Search Results (318)

Indoor navigation remains challenging for students with visual impairments because GPS is unavailable indoors and building layouts are often complex. This paper presents a wearable marker-assisted navigation system integrating QR code localization, SSD MobileNet V3 obstacle detection, TFmini-S LiDAR ranging, A*-based dynamic route planning, and audio feedback on a Raspberry Pi 5. The main contribution is an analytical framework relating marker spacing to predicted localization uncertainty and defining a latency budget for obstacle warnings. A confidence-weighted sensor-fusion method is developed analytically but was not implemented in the evaluated prototype, in which the QR code, camera, and LiDAR channels operated independently. The proposed fusion method and the simulated multi-floor planning extension require further experimental validation. Controlled tests produced a mean positioning error below 1.2 m, a LiDAR ranging MAE of 8.3 cm, and an object-detection throughput of 6–9 FPS. A pilot field evaluation covered nine routes totalling 901 m across two buildings and included one participant with self-reported vision loss of approximately 95%. All route trials were completed, although some required researcher assistance. The system remains a proof of concept and has not yet been evaluated against a baseline or with a sufficiently large target-user sample. Full article

Background: Modern household appliances often present complex interfaces that can be difficult to use, especially for older adults, people with visual impairments, and users with mild cognitive difficulties. In such cases, interacting with appliances may require sustained attention, visuospatial search, working memory, and sequential action planning, while traditional user manuals often provide limited contextual support. Methods: To address this issue, this study presents a proof-of-concept augmented reality (AR) and artificial intelligence (AI)-based system for contextual appliance guidance. The proposed architecture integrates visual sensing, deep learning, and large language models to detect appliance controls, interpret user queries, retrieve relevant information from user manuals, and provide step-by-step guidance directly on the real interface. A YOLOv8 model trained on a custom dataset was used for button detection, YOLO-Seg was employed to enhance visual highlighting through segmentation, and BoT-SORT was used to maintain detection consistency across frames. A Unity-based mobile application displayed real-time AR overlays with customizable visual settings for accessibility needs, such as low vision and color blindness that may be relevant for future accessibility-oriented applications. In addition to its technical pipeline, the system is conceptually relevant as a potential form of external cognitive support because it transforms static manual instructions into situated, sequential, and visually grounded guidance.Results: Experimental results showed promising technical performance for button detection and segmentation, while a preliminary user evaluation in a non-clinical sample suggested good usability, clarity, and acceptability of the interface. Conclusions: These findings support the technical feasibility and preliminary usability of the approach, while cognitive workload, confidence, functional autonomy, and clinical benefit were not directly measured. Targeted validation in older adults, people with visual impairments, and clinical populations is therefore still needed. Future developments will include multimodal feedback, read-aloud guidance, and more specific evaluation of workload, confidence, and functional autonomy. Full article

Gait symmetry analysis has emerged as an important tool in rehabilitation engineering and neurological disorder assessment, as it provides clinically relevant indicators of mobility impairment and gait abnormalities. The proposed framework integrates gait symmetry variability, statistical gait features and Dynamic Time Warping (DTW)-based temporal alignment to enhance robustness against gait variations and irregular walking patterns. A hybrid feature vector comprising DTW similarity scores, the hybrid gait symmetry index (GSI), and statistical gait descriptors was employed to classify gait patterns into five categories: normal, slow, medium, fast, and abnormal. The system was implemented as a wearable edge-computing platform using an NI myRIO device equipped with a tri-axial Inertial Measurement Unit (IMU) mounted on the subject’s body. The onboard FPGA performs real-time signal preprocessing, GSI computation, feature extraction, constrained DTW matching, and gait classification using fixed-point streaming architectures and BRAM-based buffering. Meanwhile, the embedded ARM processor manages TCP/IP communication and transmits real-time gait information to a remote monitoring workstation via a WiFi interface for visualization and analysis. Operating at a clock frequency of 100 MHz, the complete architecture achieves an end-to-end processing latency of approximately 4 ms. The proposed FPGA-based implementation provides low-latency, energy-efficient, and real-time gait analysis, making it well suited for wearable rehabilitation systems, assistive healthcare devices, and continuous mobility monitoring applications. Full article

