Individual Access Research Articles

UTILIZING CONVOLUTIONAL NEURAL NETWORKS TO COMPREHEND SIGN LANGUAGE AND RECOGNIZE EMOTIONS

The inability to communicate verbally is widely acknowledged as a significant disability. The primary objective of this research is to create a practical system aimed at individuals with hearing impairments, particularly those who depend on sign language as their primary means of communication. This system aims to enable deaf individuals to express themselves, communicate effectively and facilitate understanding of their language which would otherwise be challenging since most people are unfamiliar with sign language. By employing human gesture interpretation and motion capture, this technology can facilitate the translation of sign language into spoken language and vice versa. Despite the existence of various methods to convert sign language into voice, none of them currently provide an entirely intuitive user interface. Our objective is to create a system that not only translates sign language but also integrates a natural user interface, thus enhancing accessibility for individuals who are blind or have visual impairments. This system will achieve this by recognizing facial expressions and effectively conveying emotions behind words assisting visually impaired individuals in expressing themselves more effectively. Hence, in this project, we tentatively aim to build a system that can ease the lives of blind, deaf and dumb people to some extent. Normal communication like normal people do might never be possible for differently abled people but through our project, we try to provide them with a tool that can help them experience normalcy while communicating with normal people.

Enhancing Real-Time Cursor Control with Motor Imagery and Deep Neural Networks for Brain–Computer Interfaces

This paper advances real-time cursor control for individuals with motor impairments through a novel brain–computer interface (BCI) system based solely on motor imagery. We introduce an enhanced deep neural network (DNN) classifier integrated with a Four-Class Iterative Filtering (FCIF) technique for efficient preprocessing of neural signals. The underlying approach is the Four-Class Filter Bank Common Spatial Pattern (FCFBCSP) and it utilizes a customized filter bank for robust feature extraction, thereby significantly improving signal quality and cursor control responsiveness. Extensive testing under varied conditions demonstrates that our system achieves an average classification accuracy of 89.1% and response times of 663 milliseconds, illustrating high precision in feature discrimination. Evaluations using metrics such as Recall, Precision, and F1-Score confirm the system’s effectiveness and accuracy in practical applications, making it a valuable tool for enhancing accessibility for individuals with motor disabilities.

Multimodal Feedback Methods for Advancing the Accessibility of Immersive Virtual Reality for People With Balance Impairments Due to Multiple Sclerosis.

Maintaining balance in immersive virtual reality (VR) environments poses a significant challenge for users, particularly affecting those with pre-existing balance disorders. This study investigates the efficacy of multimodal feedback-comprising auditory, vibrotactile, and visual stimuli-in mitigating balance issues within VR. A sample of 68 participants, divided equally between individuals with balance deficits related to multiple sclerosis and those without, was evaluated. The research explored the impact of various feedback conditions on balance performance. The results demonstrated that the multimodal feedback condition significantly enhanced balance control compared to other conditions, with statistical analysis confirming this improvement (p<.001). These findings underscore the potential of integrated sensory feedback in addressing balance-related difficulties in VR, thereby improving the overall accessibility and user experience for individuals affected by balance impairments. This research contributes valuable insights into optimizing VR environments for enhanced stability and user comfort.

Escape behavior of rocky‐reef fish reveals the impact of spearfishing across different management regimes

AbstractFlight initiation distance (FID), the distance at which an organism begins to flee from an approaching threat, is a major component of antipredator escape behavior and a potential indicator of threat perception in fishes. In this study, we analyzed the FID of three important rocky‐reef fish species targeted by spearfishers, which are of commercial and recreational importance. We tested predictions that FID to a diver threat increases with the following factors: (1) fish body size, (2) less restricted access regimes, and (3) increased historical fishing pressure. We studied three size ranges of three rocky‐reef fish species, in three different access regimes (i.e., open access, territorial user rights for fishery areas, no‐take marine protected areas), and in two regions (northern and central region with different levels of fishing pressure depending on the species). We conducted an ANOVA to analyze pairwise interactions. We used the mean square criterion to select the models that best explained the variation of our response variable. Our findings indicate that FID can be distinctly elucidated by factors such as individual size, species, access regimes, and regions. Additionally, our models show that interactions involving regions and either species or size further contribute to explain FID variability. FID was higher in larger fishes and those of higher commercial value, outside marine reserves and in the region with the highest historical fishing pressures (based on landings data). This study supports the predictions that increased FID is associated with the threat posed by spearfishing activities. Furthermore, our findings indicate that spearfishing may already be altering the behavior of rocky‐reef fishes on the north‐central coast of Chile.

