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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.

The global increase in the population of Visually Impaired People (VIPs) underscores the rapidly growing demand for a robust navigation system to provide safe navigation in diverse environments. State-of-the-art VIP navigation systems cannot achieve the required performance (accuracy, integrity, availability, and integrity) because of insufficient positioning capabilities and unreliable investigations of transition areas and complex environments (indoor, outdoor, and urban). The primary reason for these challenges lies in the segregation of Visual Impairment (VI) research within medical and engineering disciplines, impeding technology developers’ access to comprehensive user requirements. To bridge this gap, this paper conducts a comprehensive review covering global classifications of VI, international and regional standards for VIP navigation, fundamental VIP requirements, experimentation on VIP behavior, an evaluation of state-of-the-art positioning systems for VIP navigation and wayfinding, and ways to overcome difficulties during exceptional times such as COVID-19. This review identifies current research gaps, offering insights into areas requiring advancements. Future work and recommendations are presented to enhance VIP mobility, enable daily activities, and promote societal integration. This paper addresses the urgent need for high-performance navigation systems for the growing population of VIPs, highlighting the limitations of current technologies in complex environments. Through a comprehensive review of VI classifications, VIPs’ navigation standards, user requirements, and positioning systems, this paper identifies research gaps and offers recommendations to improve VIP mobility and societal integration.

Many individuals with disabilities face barriers to meaningful employment. Legislation has been put in place to ensure employment equity for individuals with disabilities in Canada. However, little is known about the employment profile and experiences of people with seeing disabilities. The objectives of our research study were to explore the employment rates of people with seeing disabilities in Canada, the factors associated with being employed, and supports and barriers that affect their work participation. We used the nationally representative data from the Canadian Survey on Disability (CSD) 2017, collected by Statistics Canada. The CSD is a national cross-sectional survey of Canadians 15 years of age and above who face a functional limitation due to a health-related condition, representing more than 6 million (n = 6,246,640) Canadians. Our analyses focused on people who reported having a seeing disability. A subset of the complete dataset was created, focusing on individuals with a seeing disability. Weighted descriptive analyses were performed using SPSS. Multivariate logistic regression analyses were conducted for individuals between 25-64 years of age to identify predictors of employment. Out of the estimated 892,220 working-age adults (25-64 years) with a seeing disability who were represented by the survey, 54% were employed, 6% were unemployed and 40% were not in the labour force. Early onset of seeing disability (OR: 1.33; 95% CI: 1.32-1.35), less severe seeing disability (OR: 1.51; 95% CI: 1.49-1.53), education above high school (OR: 2.00; 95% CI: 1.97-2.02) and daily use of the internet (OR: 2.46; 95% CI: 2.41-2.51) were positively related with employment. The top three employment accommodations that were needed and were made available included: modified work hours (45%); work from home (38.5%) and a modified workstation (37%). The top three needed but least available accommodations were technical aids (14%), communication aids (22%) and a computer with specialized software or adaptation (27%). Overall, 26% reported that an accommodation was required but was not made available by the employer. While 75% of individuals with a seeing disability were out of the labour force due to their condition, the remaining identified barriers that prevented them from working which included (top 3): (i) too few jobs available (20%); (ii) inadequate training/experience (19%), (iii) past attempts at finding employment were unsuccessful (19%). Adults with seeing disability in Canada experience lower labour force participation than the general population. Rigorous programs are required to assist them with the job search, job retraining and workplace accommodations. It is important for governments to improve efforts towards inclusive education and develop strategies that promote digital literacy of employees and job seekers with visual impairments. Although accessibility legislations have been put in place, programs should be established that provide accessibility solutions for various employers, enabling them to hire individuals with different abilities.

This study explores spatial perception of depth by employing a novel proof of concept sensory substitution algorithm. The algorithm taps into existing cognitive scaffolds such as language and cross modal correspondences by naming objects in the scene while representing their elevation and depth by manipulation of the auditory properties for each axis. While the representation of verticality utilized a previously tested correspondence with pitch, the representation of depth employed an ecologically inspired manipulation, based on the loss of gain and filtration of higher frequency sounds over distance. The study, involving 40 participants, seven of which were blind (5) or visually impaired (2), investigates the intrinsicness of an ecologically inspired mapping of auditory cues for depth by comparing it to an interchanged condition where the mappings of the two axes are swapped. All participants successfully learned to use the algorithm following a very brief period of training, with the blind and visually impaired participants showing similar levels of success for learning to use the algorithm as did their sighted counterparts. A significant difference was found at baseline between the two conditions, indicating the intuitiveness of the original ecologically inspired mapping. Despite this, participants were able to achieve similar success rates following the training in both conditions. The findings indicate that both intrinsic and learned cues come into play with respect to depth perception. Moreover, they suggest that by employing perceptual learning, novel sensory mappings can be trained in adulthood. Regarding the blind and visually impaired, the results also support the convergence view, which claims that with training, their spatial abilities can converge with those of the sighted. Finally, we discuss how the algorithm can open new avenues for accessibility technologies, virtual reality, and other practical applications.

