Improving Walking Path Generation Through Biped Constraint in Indoor Navigation System for Visually Impaired Individuals.

This paper introduces a walking path generation method specifically developed for the Smart Cane, which is a RNA (Robotic Navigation Assistance Device) aimed at enhancing indoor navigation for visually impaired individuals. The proposed approach combines the utilization of a LIPM (Linear Inverse Pendulum Model) and LFPC (Linear Foot Placement Controller) motion primitives to generate walking paths specifically designed for visually impaired individuals. The primary objective is to generate paths that conform to human motion constraints, thereby guaranteeing an efficient and natural navigation experience. Integrating autonomous navigation framework, the Smart Cane facilitates safe and effective guidance for visually impaired participants in the indoor environments. Furthermore, comparative experiments have been conducted to validate the effectiveness of the proposed method, providing evidence of its capability to generate walking paths that conform to human motion constraints. The experiment results indicate that the proposed walking path generation method is a promising solution to enhance the navigation experience of visually impaired individuals.

Biomechanical Considerations of Refreshable Braille and Tactile Graphics Toward Equitable Access: A Review.

This review highlights the biomechanical foundations of braille and tactile graphic discrimination within the context of design innovations in information access for the blind and low-vision community. Braille discrimination is a complex and poorly understood process that necessitates the coordination of motor control, mechanotransduction, and cognitive-linguistic processing. Despite substantial technological advances and multiple design attempts over the last fifty years, a low-cost, high-fidelity refreshable braille and tactile graphics display has yet to be delivered. Consequently, the blind and low-vision communities are left with limited options for information access. This is amplified by the rapid adoption of graphical user interfaces for human-computer interaction, a move that the blind and low vision community were effectively excluded from. Text-to-speech screen readers lack the ability to convey the nuances necessary for science, technology, engineering, arts, and math education and offer limited privacy for the user. Printed braille and tactile graphics are effective modalities but are time and resource-intensive, difficult to access, and lack real-time rendering. Single- and multiline refreshable braille devices either lack functionality or are extremely cost-prohibitive. Early computational models of mechanotransduction through complex digital skin tissue and the kinematics of the braille reading finger are explored as insight into device design specifications. A use-centered, convergence approach for future designs is discussed in which the design space is defined by both the end-user requirements and the available technology.

Development and Evaluation of Refreshable Braille Display and Active Touch-Reading System for Digital Reading of the Visually Impaired.

The traditional way of reading through Braille books is constraining the reading experience of blind or visually impaired (BVI) in the digital age. In order to improve the reading convenience of BVI, this paper proposes a low-cost and refreshable Braille display device, and solves the problems of high energy consumption and low latching force existing in existing devices. Further, the Braille display device was combined with the 3D Systems Touch device to develop an active Braille touch-reading system for digital reading of BVI with the help of the CHAI3D virtual environment. Firstly, according to the actual needs of BVI to touch and read the Braille dots, this paper utilizes the beam structure to provide a full latching function for the raised Braille dot without energy consumption. Through theoretical derivation and finite element analysis, the performance of the Braille dot actuator is optimized to provide sufficient feedback force and latching force for finger's touch-reading. Then, this paper designs a virtual Braille interactive environment based on the CHAI3D, and combines the sense of touch with audio to effectively improve the recognition accuracy and reading efficiency of BVI for Braille through the multi-modal presentation of Braille information. The performance test results of the device show that the average lifting force of the Braille dot actuator is 101.67 mN, the latching force is over 5 N, and the average refresh frequency is 17.1 Hz, which meets the touch-reading needs of BVI. User experiments show that the average accuracy rate of BVI subjects in identifying digitized Braille is 95.5%, and subjects have a high subjective evaluation of the system.

Exploring the value of multi-sensory aids in co-designing assistive home devices for older adults with cognitive impairment.