American Sign Language Recognition System: Enhancing Communication Accessibility for Individuals with Hearing Disabilities

American Sign Language Recognition System: Enhancing Communication Accessibility for Individuals with Hearing Disabilities

UI/UX Design of Psychology Consultation System Application Using the Design Thinking Method

Mental health is an important issue facing the global community today. Every year, millions of people are affected by a variety of mental disorders, making mental health a very pressing issue. The study highlights the need for a mobile app-based psychological consultation system, which can provide better access for individuals experiencing mental problems. Through the design and development of effective systems, this research aims to understand how these apps can help individuals overcome mental health challenges, improve their quality of life, and facilitate better interactions between mental health professionals and users. This research method follows a design thinking approach to design UI/UX for psychology consultation system applications. This approach was chosen because it can produce more innovative solutions by understanding user needs in depth. From the results of this study, it is clear that the application of a psychology consultation system designed with a user-based approach can not only increase satisfaction but also increase user engagement. These findings support the initial hypothesis that a deep understanding of user needs can lead to better and more effective app designs.

Understanding AI's Role in Endometriosis Patient Education and Evaluating Its Information and Accuracy: Systematic Review.

Endometriosis is a chronic gynecological condition that affects a significant portion of women of reproductive age, leading to debilitating symptoms such as chronic pelvic pain and infertility. Despite advancements in diagnosis and management, patient education remains a critical challenge. With the rapid growth of digital platforms, artificial intelligence (AI) has emerged as a potential tool to enhance patient education and access to information. This systematic review aims to explore the role of AI in facilitating education and improving information accessibility for individuals with endometriosis. This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to ensure rigorous and transparent reporting. We conducted a comprehensive search of PubMed; Embase; the Regional Online Information System for Scientific Journals of Latin America, the Caribbean, Spain and Portugal (LATINDEX); Latin American and Caribbean Literature in Health Sciences (LILACS); Institute of Electrical and Electronics Engineers (IEEE) Xplore, and the Cochrane Central Register of Controlled Trials using the terms "endometriosis" and "artificial intelligence." Studies were selected based on their focus on AI applications in patient education or information dissemination regarding endometriosis. We included studies that evaluated AI-driven tools for assessing patient knowledge and addressed frequently asked questions related to endometriosis. Data extraction and quality assessment were conducted independently by 2 authors, with discrepancies resolved through consensus. Out of 400 initial search results, 11 studies met the inclusion criteria and were fully reviewed. We ultimately included 3 studies, 1 of which was an abstract. The studies examined the use of AI models, such as ChatGPT (OpenAI), machine learning, and natural language processing, in providing educational resources and answering common questions about endometriosis. The findings indicated that AI tools, particularly large language models, offer accurate responses to frequently asked questions with varying degrees of sufficiency across different categories. AI's integration with social media platforms also highlights its potential to identify patients' needs and enhance information dissemination. AI holds promise in advancing patient education and information access for endometriosis, providing accurate and comprehensive answers to common queries, and facilitating a better understanding of the condition. However, challenges remain in ensuring ethical use, equitable access, and maintaining accuracy across diverse patient populations. Future research should focus on developing standardized approaches for evaluating AI's impact on patient education and exploring its integration into clinical practice to enhance support for individuals with endometriosis.