For people with visual impairments, navigating and orienting themselves in public spaces is a daily challenge. Although a wide variety of electronic assistive devices already exist, in the past these provided only fairly abstract guidance. With large language models (LLMs), natural-language descriptions of image content are now possible. While there are already approaches that capture images via smart glasses and forward the image content to external large language models to generate image descriptions, these methods raise concerns regarding data privacy and availability. This paper therefore presents a novel approach in which data is processed exclusively locally on the user’s device. To do this, the user captures a camera image via smart glasses, which is forwarded to a connected smartphone. Inference takes place directly on the smartphone using a reduced, local LLM that has been adapted through LoRA fine-tuning using data relevant to people with visual impairments. The generated image description is then played through a speaker in the temple of the smart glasses. For the task-specific fine-tuning, a comprehensive survey of visually impaired people was conducted, and professional mobility trainers were consulted. The study demonstrated the feasibility of fully local processing using smart glasses in combination with a smartphone. The evaluation showed that fine-tuning the small, local model yields improved image descriptions compared to the non-adapted model, some of which outperform a significantly larger external language model. Full article

Graphical representations of mathematical functions remain difficult to access for blind and visually impaired users, particularly in educational contexts where understanding the overall shape and behavior of a function is essential. This paper proposes an adaptive sonification method for mathematical function graphs designed for touchscreen-based exploration supported by continuous auditory feedback and voice interaction. The study focuses on balancing perceptual accessibility and auditory saturation by analyzing the influence of key sonification parameters, including auditory plateau width, slope length, and slope curvature. Two user experiments involving participants with different levels of visual impairment were conducted to evaluate the perceived usability of the proposed approach. The results indicate that usability improves with increasing auditory coverage only up to a certain threshold, beyond which excessive auditory density reduces perceptual clarity. The experimental results suggest that the preferred auditory plateau width is approximately 52 dp (about 9.8 mm), while the optimal slope length is approximately 84 dp (about 15.8 mm). In contrast, slope curvature has relatively little influence on subjective usability. The paper also introduces the Sonification Index (SI) and Auditory Saturation (AS), two quantitative measures that support the analysis and adaptive selection of sonification parameters based on graph complexity and device characteristics. Full article

Visually impaired persons (VIPs), including blind people, face persistent barriers when shopping in conventional retail environments, such as store navigation, product identification, price verification, and checkout. Although the number of assistive technologies addressing these challenges is increasing, their usability and their ability to support the entire shopping experience remain limited. This review analyses peer-reviewed literature published between 2003 and 2025 on assistive solutions for in-store shopping by VIPs. The systems reviewed are classified according to their dominant technological approach, including tag-based solutions such as RFID and NFC; computer vision marker-based systems using barcodes, QR codes, or AR markers; computer vision non-marker-based systems; and hybrid solutions. Beyond technical functionality, the review examines supported shopping tasks, interaction demands, infrastructural dependence, usability, and validation with end users. The analysis shows that current solutions often support only isolated parts of the shopping journey, depend on modified store infrastructure or reliable product databases, and are insufficiently evaluated with VIP users in real retail environments. Human–computer interaction factors, including cognitive load, trust, discretion, feedback modality, and compatibility with familiar devices, emerge as critical for adoption and practical usefulness. Future research should therefore prioritise scalable, user-centred, discreet, and low-burden solutions that require minimal environmental modification while supporting the shopping process more holistically. Such approaches are essential for improving autonomy, dignity, and equitable access to everyday retail services for VIPs. Full article

Th paper describes a low-cost solution forsignificantly increasing the efficiency of color perception testing for visual screening. The target areas are school and preschool children, people taking medical exams, testing in occupational medicine, and military recruitment. The research involved two stages: developing a virtual application for rapid and objective assisted testing of chromatic vision, and effectively testing about 30 subjects, of different social and age categories, for visual screening. We proved not the low cost, but also the high effectiveness of the method. This creates potential for a better way to prevent visual impairments, especially in children. Full article