In this study, we aimed to investigate the benefits of co-design prompts/aids in the development of assistive devices for and with older adults who have cognitive impairment (CI), with the goal of improving their ability to live independently at home. We conducted a series of co-design workshops and utilized eight sets of multi-sensory aids to explore their values and effectiveness in engaging older adults with CI in co-design processes. Our findings revealed that the co-design aids had several benefits, including: (1) increasing the exchange of knowledge and awareness between older adults and designers; (2) eliciting insightful information through multi-sensorial aids, and (3) generating novel assistive design solutions to support seniors' independent living at home. We discuss our findings in relation to the multi-sensorial attributes of co-design aids, which empower older adults with CI to express their opinions and actively participate in co-designing assistive devices that meet their needs/expectations.

Intelligent Head-Mounted Obstacle Avoidance Wearable for the Blind and Visually Impaired.

Individuals who are Blind and Visually Impaired (BVI) take significant risks and dangers on obstacles, particularly when they are unaccompanied. We propose an intelligent head-mount device to assist BVI people with this challenge. The objective of this study is to develop a computationally efficient mechanism that can effectively detect obstacles in real time and provide warnings. The learned model aims to be both reliable and compact so that it can be integrated into a wearable device with a small size. Additionally, it should be capable of handling natural head turns, which can generally impact the accuracy of readings from the device's sensors. Over thirty models with different hyper-parameters were explored and their key metrics were compared to identify the most suitable model that strikes a balance between accuracy and real-time performance. Our study demonstrates the feasibility of a highly efficient wearable device that can assist BVI individuals in avoiding obstacles with a high level of accuracy.

Japanese Braille Translation Using Deep Learning - Conversion from Phonetic Characters (Kana) to Homonymic Characters (Kanji).

A blind student writes and submits reports in Braille word processor, which is difficult for teachers to read. This study's purpose is to make a translator from Braille into mixed Kana-Kanji sentences for such teachers. Because Kanji has homonyms, it is not always possible to get correct results when converting. To overcome this difficulty, we used deep learning for translation. We built a training dataset composed from 15,000 pairs of Braille codes and mixed Kana-Kanji sentences, and a validation dataset. In training, we got an accuracy of 0.906 and a good Bleu score of 0.600. In validation, we found 5 mistaken words in selecting homonymous Kanji by examining translation mistakes from 100 pairs of the verification sentences. The choice of homonymous Kanji depends on the context. For decreasing such type of errors, it is necessary to introduce of translation of paragraphs by increasing the scale of the network model in deep learning, and to expand the network structure.

Multi-Sensor Data Fusion Solutions for Blind and Visually Impaired: Research and Commercial Navigation Applications for Indoor and Outdoor Spaces.

Several assistive technology solutions, targeting the group of Blind and Visually Impaired (BVI), have been proposed in the literature utilizing multi-sensor data fusion techniques. Furthermore, several commercial systems are currently being used in real-life scenarios by BVI individuals. However, given the rate by which new publications are made, the available review studies become quickly outdated. Moreover, there is no comparative study regarding the multi-sensor data fusion techniques between those found in the research literature and those being used in the commercial applications that many BVI individuals trust to complete their everyday activities. The objective of this study is to classify the available multi-sensor data fusion solutions found in the research literature and the commercial applications, conduct a comparative study between the most popular commercial applications (Blindsquare, Lazarillo, Ariadne GPS, Nav by ViaOpta, Seeing Assistant Move) regarding the supported features as well as compare the two most popular ones (Blindsquare and Lazarillo) with the BlindRouteVision application, developed by the authors, from the standpoint of Usability and User Experience (UX) through field testing. The literature review of sensor-fusion solutions highlights the trends of utilizing computer vision and deep learning techniques, the comparison of the commercial applications reveals their features, strengths, and weaknesses while Usability and UX demonstrate that BVI individuals are willing to sacrifice a wealth of features for more reliable navigation.

Efficacy of low-vision devices in elderly population with age-related macular degeneration.