Mobile street clinics and harm reduction: pathways to provide care to homeless people in Brazil

Abstract Homeless people (HP) are defined as a heterogeneous group that lives in extreme poverty, with weakened or severed family ties, lacking access to conventional housing, and using public spaces as residence. In terms of health, mental health conditions are prominent as a primary treatment demand, with care aimed at addressing needs arising from the use of alcohol and other drugs being a priority in serving HP. In this context, mobile street clinics (MSC) provide health services directly in street environments, focusing on individuals who use alcohol and other substances through harm reduction approaches. Harm reduction is defined as a set of practices aimed at minimizing the adverse health effects of alcohol and drug use without necessarily eliminating usage. Moreover, it represents an ethical and political direction in care, confronting the logic of criminalization and compulsory abstinence, enhancing access to health care for alcohol and other drug users through strategies that foster autonomy development. In the current study, we conducted an integrative review assessing the facilitators and barriers for HP access to mental health services. Preliminary results indicated that MSC, the connection between healthcare workers and the HP they serve, and the implementation of local street-based actions were the main facilitators of access for individuals who engage in harmful use of alcohol and other drugs to mental health services. Conversely, stigma, services’ lack of adjustment to the lifestyles of homeless people, and excessive bureaucracy were identified as the principal barriers of access. MSC, through their harm reduction activities, have emerged as a potential care technology to facilitate access to health services for homeless people. This research aims to present harm reduction as an ethical and political direction of care through the actions of mobile street clinics, as a way to facilitate access to health services for the homeless people. Key messages • Homeless people require mental health care tailored to the use of alcohol and other drugs, and harm reduction strategies implemented through mobile street clinics prove to be an effective form of care. • Mobile street clinics and harm reduction are important care technologies that enhance access to health services for the homeless population.

Healthcare accessibility of individuals at increased risk of STIs: a cross-sectional survey in Italy

Abstract Access to STI prevention and sexual health services in Italy, especially among vulnerable and marginalized groups, is inadequately documented. With the support of community organizations, a cross-sectional survey was conducted between September 2023 and March 2024 to assess health services accessibility among individuals at increased risk of STIs. A total of 1456 responses were analyzed. The respondents comprised 1327 (91.14%) cisgender men, 58 (3.98%) cisgender women, 11 (0.76%) transgender men, 10 (0.69%) transgender women, and 47 (3.23%) non-binary individuals. Overall, 93.8% were men who have sex with men, and 2.8% were women who have sex with women. Mean age was 38 (SD 12). Among respondents, 1284 (88.2%) had undergone HIV testing at least once [102 (7%) living with HIV; 45.1% diagnosed during prior year]. Overall, 563 (38.7%) had been diagnosed with at least one STI in the prior year. Chemsex was reported by 106 (7.3%), with 4.7%, 75.5%, and 35.5% using injective, inhaled or oral substances. Despite a high risk for STIs, 728 (50%) and 410 (28.2%) individuals, respectively, did not visit a specialist in infectious diseases or their primary care provider in the prior year. PrEP awareness was high: 1286 (88.2%) respondents were knowledgeable about it, but only 342 (23.5%) attended PrEP centers and 297 (20.4%) used it. Among PrEP users, 27 (7.9%) encountered difficulties obtaining the drug and 174 (50.9%) considered the cost prohibitive (before it became free). Additionally, 27 (7.9%) of those attending PrEP centers felt judged or disrespected. Overall, 1179 (80.9%) had heard of Mpox, and 1012 (69.5%) were aware of the vaccine, with 301 (20.6%) being vaccinated. Among those vaccinated, 18 (5.2%) felt judged or disrespected. Out of 55 (3.8%) individuals tested for Mpox, 13 (23.6%) tested positive, among which one reported feeling disrespected. The survey highlights critical gaps in healthcare access and awareness among individuals at increased risk of STIs in Italy. Key messages • In a final open-ended question, responders commented on various issues including high costs of services, insufficient provider training and the necessity for improved inclusion of LGBTQ+ individuals. • The robust response from typically hard-to-reach groups underscores the pivotal role of community involvement in enhancing awareness and accessibility of sexual health services, fostering inclusivity.

