechanical rationale. This reference system was capable of representing rotations for comparing head flexion relative to trunk, head flexion relative to neck, head rotation relative to trunk and trunk flexion. The subject sample comprised 31 volunteers and three PAL designs with different viewing zones were selected. Significant differences were found between the lenses for three of the seven movement parameters examined. The differences occurred for both vertical and horizontal head movements and could be attributed to aspects of the PAL design. The measurement of the complete kinematic trunk-neck-head chain improved the number of differences that were found over those in previous studies. STATEMENT OF RELEVANCE: The study proposes a methodology based on a biomechanical rationale able to differentiate head-neck-trunk posture and movements caused by different progressive addition lens designs with minimum invasiveness. This methodology could also be applied to analyse the ergonomics of other devices that restrict the user's field of view, such as helmets, personal protective equipment or helmet-mounted displays for pilots. This analysis will allow designers to optimise designs offering higher comfort and performance.

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Emotion recognition is crucial to increase user acceptance in autonomous driving. SUaaVE project aims to formulate ALFRED, defined as the human-centered artificial intelligence to humanize the vehicle actions by estimating the emotions felt by the passengers and managing preventive or corrective actions, providing tailored support. This paper presents the development of an emotional model able to estimate the values of valence (how negative or positive a stimulus is) and arousal (the level of excitement) from the analysis of physiological signals. The model has been validated with an experimental test simulating different driving scenarios of autonomous vehicles. The results found that driving mode can influence the emotional state felt by the passengers. Further exploration of this emotional model is therefore advised to detect on board experiences and to lead to new applications in the framework of empathic vehicles.