Object recognition and localization enhancement in visual prostheses: a real-time mixed reality simulation.

Blindness is a main threat that affects the daily life activities of any human. Visual prostheses have been introduced to provide artificial vision to the blind with the aim of allowing them to restore confidence and independence. In this article, we propose an approach that involves four image enhancement techniques to facilitate object recognition and localization for visual prostheses users.

PVGAN: a generative adversarial network for object simplification in prosthetic vision.

By means of electrical stimulation of the visual system, visual prostheses provide promising solution for blind patients through partial restoration of their vision. Despite the great success achieved so far in this field, the limited resolution of the perceived vision using these devices hinders the ability of visual prostheses users to correctly recognize viewed objects. Accordingly, we propose a deep learning approach based on generative adversarial networks (GANs), termed prosthetic vision GAN (PVGAN), to enhance object recognition for the implanted patients by representing objects in the field of view based on a corresponding simplified clip art version.

Electrode Dropout Compensation in Visual Prostheses: An Optimal Object Placement Approach.

Visual prostheses provide promising solution to the blind through partial restoration of their vision via electrical stimulation of the visual system. However, there are some challenges that hinder the ability of subjects implanted with visual prostheses to correctly identify an object. One of these challenges is electrode dropout; the malfunction of some electrodes resulting in consistently dark phosphenes.

An Ontology-Based Framework for Psychological Monitoring in Education During the COVID-19 Pandemic.

Background: Especially in the current crisis of the COVID-19 pandemic and the lockdown it entailed, technology became crucial. Machines need to be able to interpret and represent human behavior, to improve human interaction with technology. This holds for all domains but even more so for the domain of student behavior in relation to education and psychological well-being.

How do you feel during the COVID-19 pandemic? A survey using psychological and linguistic self-report measures, and machine learning to investigate mental health, subjective experience, personality, and behaviour during the COVID-19 pandemic among university students.

Background: The WHO has raised concerns about the psychological consequences of the current COVID-19 pandemic, negatively affecting health across societies, cultures and age-groups.

Methods: This online survey study investigated mental health, subjective experience, and behaviour (health, learning/teaching) among university students studying in Egypt or Germany shortly after the first pandemic lockdown in May 2020. Psychological assessment included stable personality traits, self-concept and state-like psychological variables related to (a) mental health (depression, anxiety), (b) pandemic threat perception (feelings during the pandemic, perceived difficulties in describing, identifying, expressing emotions), (c) health (e.