Purpose: Age-related macular degeneration (AMD) is a common cause of blindness, residual damage to macular area in spite of treatment necessitates visual rehabilitation by means of low-vision aids (LVAs). Methods: Thirty patients suffering from different stages of AMD requiring LVAs were included in this prospective study. Patients with nonprogressive, adequately treated AMD were enrolled over a 12-month period, prescribed requisite LVAs and followed-up for a minimum 1-month period. Before and after provision of LVAs, near work efficiencies were evaluated by calculating reading speed as words per minute (wpm) under both photopic and mesopic light conditions, and impact of poor vision on activities of daily living (ADL) was quantified by modified standard questionnaire based on Nhung X et al. questionnaire. Results: Of the 30 patients mean studied with mean age of 68 ± 10 years, 20 patients (66.7%) had dry AMD in better eye and 10 (33.3%) had wet AMD. Post-LVA, near visual acuity improved significantly and all cases were able to read some letters on near vision chart with an average improvement of 2.4 ± 0.96 lines. The different LVAs prescribed were high plus reading spectacles (up to 10 D) in 23.3%, hand-held magnifiers in 53.3%, base in prisms in 10%, stand held magnifiers in 6.7%, and bar and dome magnifiers in 3.3%. Conclusion: LVAs are effective in visual rehabilitation in patients with AMD. Self-reported reduction in visual dependency and improvement in vision-related quality of life post use of aids corroborated perceived benefit.

Obstacle Detection System for Navigation Assistance of Visually Impaired People Based on Deep Learning Techniques.

Visually impaired people seek social integration, yet their mobility is restricted. They need a personal navigation system that can provide privacy and increase their confidence for better life quality. In this paper, based on deep learning and neural architecture search (NAS), we propose an intelligent navigation assistance system for visually impaired people. The deep learning model has achieved significant success through well-designed architecture. Subsequently, NAS has proved to be a promising technique for automatically searching for the optimal architecture and reducing human efforts for architecture design. However, this new technique requires extensive computation, limiting its wide use. Due to its high computation requirement, NAS has been less investigated for computer vision tasks, especially object detection. Therefore, we propose a fast NAS to search for an object detection framework by considering efficiency. The NAS will be used to explore the feature pyramid network and the prediction stage for an anchor-free object detection model. The proposed NAS is based on a tailored reinforcement learning technique. The searched model was evaluated on a combination of the Coco dataset and the Indoor Object Detection and Recognition (IODR) dataset. The resulting model outperformed the original model by 2.6% in average precision (AP) with acceptable computation complexity. The achieved results proved the efficiency of the proposed NAS for custom object detection.

Braille recognition by E-skin system based on binary memristive neural network.

Braille system is widely used worldwide for communication by visually impaired people. However, there are still some visually impaired people who are unable to learn Braille system due to various factors, such as the age (too young or too old), brain damage, etc. A wearable and low-cost Braille recognition system may substantially help these people recognize Braille or assist them in Braille learning. In this work, we fabricated polydimethylsiloxane (PDMS)-based flexible pressure sensors to construct an electronic skin (E-skin) for the application of Braille recognition. The E-skin mimics human touch sensing function for collecting Braille information. Braille recognition is realized with a neural network based on memristors. We utilize a binary neural network algorithm with only two bias layers and three fully connected layers. Such neural network design remarkably reduces the calculation burden and, thus, the system cost. Experiments show that the system can achieve a recognition accuracy of up to 91.25%. This work demonstrates the possibility of realizing a wearable and low-cost Braille recognition system and a Braille learning-assistance system.

Value of Handheld Optical Illuminated Magnifiers for Sustained Silent Reading by Visually Impaired Adults.

