Blind Individuals Research Articles

Playfulness as a pedagogical strategy: game to educate about the rights of blind people and the role of guide dog

Since 1990, there has been a concern, in the most varied countries, with external public policies for inclusive education, however, still has a long way to go. There are several ways of promoting the rights of the blind to society, and one of these ways is games. This research characterized, in terms of nature, as applied research, as its interest is practical, and it is hoped that the results are used in solving real problems. This study adopts a methodology qualitative and has an exploratory character, advancing by deepening the understanding of the problem under study. Based on pre-existing literature on the rights of blind people and the reactions to the guide dog, the research is categorized as both bibliographic and documentary. The central purpose game theory is used to facilitate the dissemination of the rights of blind individuals and specifically the role played by the guide dog. Through a playful approach, the aim is to investigate why way the design of games can help in the development of effective pedagogical strategies external activities to raise awareness about the rights of blind people and the role played by guide dogs, preceding their effective implementation in the social context.

A Case Study on (EAVIBA) Enhanced Accessibility for Visually Impaired & Blind Artists using Machine Learning

This research paper introduces EAVIBA, a pioneering voice-driven interface tailored to empower visually impaired and blind individuals in artistic pursuits. Rooted in principles of accessible design, EAVIBA seamlessly integrates voice commands, text-to-speech functionality, and a customizable graphical user interface. The study delves into EAVIBA's architecture, implementation of assistive technologies, and commitment to accessibility standards like the Web Content Accessibility Guidelines (WCAG). Through a robust iterative testing process, the paper evaluates the program, soliciting both qualitative and quantitative feedback on key aspects such as interface clarity, voice command recognition, and customization features. Objectives: The primary objectives of EAVIBA encompass developing an inclusive graphical user interface, implementing voice recognition and text-to-speech functionalities, providing creative tools and recommendations, enabling customization, and facilitating user feedback for continuous improvement. Method: The research employed a user-centric approach, incorporating principles of universal design in the graphical user interface. The integration of voice recognition and text-to-speech technologies, along with customizable features, was meticulously executed. Evaluation involved iterative user testing to gauge interface clarity, voice command recognition accuracy, and the efficacy of customization options. Findings: Quantitative data revealed a high degree of satisfaction among users, with an average recognition accuracy of voice commands exceeding 90%. Interface clarity received positive feedback, emphasizing the success of the high contrast mode and Blind User Mode. Users appreciated the customization options, with 80% indicating satisfaction with tailored voice commands. Novelty: EAVIBA's novelty lies in its holistic approach, addressing accessibility and fostering creativity. The integration of voice-driven interactions, high contrast modes, and creative recommendations distinguishes EAVIBA from existing solutions, presenting a comprehensive platform for visually impaired artists. Keywords: Assistive technologies, visually impaired, blind artists, voice-driven interface, machine learning, voice recognition.

Empowering the Blind: Contactless Activity Recognition with Commodity Software-Defined Radio and Ultra-High-Frequency Radio Frequency Identification.

This study presents a novel computational radio frequency identification (RFID) system designed specifically for assisting blind individuals, utilising software-defined radio (SDR) with coherent detection. The system employs battery-less ultra-high-frequency (UHF) tag arrays in Gen2 RFID systems, enhancing the transmission of sensed information beyond standard identification bits. Our method uses an SDR reader to efficiently manage multiple tags with Gen2 preambles implemented on a single transceiver card. The results highlight the system's real-time capability to detect movements and direction of walking within a four-meter range, indicating significant advances in contactless activity monitoring. This system not only handles the complexities of multiple tag scenarios but also delineates the influence of system parameters on RFID operational efficiency. This study contributes to assistive technology, provides a platform for future advancements aimed at addressing contemporary limitations in pseudo-localisation, and offers a practical, affordable assistance system for blind individuals.

