ABSTRACT
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. Importantly, the TechArm is suitable to be used by very young children, when the rehabilitative potential is maximal. In the present work, we validated the TechArm system on a pediatric population of low-vision, blind, and sighted children. In particular, four TechArm units were used to deliver uni- (audio or tactile) or multi-sensory stimulation (audio-tactile) on the participant's arm, and subject was asked to evaluate the number of active units. Results showed no significant difference among groups (normal or impaired vision). Overall, we observed the best performance in tactile condition, while auditory accuracy was around chance level. Also, we found that the audio-tactile condition is better than the audio condition alone, suggesting that multisensory stimulation is beneficial when perceptual accuracy and precision are low. Interestingly, we observed that for low-vision children the accuracy in audio condition improved proportionally to the severity of the visual impairment. Our findings confirmed the TechArm system's effectiveness in assessing perceptual competencies in sighted and visually impaired children, and its potential to be used to develop personalized rehabilitation programs for people with visual and sensory impairments.
ABSTRACT
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. These findings are of essential importance, as they suggest that we live in a time that may potentially affect the development of social and emotion reasoning, and young children's future social abilities should be monitored to assess the true impact of the use of masks.
ABSTRACT
Robotic systems offer the possibility of improving the life quality of people with severe motor disabilities, enhancing the individual's degree of independence and interaction with the external environment. In this direction, the operator's residual functions must be exploited for the control of the robot movements and the underlying dynamic interaction through intuitive and effective human-robot interfaces. Towards this end, this work aims at exploring the potential of a novel Soft Brain-Machine Interface (BMI), suitable for dynamic execution of remote manipulation tasks for a wide range of patients. The interface is composed of an eye-tracking system, for an intuitive and reliable control of a robotic arm system's trajectories, and a Brain-Computer Interface (BCI) unit, for the control of the robot Cartesian stiffness, which determines the interaction forces between the robot and environment. The latter control is achieved by estimating in real-time a unidimensional index from user's electroencephalographic (EEG) signals, which provides the probability of a neutral or active state. This estimated state is then translated into a stiffness value for the robotic arm, allowing a reliable modulation of the robot's impedance. A preliminary evaluation of this hybrid interface concept provided evidence on the effective execution of tasks with dynamic uncertainties, demonstrating the great potential of this control method in BMI applications for self-service and clinical care.
