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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. These techniques are clip art representation of the objects, edge sharpening, corner enhancement and electrode dropout handling. The proposed techniques are tested in a real-time mixed reality simulation environment that mimics vision perceived by visual prostheses users. Twelve experiments were conducted to measure the performance of the participants in object recognition and localization. The experiments involved single objects, multiple objects and navigation. To evaluate the performance of the participants in objects recognition, we measure their recognition time, recognition accuracy and confidence level. For object localization, two metrics were used to measure the performance of the participants which are the grasping attempt time and the grasping accuracy. The results demonstrate that using all enhancement techniques simultaneously gives higher accuracy, higher confidence level and less time for recognizing and grasping objects in comparison to not applying the enhancement techniques or applying pair-wise combinations of them. Visual prostheses could benefit from the proposed approach to provide users with an enhanced perception.
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Realidad Aumentada , Prótesis Visuales , Humanos , Percepción Visual , Visión Ocular , Reconocimiento en PsicologíaRESUMEN
Objective.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.Approach.To assess the performance, an axon map model was used to simulate prosthetic vision in experiments involving normally-sighted participants. In these experiments, four types of image representation were examined. The first and second types comprised presenting phosphene simulation of real images containing the actual high-resolution object, and presenting phosphene simulation of the real image followed by the clip art image, respectively. The other two types were utilized to evaluate the performance in the case of electrode dropout, where the third type comprised presenting phosphene simulation of only clip art images without electrode dropout, while the fourth type involved clip art images with electrode dropout.Main results.The performance was measured through three evaluation metrics which are the accuracy of the participants in recognizing the objects, the time taken by the participants to correctly recognize the object, and the confidence level of the participants in the recognition process. Results demonstrate that representing the objects using clip art images generated by the PVGAN model results in a significant enhancement in the speed and confidence of the subjects in recognizing the objects.Significance.These results demonstrate the utility of using GANs in enhancing the quality of images perceived using prosthetic vision.
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Fosfenos , Prótesis Visuales , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Reconocimiento en Psicología , Trastornos de la Visión , Visión Ocular , Percepción Visual/fisiologíaRESUMEN
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. In this paper, we propose a dropout handling algorithm for better and faster identification of objects. In this algorithm, phosphenes representing the object are translated to another location within the same image that has the minimum number of dropouts. Using simulated prosthetic vision, experiments were conducted to test the efficacy of our proposed algorithm. Electrode dropout rates of 10%, 20% and 30% were examined. Our results demonstrate significant increase in the object recognition accuracy, reduction in the recognition time and increase in the recognition confidence level using the proposed approach compared to presenting the images without dropout handling.Clinical Relevance- These results demonstrate the utility of dropout handling in enhancing the perception of images in prosthetic vision.
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Prótesis Visuales , Electrodos , Humanos , Fosfenos , Visión Ocular , Percepción VisualRESUMEN
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. METHODS: This work presents the theoretical framework of a psychologically driven computing ontology, CCOnto, describing situation-based human behavior in relation to psychological states and traits. In this manuscript, we use and apply CCOnto as a theoretical and formal description system to categorize psychological factors that influence student behavior during the COVID-19 situation. By doing so, we show the added value of ontologies, i.e., their ability to automatically organize information from unstructured human data by identifying and categorizing relevant psychological concepts. RESULTS: The already existing CCOnto was modified to automatically categorize university students' state and trait markers related to different aspects of student behavior, including learning, worrying, health, and socially based on psychological theorizing and psychological data conceptualization. DISCUSSION: The paper discusses the potential advantages of using ontologies for describing and modeling psychological research questions. The handling of dataset completion, unification, and its explanation by means of Artificial Intelligence and Machine Learning models is also discussed.
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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.g., worries about health, bodily symptoms) and behaviour including perceived difficulties in learning. Assessment methods comprised self-report questions, standardized psychological scales, psychological questionnaires, and linguistic self-report measures. Data analysis comprised descriptive analysis of mental health, linguistic analysis of self-concept, personality and feelings, as well as correlational analysis and machine learning. N = 220 (107 women, 112 men, 1 = other) studying in Egypt or Germany provided answers to all psychological questionnaires and survey items. RESULTS: Mean state and trait anxiety scores were significantly above the cut off scores that distinguish between high versus low anxious subjects. Depressive symptoms were reported by 51.82% of the student sample, the mean score was significantly above the screening cut off score for risk of depression. Worries about health (mental and physical health) and perceived difficulties in identifying feelings, and difficulties in learning behaviour relative to before the pandemic were also significant. No negative self-concept was found in the linguistic descriptions of the participants, whereas linguistic descriptions of feelings during the pandemic revealed a negativity bias in emotion perception. Machine learning (exploratory) predicted personality from the self-report data suggesting relations between personality and subjective experience that were not captured by descriptive or correlative data analytics alone. CONCLUSION: Despite small sample sizes, this multimethod survey provides important insight into mental health of university students studying in Egypt or Germany and how they perceived the first COVID-19 pandemic lockdown in May 2020. The results should be continued with larger samples to help develop psychological interventions that support university students across countries and cultures to stay psychologically resilient during the pandemic.