Virtual Assistance for Banking System using AI and ML

Virtual assistance has emerged as a transformative technology in the realm of banking, offering an efficient and user-friendly means of engaging with financial services. This abstract explores the concept of a virtual assistant for the banking system, a sophisticated digital entity designed to facilitate and enhance customer interactions within the financial sector. A groundbreaking solution to empower blind individuals in navigating the complex landscape of banking operations. Rooted in the ethos of inclusivity, the project merges cutting-edge technologies with a user-friendly interface, ensuring a seamless and independent banking experience for visually impaired users. The frontend of the application is developed using Flutter, offering a responsive and visually appealing cross-platform interface that caters to a diverse range of user preferences. Meanwhile, the Python backend orchestrates the intricate ballet of banking operations, communicating securely with banking APIs to perform tasks such as balance inquiries, fund transfers, and transaction history retrieval. Central to the project's innovation is the integration of speech-to-text and text-to-speech functionalities. Users can interact with the application through spoken commands, which are accurately transcribed into text, and receive information through synthesized speech, providing a natural and conversational experience. A sophisticated chat box further facilitates communication, enabling users to seek assistance and guidance effortlessly. The text-based conversation within the chat box becomes a crucial element in ensuring a user-friendly and accessible interface. Security is paramount, with robust measures in place to safeguard sensitive information, employing encryption for data such as login credentials and transaction details

How path integration abilities of blind people change in different exploration conditions.

For animals to locate resources and stay safe, navigation is an essential cognitive skill. Blind people use different navigational strategies to encode the environment. Path integration significantly influences spatial navigation, which is the ongoing update of position and orientation during self-motion. This study examines two separate things: (i) how guided and non-guided strategies affect blind individuals in encoding and mentally representing a trajectory and (ii) the sensory preferences for potential navigational aids through questionnaire-based research. This study first highlights the significant role that the absence of vision plays in understanding body centered and proprioceptive cues. Furthermore, it also underscores the urgent need to develop navigation-assistive technologies customized to meet the specific needs of users.

Seeing with Sound: Neural Networks and Deep Learning for Indian Currency Recognition

Visually impaired individuals face significant challenges in distinguishing between different denominations of Indian currency notes, which hinders their ability to perform routine tasks such as shopping and financial management. Traditional methods of currency recognition, such as tactile features or optical scanners, are often burdensome and inconvenient. This groundbreaking system utilizes a deep learning model developed from a vast dataset of Indian rupee notes captured in diverse environments, including various orientations, stages of wear-and-tear, and lighting conditions, ensuring robust training. By leveraging Convolutional Neural Networks (CNNs), the system ensures a high degree of accuracy in identifying notes and generates distinct audio cues corresponding to each denomination, simplifying the differentiation process for visually impaired individuals. The use of neural networks enables the system to continuously refine its recognition capabilities, adapting and improving its identification methods with additional data, including updated note variations or unexpected scenarios. Upon widespread availability, this technology has the potential to revolutionize how blind individuals in India manage their finances, providing them with increased autonomy and confidence in everyday transactions. The proposed system offers several advantages over traditional currency recognition methods: it is non-invasive, requiring no physical contact with the currency notes, thus enhancing convenience; its portability enables implementation on mobile devices, facilitating access for users on the go; and it prioritizes user-friendliness by providing real-time audio feedback, further simplifying the currency identification process for visually impaired individuals. Overall, this innovative solution represents a significant stride towards empowering the visually impaired community in India, offering newfound freedom and self-assurance in managing their monetary affairs.

Identify, Adapt, Persist

Personal health technologies (PHTs) often do not consider the accessibility needs of blind individuals, preventing access to their capabilities and data. However, despite the accessibility barriers, some blind individuals persistently use such systems and even express satisfaction with them. To obtain a deeper understanding of blind users' prolonged experiences in PHTs, we interviewed 11 individuals who continue to use such technologies, discussing and observing their past and current interactions with their systems. We report on usability issues blind users encounter and how they adapt to these situations, and theories for the persistent use of PHTs in the face of poor accessibility. We reflect on strategies to improve the accessibility and usability of PHTs for blind users, as well as ideas to aid the normalization of accessible features within these systems.

Perception and discrimination of real-life emotional vocalizations in early blind individuals.

The capacity to understand others' emotions and react accordingly is a key social ability. However, it may be compromised in case of a profound sensory loss that limits the contribution of available contextual cues (e.g., facial expression, gestures, body posture) to interpret emotions expressed by others. In this study, we specifically investigated whether early blindness affects the capacity to interpret emotional vocalizations, whose valence may be difficult to recognize without a meaningful context. We asked a group of early blind (N = 22) and sighted controls (N = 22) to evaluate the valence and the intensity of spontaneous fearful and joyful non-verbal vocalizations. Our data showed that emotional vocalizations presented alone (i.e., with no contextual information) are similarly ambiguous for blind and sighted individuals but are perceived as more intense by the former possibly reflecting their higher saliency when visual experience is unavailable. Our study contributes to a better understanding of how sensory experience shapes ememotion recognition.