Thought-Controlled Computer Applications: A Brain–Computer Interface System for Severe Disability Support

This study introduces an integrated computational environment that leverages Brain–Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human–Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system’s Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods.

Rapid assessment of main agronomic indicators in the grain of winter and spring forms of triticale using infrared spectroscopy

Background. Calibration models were developed for rapid assessment of crucial agronomic indicators in the grain of winter and spring triticale from the VIR collection using near-infrared spectroscopy (NIRS). These calibration models will enable users to find the most optimal utilization trends for individual triticale accessions. Materials and methods. Indicators of agronomic value (coloration, moisture, and the content of ash, protein, starch, amylose and fiber) were studied on a set of 32 grain samples collected from winter and spring triticale forms (Triticosecale Wittm. &amp; A. Camus) reproduced in 2021–2022 at the Dagestan Experiment Station, a branch of the N.I. Vavilov All-Russian Institute of Plant Genetic Resources (VIR). Calibration models for the assessment of the said agronomic characters in triticale grain were developed on the platform of the MATRIX-I IR analyzer (Bruker Optics, Germany) and based on the values obtained by classical methods of biochemical analysis adopted at VIR. Results. Reliability of the calibration models was tested on a randomized batch of triticale grain samples: the results of the new calibrations were compared with those of the conventional biochemical analysis. This verification showed that the data obtained through the use of calibration curves were reliable for protein and ash content, while other models required further improvement. Conclusion. Calibration models for measuring protein and ash content developed on the basis of the MATRIX-I IR analyzer can be used to perform rapid assessment of the winter and spring triticale grain. This method makes it possible to accelerate the process of obtaining crop characteristics for a number of basic agronomic characters and preserve valuable breeding material.

Accessibility Design Research: Redesign of the Canterbury Square

Accessibility for disabled individuals in urban environments is a fundamental aspect of inclusive design and social equity. When investigating a suitable site for this redesign project as a group, it is found some iconic outdoor barrier-free designs. Conducted the research in China, the examples include ramps, curb ramps, blind access, and other auxiliary facilities, and most of the specific relevant technologies should comply with the relevant provisions of the Code for Barrier-free Design of Urban Roads and Buildings. Despite progress in awareness and legislation, many cities around the world still face significant challenges in providing adequate access for disabled people. This essay will explore the current state of accessibility for disabled individuals in cities, focusing on the barriers they encounter, and the efforts being made to overcome them. Subsequently, this essay will also examine several exemplary architectural designs that prioritize accessibility and inclusivity, providing real-world examples of innovative solutions that enhance the quality of life for disabled individuals, which give insight into the ongoing redesign project of the Canterbury Lane Square near the Canterbury City Centre.

Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique

Cloud computing has revolutionized the way businesses and individuals access and utilize computing resources. Efficient virtual machine placement is a critical aspect of optimizing resource utilization, reducing operational costs, energy consumption, service level agreement and minimum virtual machine migrations, execution time, and ensuring the overall performance of cloud services. This manuscript introduces a novel approach that combines the creative problem-solving capabilities of brainstorming with the computational power of Artificial Neural Networks (ANN) to address the virtual machine placement problem in cloud environments. In this study, we propose a hybrid technique that leverages the collective intelligence of human brainstorming to generate a diverse set of placement strategies. These strategies are then evaluated, refined, and optimized using an ANN model trained on historical cloud resource allocation workload logs. By integrating the human creative process with the data-driven predictive capabilities of ANN, our approach aims to overcome the limitations of traditional virtual machine placement algorithms, which often struggle to adapt to dynamic workloads and changing resource requirements. The manuscript provides a detailed description of the hybrid technique, including the process of brainstorming for generating placement strategies, data collection and preprocessing, ANN model development, and the integration of these components into an efficient placement system. We present experimental results demonstrating the effectiveness of our approach in optimizing resource allocation, improving service performance, and optimizing resource utilization, reducing energy consumption, service level agreement and minimum virtual machine migrations, reducing execution time compared to existing static, and meta-heuristic methods. The proposed Brain Storming with ANN based Hybrid Technique offers a promising solution for enhancing the efficiency of virtual machine placement in cloud computing environments. BSO-ANN outperforms the existing techniques using performance metrics (energy consumption(Kwh), execution time(ms), SLA violations, and number of migrations). It combines human ingenuity with data-driven insights to adapt to the ever-changing dynamics of cloud workloads. This manuscript contributes to the ongoing research in cloud resource management, offering a practical approach for cloud service providers and organizations to better utilize their resources and enhance the overall quality of cloud-based services. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd.

