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Abstract Action video-games (AVGs) could improve reading efficiency, enhancing not only visual attention but also phonological processing. Here we tested the AVG effects upon three consolidated language-based predictors of reading development in a sample of 79 pre-readers at-risk and 41 non-at-risk for developmental dyslexia. At-risk children were impaired in either phonemic awareness (i.e., phoneme discrimination task), phonological working memory (i.e., pseudoword repetition task) or rapid automatized naming (i.e., RAN of colours task). At-risk children were assigned to different groups by using an unequal allocation randomization: (1) AVG (n = 43), (2) Serious Non-Action Video Game (n = 11), (3) treatment-as-usual (i.e., speech therapy, n = 11), and (4) waiting list (n = 14). Pre- and post-training comparisons show that only phonemic awareness has a significantly higher improvement in the AVG group compared to the waiting list, the non-AVG, and the treatment-as-usual groups, as well as the combined active groups (n = 22). This cross-modal plastic change: (i) leads to a recovery in phonemic awareness when compared to the not-at-risk pre-readers; (ii) is present in more than 80% of AVG at-risk pre-readers, and; (iii) is maintained at a 6-months follow-up. The present findings indicate that this specific multisensory attentional training positively affects how phonemic awareness develops in pre-readers at risk for developmental dyslexia, paving the way for innovative prevention programs.

This paper presents a new tool to help people with visual impairments learn mathematical functions through the extensive use of sounds and touch. STEM subjects like mathematics are difficult for this kind of user, since concepts like lines or curves heavily depend on visual content to convey essential information. GraficiAccessibili is a mobile app that allows to draw lines and parabolas in the cartesian plane, incorporating sound and haptic feedback to make them perceivable by all users. Mathematical functions are sonified enabling users to discern the position of their points on the quarters of the cartesian plane through sound cues. Furthermore, the application assists users in exploring the cartesian plane through touch giving haptic feedback for curves and axes. In this way, the user can mentally visualize the graphical representation of the function. Tests were conducted with real users to assess the effectiveness of this approach.

This topical collection focuses on applying sensors and communications technologies for social good [...]

Stress condition, if experienced for an extended amount of time, can negatively affect individual's health. Several external sensors monitoring different physiological states correlated with stress, or smartphone apps that monitor individuals context, have been leveraged to assess stress state in everyday life. The less intrusive ''human-smartphone interaction'' have been under-investigated so far. In our research we leverage 'swipe', 'scroll' and 'text input' interactions to assess the stress state of smartphone users. Based on data collected from 13 participants, we leverage 'swipe' and 'scroll' data to assess stress with an average F-measure of 79-85% for a within-subject model, and of 70-80% when building a global model. Moreover, 'text input' via a virtual keyboard has been analyzed, showing how several easy to calculate features enable to differentiate between stress and no-stress state. To the best of our knowledge, this is the first attempt to leverage human-smartphone interaction, and in particular 'swipe', 'scroll' and 'text input' interactions, to accurately assess stress state in individuals without using any external sensor or leveraging privacy-sensitive context information.