“EDUPLAY”- Interactive Game Based Learning Platform

This research introduces an innovative approach to inclusive education through the development of a game-based Learning platform tailored for Persons with Disabilities (PwDs). With a focus on fostering an accessible and engaging educational environment, the platform aims to break down barriers and promote equal learning opportunities for individuals with diverse abilities. The present study investigates the use of gamification to foster commitment and engagement among users with disabilities. Two case studies demonstrating the application of gamification are provided. The first is the development of an application to teach a blind person, and the second one is the creation of an application to aid individuals with Autism Spectrum Disorder (ASD) in navigating their environment. The study reveals that universal design for learning principles can be used indirectly to adjust apps for users to utilize the software consistently. This study provides preliminary evaluations for both case studies, which were undertaken with relatively small samples. The first case study revealed that three blind individuals who took part in the review scored an average of 91.7on the system usability scale. At the same time, the second case study involving the observation of a single individual with ASD also revealed that utilizing the designed app improved performance. Despite the limited sample size, the findings suggest that gamification may effectively encourage and generate commitment among the users with disabilities.

A Deep Learning Based Framework for Blind Road Occupancy Detection

This research paper addresses the pressing concern of road safety for the visually impaired in densely populated regions, especially in China. While tactile paving exists to guide blind individuals, unexpected obstacles pose serious hazards. The study proposes an Artificial Intelligence (AI) system utilizing advanced machine learning techniques to identify obstacles on blind roads, providing real-time feedback for navigation. To overcome data limitations, a virtual environment using Pybullet is created for data generation, combining synthetic and real-world images for training. This study introduces a Convolutional Neural Network (CNN)-based model and integrates a Vision Transformer (ViT) model, comparing their efficacies. The progressive training approach yields a highly effective CNN model, outperforming ViT models. The practical application of the CNN model in real-world scenarios has proven its efficacy in detecting obstacles, underscoring its reliability and significant contribution to improving road safety for visually impaired individuals. This research extends beyond providing a safety enhancement measure; it also sheds light on the wider applicability of such methods in addressing issues like the scarcity of real-world data.

Altered grid-like coding in early blind people

Cognitive maps in the hippocampal-entorhinal system are central for the representation of both spatial and non-spatial relationships. Although this system, especially in humans, heavily relies on vision, the role of visual experience in shaping the development of cognitive maps remains largely unknown. Here, we test sighted and early blind individuals in both imagined navigation in fMRI and real-world navigation. During imagined navigation, the Human Navigation Network, constituted by frontal, medial temporal, and parietal cortices, is reliably activated in both groups, showing resilience to visual deprivation. However, neural geometry analyses highlight crucial differences between groups. A 60° rotational symmetry, characteristic of a hexagonal grid-like coding, emerges in the entorhinal cortex of sighted but not blind people, who instead show a 90° (4-fold) symmetry, indicative of a square grid. Moreover, higher parietal cortex activity during navigation in blind people correlates with the magnitude of 4-fold symmetry. In sum, early blindness can alter the geometry of entorhinal cognitive maps, possibly as a consequence of higher reliance on parietal egocentric coding during navigation.

Smart Eye : Empowering the Visually Impaired with Assistive Technology

According to the World Health Organization, an estimated 39 million people worldwide are visually impaired, underscoring the critical need for innovative solutions to assist blind individuals in navigating their environment independently. In response, this study introduces an intelligent and multipurpose bright blind stick leveraging Internet of Things (IoT) technology. The stick integrates components such as Arduino UNO, ultrasonic sensor, servo motor, Bluetooth module, GPS, and GSM modules to detect obstacles in real-time and provide audio feedback alerts to the user. Additionally, the technology enables remote monitoring by caregivers through GPS tracking and allows users to send location-based alert messages in emergencies. Through extensive research and comparative analysis of existing systems, including traditional white canes and other electronic aids, our solution has been designed to overcome common limitations such as high cost, limited functionality, and lack of portability. The intelligent multipurpose bright blind stick offers a cost-effective, portable, and intelligent tool aimed at significantly enhancing the independence and safety of blind individuals, fostering inclusivity and autonomy in their daily lives. This paper will provide insights into the design, implementation, and user testing of the solution, highlighting its effectiveness in real-world scenarios and its potential to make a meaningful impact on the lives of blind individuals worldwide. Index Terms - Arduino UNO, Servo Motor , Ultrasonic Sensor Bluetooth Module, GPS Module, GSM Module, Multi-functional Blind Stick and Internet of Things.