Asthma Care Chatbot

Asthma is a chronic respiratory condition affecting millions of people worldwide, characterized by inflammation and narrowing of the airways, leading to symptoms such as coughing, wheezing, and shortness of breath. Managing asthma effectively requires timely intervention and personalized care to alleviate symptoms and prevent exacerbations. In this context, the Asthma Care Chatbot serves as an innovative solution to assist individuals in managing their asthma symptoms more efficiently. Leveraging advancements in technology and machine learning, this chatbot provides personalized recommendations based on user-reported symptoms, age, and gender. By integrating a user-friendly graphical interface, the chatbot aims to enhance accessibility and usability for individuals seeking guidance on managing their asthma condition. This project utilizes a machine learning model trained on a dataset comprising asthma severity data to predict the severity of symptoms reported by users. Through an intuitive interface, users can input their symptoms, demographic information, and receive real-time predictions regarding the severity of their asthma symptoms. Based on the predicted severity level, the chatbot offers tailored recommendations, ranging from self-care measures to prompt medical intervention. Furthermore, the chatbot's performance is evaluated using various metrics such as accuracy, precision, recall, and F1 score, ensuring its reliability and effectiveness in providing accurate predictions. By continuously refining its recommendations based on user feedback and data-driven insights, the Asthma Care Chatbot aims to empower individuals to take proactive steps in managing their asthma condition and improving their quality of life. Asthma Care Chatbot represents a pioneering approach towards personalized asthma management, bridging the gap between individuals and accessible healthcare solutions through the integration of technology and machine learning. By offering personalized guidance and support, the chatbot endeavours to empower individuals in effectively managing their asthma symptoms and promoting better.

Exploring Large Language Models to generate Easy to Read content

Ensuring text accessibility and understandability are essential goals, particularly for individuals with cognitive impairments and intellectual disabilities, who encounter problems accessing information across various mediums such as web pages, newspapers, online administrative tasks, or health documents. Initiatives like Easy to Read and Plain Language guidelines aim to simplify complex texts; however, standardizing these guidelines remains challenging and often involves manual processes. This work presents an exploratory investigation into leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) approaches to simplify Spanish texts into Easy to Read formats, with a focus on utilizing Large Language Models (LLMs) for creating accessible texts, especially in generating Easy to Read content. The study contributes a parallel corpus of Spanish adapted for Easy To Read format, which serves as a valuable resource for training and testing text simplification systems. Additionally, several text simplification experiments using LLMs and the collected corpus are conducted, involving fine-tuning and testing a Llama2 model to generate Easy to Read content. A qualitative evaluation, guided by an expert in text adaptation for Easy to Read content, is carried out to assess the automatically simplified texts. This research contributes to advancing text accessibility for individuals with cognitive impairments, highlighting promising strategies for leveraging LLMs while responsibly managing energy usage.

"Everyone should at least get a Kia": Policymaker perspectives on equity of Medicaid among transition age autistic youth

BackgroundThis study examined the challenges faced by transition age autistic youth in accessing Medicaid services, considering the historical focus on children in autism research and services. The study aimed to explore Medicaid enrollment and support for this population. MethodThe study involved Medicaid and Protection & Advocacy (P&A) representatives from states with varying performance levels in eligibility, access, and service use. Discussions with experts focused on waivers, Early, Periodic, Screening, Detection, and Treatment (EPSDT), and barriers to service access. ResultsThe findings highlighted the need to improve service access for transition age autistic youth. States identified the importance of maximizing EPDST utilization before aging out of childhood systems and pursuing waiver enrollment. These insights emphasized the significance of addressing service gaps during the transition to adulthood. ConclusionsThe study's implications for practice are substantial. Policymakers and service providers must acknowledge the limitedservice access for autistic individuals as they transition from child systems. The study emphasized the value of strategies like maximizing EPDST utilization and pursuing waivers to ensure ongoing support for autistic youth during their transition to adulthood. By addressing these challenges, practitioners can better meet the needs of transition age autistic individuals and facilitate their access to essential services.

