Clinical assessment of the TechArm system on visually impaired and blind children during uni- and multi-sensory perception tasks.

We developed the TechArm system as a novel technological tool intended for visual rehabilitation settings. The system is designed to provide a quantitative assessment of the stage of development of perceptual and functional skills that are normally vision-dependent, and to be integrated in customized training protocols. Indeed, the system can provide uni- and multisensory stimulation, allowing visually impaired people to train their capability of correctly interpreting non-visual cues from the environment.

Masking Emotions: Face Masks Impair How We Read Emotions.

To date, COVID-19 has spread across the world, changing our way of life and forcing us to wear face masks. This report demonstrates that face masks influence the human ability to infer emotions by observing facial configurations. Specifically, a mask obstructing a face limits the ability of people of all ages to infer emotions expressed by facial features, but the difficulties associated with the mask's use are significantly pronounced in children aged between 3 and 5 years old.

A Novel Wearable and Wireless Device to Investigate Perception in Interactive Scenarios.

The aim of the present work is to introduce a novel wearable device suitable to be used to investigate perception in interactive tasks, on individuals with and without sensory disabilities. The system is composed by small units embedded with sensors and actuators that allows emitting different kind of stimuli (light, haptic, sound) and to record the user response, thanks to a capacitive sensor. We validated the system by implementing an interception task in three different sensory modalities: visual, tactile and auditory.

The Effect of Vibrotactile Feedback on ErrP-based Adaptive Classification of Motor Imagery.

This work presents an implementation of Error-related Potential (ErrP) detection to produce progressive adaptation of a motor imagery task classifier. The main contribution is in the evaluation of the effect of vibrotactile feedback on both ErrP and motor imagery detection. Results confirm the potential of self-adaptive techniques to improve motor imagery classification, and support the design of vibratory and in general tactile feedback into Brain-Computer Interfaces to improve both static and adaptive performance.