Incidence of visual impairment (VI) and dyslipidemia is increasing with aging. Although good medication adherence (MA) is a crucial factor in achieving therapeutic goals for dyslipidemia, there is a paucity of studies measuring MA in the visually impaired with dyslipidemia. We investigated whether patients with VI had worse MA to dyslipidemia drugs than non-disabled people and determined the factors affecting MA among patients with VI. Data on dyslipidemia patients with VI were extracted in 2017 from the sample cohort database of the National Health Insurance Service. MA to dyslipidemia drugs was measured for two years based on the proportion of days covered (PDC). Conditional logistic regression analysis was performed to analyze the effect of VI on good MA (PDC ≥0.8). The VI group (0.860) had a larger PDC than the non-disabled group (0.850). The adjusted odds ratio (aOR) for good MA among VI vs. non-disabled individuals was statistically insignificant (1.137, 95% confidence interval:0.958–1.350). Significant factors for poor MA in the VI group were younger age (aOR for 20–39 vs. ≥75 years old: 0.124), lower income (aOR for 9-10th decile (rich) vs. 1-4 th decile (poor): 1.771), shorter duration of dyslipidemia (aOR for 1–4 vs. 15 years: 0.416), having lower-level providers sas their main providers (aOR for clinics vs. general/tertiary-care hospitals: 0.545), and having mental diseases (aOR: 0.679). Patients with VI did not have worse MA than non-disabled patients taking dyslipidemia medication.

Blind and visually impaired (BVI) people face significant challenges in perception, navigation, and safety during travel. Existing infrastructure (e.g., blind lanes) and traditional aids (e.g., walking sticks, basic audio feedback) provide limited flexibility and interactivity for complex environments. To solve this problem, we propose a real-time travel assistance system based on deep learning. The hardware comprises an NVIDIA Jetson Nano controller, an Intel D435i depth camera for environmental sensing, and SG90 servo motors for feedback. To address embedded device computational constraints, we developed a lightweight object detection and segmentation algorithm. Key innovations include a multi-scale attention feature extraction backbone, a dual-stream fusion module incorporating the Mamba architecture, and adaptive context-aware detection/segmentation heads. This design ensures high computational efficiency and real-time performance. The system workflow is as follows: (1) the D435i captures real-time environmental data; (2) the processor analyzes this data, converting obstacle distances and path deviations into electrical signals; (3) servo motors deliver vibratory feedback for guidance and alerts. Preliminary tests confirm that the system can effectively detect obstacles and correct path deviations in real time, suggesting its potential to assist BVI users. However, as this is a work in progress, comprehensive field trials with BVI participants are required to fully validate its efficacy.

Understanding the behavior and the challenges of visually impaired individuals in waste separation remains a significant research gap that affects the development of an inclusive and sustainable society. This research study aims to identify and analyze the factors influencing the waste separation behavior of visually impaired people and the key obstacles that these individuals face in public spaces. The study uses a quantitative approach, relying on surveys to gather data from a sample of 358 visually impaired individuals through the use of structured questionnaires. The data was then examined using multiple regression analysis. Results revealed that 91.1% possessed a moderate understanding of waste segregation, while 97.7% held generally positive attitudes toward the practice. Approximately 74.9% of the participants actively engaged in waste segregation at a moderate level. Motivation emerged as the most significant influence of proper waste management behavior, alongside attitude and knowledge. However, challenges persist in public settings, where 69.3% of participants discarded waste without regard to bin types, primarily due to difficulties in distinguishing bin colors and general ignorance. To better address their needs, participants recommended practical solutions, such as bins designed to be distinguishable by shape or sound, ensuring a more accessible and inclusive waste segregation system. This research can help in designing targeted interventions that promote sustainable waste management practices for visually impaired individuals. It can also provide valuable information to researchers, practitioners, and policymakers working towards creating inclusive and accessible waste management environments and processes for people with visual impairments.

The longstanding social disadvantages experienced by individuals with disabilities have garnered considerable attention from the scientific community and social movements worldwide. The political mobilization in defense of the rights of this minority group underscores the importance of understanding societal attitudes towards their inclusion. Our research aims to delve into Brazilian societal attitudes regarding the inclusion of people with visual impairments in various sectors of society. To achieve this objective, we developed a research program composed of four studies dedicated to validating the Scale of Support for Inclusive Policies (SSIP). Specifically, in Study 1 (N = 37), we developed the SSIP items and assessed their content validity. In Studies 2 (N = 176) and 3 (N = 116), exploratory and confirmatory factor analyses provided indications of the factorial validity and reliability of the scores obtained with the SSIP. Finally, Study 4 (N = 234) gathered evidence for the convergent and discriminant validity of the SSIP. Moreover, multigroup factor analysis was undertaken in Study 4, providing evidence of the instrument's configural, metric, and scalar invariance to measure the expression of support for inclusive policies towards people with visual impairments for men and women. Overall, the results strongly suggest that the SSIP is an effective and innovative tool for gauging individual differences in endorsing inclusive public policies, particularly for those with visual impairments. This instrument is anticipated to deepen our comprehension of how support for inclusive policies varies among individuals, thereby expanding research into attitudes towards disabilities broadly, beyond visual impairment.

Visual impairment affects millions worldwide, creating significant barriers to environmental interaction and independence. Existing assistive technologies often rely on cloud-based processing, raising privacy concerns and limiting accessibility in resource-constrained environments. This paper explores the integration and potential of open-source AI models in developing a fully offline assistive system that can be locally set up and operated to support visually impaired individuals. Built on a Raspberry Pi 5, the system combines real-time object detection (YOLOv8), optical character recognition (Tesseract), face recognition with voice-guided registration, and offline voice command control (VOSK), delivering hands-free multimodal interaction without dependence on cloud infrastructure. Audio feedback is generated using Piper for real-time environmental awareness. Designed to prioritize user privacy, low latency, and affordability, the platform demonstrates that effective assistive functionality can be achieved using only open-source tools on low-power edge hardware. Evaluation results in controlled conditions show 75–90% detection and recognition accuracies, with sub-second response times, confirming the feasibility of deploying such systems in privacy-sensitive or resource-constrained environments.

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.