A real-time eye movement-based computer interface for people with disabilities

It is costly to develop systems that enable individuals exposed to Amyotrophic Lateral Sclerosis and similar diseases that directly affect the neuromotor ability to communicate with the outside world. In this study, a budget friendly, high-accuracy, software-based, gaze-controlled, real-time virtual keyboard approach that can enable these people to communicate effectively is proposed. The proposed application requires only a computer and a webcam and has a user-friendly interface that meets the basic daily needs of individuals with disabilities. Since the proposed system does not require an extra action such as blinking, it makes it possible to use computers in advanced stage patients who cannot blink their eyes. The application which uses a deep learning-based facial landmark detector, determines the letters the user focuses on the screen and converts thoughts into text. The part of the screen that the user focuses on is determined with a new selection approach inspired by the K-Nearest Neighbors algorithm. This approach, which does not require blinking, offers high speed and accuracy. In the tests, a typing speed of 23.33 characters per minute is achieved with an accuracy rate of 95.12%. It is anticipated that the study will increase computer accessibility for disabled individuals with limited mobility and contribute to the development of real-time eye tracking systems.

The Influence of Problematic Social Media Use on Impulsive Buying in Early Adulthood in Padang City

The amount of interesting information on TikTok that TikTok users see will make users interested and often there is a sudden and unconscious desire to buy a product without any prior planning or it is called impulsive buying. This is because many and often individuals access TikTok for a long time or because they are in a negative mood if they do not access TikTok. This research aims to determine the problematic social media use on impulsive buying among early adult TikTok users in the city of Padang. This research design uses quantitative sampling techniques used namely purposive sampling. The sample in this study was 200 respondents whose characteristics were determined by the researcher to be used as samples. The results of the data were processed and analyzed using simple linaer regression analysis techniques, obtaining values of r = 0,535 and p = 0,000. The final results of the research show that there is a positive influence of the independent variable (problematic social media use) on the dependent variable (impulsive buying). These results show that the higher the problematic social media use in early adults using TikTok the higher impulsive buying.

TECHNOLOGICAL PRACTICE MEDIATED BY REFERENCE GROUPS: A THREE-DIMENSIONAL PERSPECTIVE

The article aims to understand how the organizational logic of a family budget unit facilitates dialogue between the members of that unit and establishes the intermediation of information and communication between these individuals. In other words, does the family context influence technological practice and learning? Technological praxis also depends on the ability to use technology, mainly developed during schooling. Is the correlation between formal education and technological learning significant? A three-dimensional analytical model is proposed that simultaneously evaluates three independent variable factors that can directly and indirectly interfere with an individual's technological practice (dependent variable). First is the individual's access to communication technologies in their family. Secondly, the level of education achieved by the individual. Thirdly, access to communication and information technologies (ICTs) in the family of destination. Quantitative methodologies (path analysis) are used to analyze data and information on Brazil based on data from the National Household Sampling Survey (PNAD/2014).

BUILDING DOCUMENT READING ASSISTANT FOR THE VISUALLY IMPAIRED

This study introduces a solution that applies document analysis and recognition technologies to enhance document accessibility for individuals with visual impairments. The objective is to develop an algorithm capable of accurately analyzing the content of document components and converting them into voice format. Leveraging the pre-trained YOLOv8 model for document analysis and optical character recognition technology, the image annotation model uses the AIAnytime API and Pix2Tex technology to extract LaTeX code from images, facilitating the conversion of mathematical formulas into spoken words. The research results demonstrate significant progress in effectively supporting document reading, making a meaningful contribution to the field of assistive technology for the visually impaired.