SIGNIFICANCE: Vision rehabilitation providers tend to recommend handheld, illuminated optical magnifiers for short-duration spot reading tasks, but this study indicates that they are also a viable option to improve sustained, continuous text reading (e.g., books or magazines), especially for visually impaired adults who read slowly with only spectacle-based near correction. PURPOSE: The utility of handheld optical magnifiers for sustained silent reading tasks involving normal-sized continuous text could be a valuable indication that is not recognized by vision rehabilitation providers and patients. METHODS: Handheld, illuminated optical magnifiers were dispensed to 29 visually impaired adults who completed the sustained silent reading test by phone at baseline without the new magnifier and 1 month after using the magnifier. Reading speed in words per minute (wpm) was calculated from the time to read each page and then averaged across up to 10 pages or determined for the fastest read page (maximum). RESULTS: From baseline without the magnifier to 1 month with the magnifier, there was a significant improvement in mean reading speed by 14 wpm (95% confidence interval [CI], 2.6 to 24; P = .02) and for maximum reading speed by 18 wpm (95% CI, 5.4 to 30; P = .005) on average across participants. Participants who had slower baseline reading speeds without the magnifier demonstrated significantly greater improvements in mean and maximum reading speeds on average with the magnifier (95% CI, 8 to 32 [ P = .003]; 95% CI, 4 to 36 [ P = .02]). A significantly greater number of pages were read with the new magnifier than without it (Wilcoxon z = -2.5; P = .01). A significantly greater number of pages were read with the magnifier by participants who read fewer pages at baseline (95% CI, 0.57 to 5.6; P = .02) or had greater improvements in mean reading speed (95% CI, 0.57 to 5.6; P = .007). CONCLUSIONS: Many visually impaired adults read more quickly and/or read a greater number of pages after using a new magnifier for a month than compared to without it. The largest gains occurred among those with more difficulty at baseline, indicating the potential to improve reading rates with magnifiers for those with greater deficits.

An Evaluation of a Clinic-Based Low Vision Device Lending Library.

As the efficacy of low vision service provision is facilitated by clients' access to and proper use of low vision devices, the objective of this study was to evaluate an outpatient clinic-based low vision device lending library program and the functional and psychosocial impact that device use had upon clients. Twenty individuals borrowed portable video magnifiers during the study period. Line items from the Revised-Self-Report Assessment of Functional Visual Performance and the Reading Behavior Inventory were analyzed before and after device loan at two months. The Psychosocial Impact of Assistive Devices Scale-10 and a semi-structured interview were also completed at two months. Reported improvements in reading performance and satisfaction levels on the Reading Behavior Inventory were significant (p<.001). The Revised-Self-Report Assessment of Functional Visual Performance indicated improved independence in reading medications, bills and labels. Higher scores in happiness, independence, sense of control and adaptability on the Psychosocial Impact of Assistive Devices Scale-10 indicated device retention at two months. Qualitative themes included improved independence, time needed to acclimate to the device, personal appraisal impacting motivation and challenges specific to low vision. This article provides occupational therapists a model to facilitate access, person-device fit and successful use of low vision devices to promote therapy outcomes.

Adaptation of food guideline messages for the visually impaired in Lima, Peru: a validation experience. / Adecuación de mensajes de las guías alimentarias para personas con discapacidad visual en Lima, Perú: una experiencia de validación.

This article aims to describe the design and validation process of food guideline messages written in the braille reading and writing system for people with visual impairment. The process involved the following stages: i) design and elaboration of the material containing the food guideline messages, as well as its adaptation to the format; ii) technical validation with representatives of the National Council for the Integration of Persons with Disabilities (CONADIS); and iii) operational validation through focus groups with children, adolescents and adults with visual disabilities who can read braille. The participants agreed on the legibility, clarity and comprehension of the messages and approved the format of the material. Finally, it should be noted that the participation of key informants and people with visual impairment in all stages was important for the development of an informative material with healthy eating messages in braille.

IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition.

This paper presents the design, development, and testing of an IoT-enabled smart stick for visually impaired people to navigate the outside environment with the ability to detect and warn about obstacles. The proposed design employs ultrasonic sensors for obstacle detection, a water sensor for sensing the puddles and wet surfaces in the user's path, and a high-definition video camera integrated with object recognition. Furthermore, the user is signaled about various hindrances and objects using voice feedback through earphones after accurately detecting and identifying objects. The proposed smart stick has two modes; one uses ultrasonic sensors for detection and feedback through vibration motors to inform about the direction of the obstacle, and the second mode is the detection and recognition of obstacles and providing voice feedback. The proposed system allows for switching between the two modes depending on the environment and personal preference. Moreover, the latitude/longitude values of the user are captured and uploaded to the IoT platform for effective tracking via global positioning system (GPS)/global system for mobile communication (GSM) modules, which enable the live location of the user/stick to be monitored on the IoT dashboard. A panic button is also provided for emergency assistance by generating a request signal in the form of an SMS containing a Google maps link generated with latitude and longitude coordinates and sent through an IoT-enabled environment. The smart stick has been designed to be lightweight, waterproof, size adjustable, and has long battery life. The overall design ensures energy efficiency, portability, stability, ease of access, and robust features.