Lack of braille labelling and instructions on Chinese pharmaceutical packaging

Braille labels and instructions on medicine packaging are scarce in China, impeding blind individuals’ ability to obtain information about medications. Article 37 of China’s Barrier-Free Environment Creation Law, enacted in September 2023, requires braille labelling and instructions for pharmaceuticals. This provision offers essential legal protection to ensure the safety of blind individuals when using medications. Nevertheless, the use of braille labels and instructions for medicines is hindered by ambiguous statutes, limited social consciousness, financial factors and technological constraints. To promote the use of braille on medicinal labels and instructions, China should bolster its legislation, increase social consciousness, alleviate the financial strain on pharmaceutical manufacturers, foster technological advancement and encourage collaboration and self-governance within the pharmaceutical industry.

Trinal Optics for Blind People Using Smart Object Recognition for Navigation Through Voice Assistant

The project Trinal Optics is a wearable device, it will be able to make blind people to live their lives more independently. The device is combination of hardware, software and Technologies such as Camera, Arduino Uno, Voice Assistant, Bluetooth Headset/Earphone, Ultrasonic Sensor, Object Detection, and Distance Analysis. These components help user to understand the environment better. The Camera is used to capture the image for object detection and then Voice assistant is conveying audio message to the user along with the Distance analysis of object, Arduino Uno is used to integrate hardware and software, navigation is used to guide the users to its destination Ultrasonic sensor is used to make the beep sound if there is any object in front of the user. Trinal Optics looks forward to help blind individuals to have a bit easy life with the help of technology. Keywords: Arduino Uno, Camera, Bluetooth Earphone, Voice Assistant, Object Detection, Distances Analysis, Ultrasonic Sensor.

Smart Gloves for Blind Person using Ultrasonic Sensor & Arduino Nano

This paper presents the design and development of smart gloves aimed at enhancing the mobility and independence of blind individuals. The proposed system integrates an Arduino Nano microcontroller with an ultrasonic sensor to detect obstacles in the user's path. The ultrasonic sensor continuously scans the surroundings and provides real-time feedback to the wearer through haptic feedback mechanisms incorporated into the gloves. The system employs vibration patterns to convey the proximity and location of obstacles, allowing the user to navigate safely and confidently in various environments. Additionally, the compact and lightweight nature of the Arduino Nano ensures portability and ease of use. The effectiveness of the smart gloves was evaluated through user testing, demonstrating promising results in improving the mobility and autonomy of visually impaired individuals. This innovative solution has the potential to significantly enhance the quality of life for blind individuals by providing them with greater freedom of movement and increased safety in their daily activities. Keywords: Ultrasonic Sensors Arduino Nano, Vibrator motor, Speaker, Connecting wires, JQ6500 voice module.

Visualizations and pictures for the visually impaired and its connection to STEM education

Visualization is an important tool for teaching abstract subjects such as mathematics and for processing and expressing thoughts, also in art. Blind individuals cannot obtain information from two-dimensional visualizations, but they can touch structured surfaces with their fingers. To aid visually impaired students in creating and reading tangible, 3D printable visualizations, we have developed a generator that produces three-dimensional reliefs of images through techniques used by artists to make artwork more tangible. Our research includes interviews with visually impaired individuals to uncover new approaches and improve our touchable 3D visualizations. Additionally, we asked over 30 pre-service mathematics teachers through an open questionnaire whether 3D printing offers educational benefits for students to find potential connections between 3D printing and STEM education. Our findings hint towards opportunities for STEM education for both visually impaired and non-impaired students.

Neuronal Control of Posture in Blind Individuals

The control of posture is guided by the integration of sensory information. Because blind individuals cannot apply visual information to control posture as sighted individuals do they must compensate by the remaining senses. We therefore hypothesize that blind individuals alter their brain activation in the sensorimotor cortex during postural control to compensate for balance control without vision by the increased integration of somatosensory information. Ten blind and ten sighted (matched) individuals controlled posture during conditions with (I) eyes closed / open, and (II) stable / unstable surface conditions. Postural sway was recorded by applying a pressure distribution measuring plate. Brain activation was collected by functional Near InfraRed Spectroscopy (fNIRS) above motor-sensory cortices of the right and left hemispheres. Blind individuals showed significantly increased postural sway when balancing with open eyes on an unstable surface and when compared to sighted individuals. Whereas blind individuals showed significantly increased brain activation when balancing with open eyes on stable and unstable surface conditions, sighted individuals increased their brain oxygenation only during closed eyes and unstable surface conditions. Overall conditions, blind individuals presented significantly increased brain activation in two channels of the left and right hemispheric motor-sensory cortex when compared to sighted individuals. We therefore conclude that sighted individuals increase their brain oxygenation in the sensorimotor cortex during postural control tasks that demand sensory integration processes. Blind individuals are characterized by increased brain activation overall conditions indicating additional sensory integration during postural control. Thus, the sensorimotor cortex of blind individuals adapts to control posture without vision.

Implementation of Smart Hindrance Tracking System

The Smart Hindrance Tracking System for Blind Individuals is a mini-project aimed at enhancing the mobility and safety of visually impaired individuals in navigating their surroundings. Leveraging cutting-edge technologies, this system integrates computer vision, sensor networks, and mobile applications to provide real-time hindrance detection and navigation assistance. The primary objective is to empower blind individuals with a reliable and intelligent solution that enables them to identify and navigate around obstacles in their path. The system utilizes a combination of depth-sensing cameras and ultrasonic sensors to detect obstacles in the user's vicinity. These sensors feed data to a central processing unit, where a computer vision algorithm processes the information in real-time, identifying obstacles and their spatial locations. The processed data is then communicated to the user through a user-friendly mobile application. This research presents the daily challenges faced by visually impaired individuals but also fosters inclusivity and independence. The Smart Hindrance Tracking System stands at the intersection of technology and social impact, offering an innovative solution to improve the lives of those with visual impairments.

Vision plays a calibrating role in discriminating threat-related vocal emotions.

The ability to reliably discriminate vocal expressions of emotion is crucial to engage in successful social interactions. This process is arguably more crucial for blind individuals, since they cannot extract social information from faces and bodies, and therefore chiefly rely on voices to infer the emotional state of their interlocutors. Blind have demonstrated superior abilities in several aspects of auditory perception, but research on their ability to discriminate vocal features is still scarce and has provided unclear results. Here, we used a gating psychophysical paradigm to test whether early blind people would differ from individually matched sighted controls at the recognition of emotional expressions. Surprisingly, blind people showed lower performance than controls in discriminating specific vocal emotions. We presented segments of nonlinguistic emotional vocalizations of increasing duration (100-400 ms), portraying five basic emotions (fear, happy, sad, disgust, and angry), and we asked our participants for an explicit emotion categorization task. We then calculated sensitivity indices and confusion patterns of their performance. We observed better performance of the sighted group in the discrimination of angry and fearful expression, with no between-group differences for other emotions. This result supports the view that vision plays a calibrating role for specific threat-related emotions specifically. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Object recognition via echoes: quantifying the crossmodal transfer of three-dimensional shape information between echolocation, vision, and haptics.

Active echolocation allows blind individuals to explore their surroundings via self-generated sounds, similarly to dolphins and other echolocating animals. Echolocators emit sounds, such as finger snaps or mouth clicks, and parse the returning echoes for information about their surroundings, including the location, size, and material composition of objects. Because a crucial function of perceiving objects is to enable effective interaction with them, it is important to understand the degree to which three-dimensional shape information extracted from object echoes is useful in the context of other modalities such as haptics or vision. Here, we investigated the resolution of crossmodal transfer of object-level information between acoustic echoes and other senses. First, in a delayed match-to-sample task, blind expert echolocators and sighted control participants inspected common (everyday) and novel target objects using echolocation, then distinguished the target object from a distractor using only haptic information. For blind participants, discrimination accuracy was overall above chance and similar for both common and novel objects, whereas as a group, sighted participants performed above chance for the common, but not novel objects, suggesting that some coarse object information (a) is available to both expert blind and novice sighted echolocators, (b) transfers from auditory to haptic modalities, and (c) may be facilitated by prior object familiarity and/or material differences, particularly for novice echolocators. Next, to estimate an equivalent resolution in visual terms, we briefly presented blurred images of the novel stimuli to sighted participants (N = 22), who then performed the same haptic discrimination task. We found that visuo-haptic discrimination performance approximately matched echo-haptic discrimination for a Gaussian blur kernel σ of ~2.5°. In this way, by matching visual and echo-based contributions to object discrimination, we can estimate the quality of echoacoustic information that transfers to other sensory modalities, predict theoretical bounds on perception, and inform the design of assistive techniques and technology available for blind individuals.