An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People.

Visually impaired people face many challenges that limit their ability to perform daily tasks and interact with the surrounding world. Navigating around places is one of the biggest challenges that face visually impaired people, especially those with complete loss of vision. As the Internet of Things (IoT) concept starts to play a major role in smart cities applications, visually impaired people can be one of the benefitted clients. In this paper, we propose a smart IoT-based mobile sensors unit that can be attached to an off-the-shelf cane, hereafter a smart cane, to facilitate independent movement for visually impaired people. The proposed mobile sensors unit consists of a six-axis accelerometer/gyro, ultrasonic sensors, GPS sensor, cameras, a digital motion processor and a single credit-card-sized single-board microcomputer. The unit is used to collect information about the cane user and the surrounding obstacles while on the move. An embedded machine learning algorithm is developed and stored in the microcomputer memory to identify the detected obstacles and alarm the user about their nature. In addition, in case of emergencies such as a cane fall, the unit alerts the cane user and their guardian. Moreover, a mobile application is developed to be used by the guardian to track the cane user via Google Maps using a mobile handset to ensure safety. To validate the system, a prototype was developed and tested.

Sensor-Based Prototype of a Smart Assistant for Visually Impaired People-Preliminary Results.

People with visual impairment are the second largest affected category with limited access to assistive products. A complete, portable, and affordable smart assistant for helping visually impaired people to navigate indoors, outdoors, and interact with the environment is presented in this paper. The prototype of the smart assistant consists of a smart cane and a central unit; communication between user and the assistant is carried out through voice messages, making the system suitable for any user, regardless of their IT skills. The assistant is equipped with GPS, electronic compass, Wi-Fi, ultrasonic sensors, an optical sensor, and an RFID reader, to help the user navigate safely. Navigation functionalities work offline, which is especially important in areas where Internet coverage is weak or missing altogether. Physical condition monitoring, medication, shopping, and weather information, facilitate the interaction between the user and the environment, supporting daily activities. The proposed system uses different components for navigation, provides independent navigation systems for indoors and outdoors, both day and night, regardless of weather conditions. Preliminary tests provide encouraging results, indicating that the prototype has the potential to help visually impaired people to achieve a high level of independence in daily activities.

Convolutional Neural Network Based Real Time Arabic Speech Recognition to Arabic Braille for Hearing and Visually Impaired.

Natural Language Processing (NLP) is a group of theoretically inspired computer structures for analyzing and modeling clearly going on texts at one or extra degrees of linguistic evaluation to acquire human-like language processing for quite a few activities and applications. Hearing and visually impaired people are unable to see entirely or have very low vision, as well as being unable to hear completely or having a hard time hearing. It is difficult to get information since both hearing and vision, which are crucial organs for receiving information, are harmed. Hearing and visually impaired people are considered to have a substantial information deficit, as opposed to people who just have one handicap, such as blindness or deafness. Visually and hearing-impaired people who are unable to communicate with the outside world may experience emotional loneliness, which can lead to stress and, in extreme cases, serious mental illness. As a result, overcoming information handicap is a critical issue for visually and hearing-impaired people who want to live active, independent lives in society. The major objective of this study is to recognize Arabic speech in real time and convert it to Arabic text using Convolutional Neural Network-based algorithms before saving it to an SD card. The Arabic text is then translated into Arabic Braille characters, which are then used to control the Braille pattern via a Braille display with a solenoid drive. The Braille lettering triggered on the finger was deciphered by visually and hearing challenged participants who were proficient in Braille reading. The CNN, in combination with the ReLU model learning parameters, is fine-tuned for optimization, resulting in a model training accuracy of 90%. The tuned parameters model's testing results show that adding the ReLU activation function to the CNN model improves recognition accuracy by 84 % when speaking Arabic digits.