Background/Objectives: Adult Polyglucosan Body Disease (APBD) is a rare neurodegenerative glycogen storage disorder characterized by progressive gait disturbance, sensory impairment, and balance dysfunction. Although rehabilitation is recommended for functional maintenance, evidence regarding robot-assisted gait training (RAGT) in APBD remains extremely limited. Methods: A 58-year-old man with progressive lower extremity sensory and motor symptoms was diagnosed with APBD in 2026. Neurological examination revealed severe proprioceptive impairment in both great toes, generalized sensory deficits, gait instability, and impaired balance. Functional assessment demonstrated mild balance impairment with generally preserved muscle strength except for mild weakness in the lower extremities. RAGT was initiated and performed for 19 sessions over approximately 6 weeks in combination with conventional rehabilitation therapy, including gait and balance training with visual feedback. Results: Following the combined rehabilitation program, improvements were observed in balance function, postural stability and proprioceptive function. Conclusions: This case suggests that RAGT combined with conventional rehabilitation may improve balance and gait-related function in patients with APBD. Repetitive task-specific gait training with enhanced sensory feedback may be particularly beneficial in APBD, where proprioceptive impairment and sensory ataxia are major contributors to gait dysfunction; however, this remains a hypothesis that requires validation in future studies. This report highlights the feasibility and potential applicability of RAGT in rare neurodegenerative disorders such as APBD. Full article

Human-computer interaction (HCI) exploits various input methods to improve user experience, but those without visual access suffer more than the mainstream. In this context, this paper proposes a novel Gesture-based Human-Computer Interaction (GCI) system for visually impaired people (VIP). However, a large set of gestures introduces complexity, which poses challenges for VIP to interact with computers. Therefore, an accessible assistive application with a minimal set of gestures is designed here. Nineteen (19) participants engaged, and several dimensions were evaluated, including skin conductance, NASA-TLX, and performance indicators. The gesture response time revealed that the proposed GCI technique is 39% faster than the existing technique. In addition, the skin conductance revealed a modest reduction, which means GCI caused a more relaxed reaction than the existing technique. GCI demonstrated significant statistical advantages in gesture response time, skin conductance, and forgot word count, while other measures showed comparable performance between the two techniques. GCI provides a more efficient and cognitively favorable interaction experience which opens a new era in the design and development of assistive technologies for VIP. Full article

Keratoconus, a progressive corneal ectasia, remains a major cause of irreversible visual impairment worldwide. Conventional corneal cross-linking (CXL) with riboflavin/ultraviolet A (UVA) has revolutionized clinical management, yet its efficacy is still constrained by epithelial barriers, oxygen dependence, and safety concerns in thin corneas. Emerging nanotechnology provides a transformative opportunity to overcome these bottlenecks. This review highlights the enhancement of riboflavin delivery efficiency by nanocarriers, the photodynamic optimization of nano-enhanced cross-linking agents, and the synergistic strengthening effect of nanocomposites on corneal mechanical strength. We emphasize not only their potential to enhance drug penetration, improve cross-linking efficiency, and extend clinical indications, but also their role in advancing toward a new generation of personalized, intelligent, and minimally invasive corneal therapy. Finally, we discuss translational challenges, including manufacturing, long-term biosafety, and regulatory frameworks, and present a theoretical roadmap that integrates nanotechnology, real-time imaging, and artificial intelligence (AI)-assisted decision-making to achieve a closed-loop “sense–decide–act” therapeutic system. By situating nanomaterial-enhanced CXL within precision ophthalmology, this review highlights its capacity to redefine the standard of care for keratoconus and related ectatic disorders. Full article

Background: Acute pain after video-assisted thoracoscopic surgery (VATS) promotes respiratory splinting, impaired cough, and pulmonary complications, and predicts persistent opioid use. Surgeon-administered intercostal nerve block (ICNB) is a simple regional technique, but its independent effect on early pain and opioid requirement in a contemporary uniportal VATS (UVATS) pathway is incompletely defined. Methods: We performed a retrospective cohort study of 456 consecutive patients undergoing UVATS at a single Israeli center between 2017 and 30 May 2025. Patients receiving an intercostal block were compared with those who did not. Baseline covariates were balanced by 1:1 nearest-neighbor propensity-score matching (caliper 0.2 SD of the logit propensity score). The primary endpoints were pain on postoperative day (POD) 1 (visual analog scale, VAS) and postoperative opioid use; secondary endpoints included later pain, analgesic regimen, postoperative pneumonia, and mortality. Results: Matching yielded 159 patients per group (n = 318) with all clinically relevant covariates balanced (standardized mean difference [SMD] < 0.13). Median POD1 VAS was lower with the block (4 [IQR 3–4] vs. 5 [5–7]; p < 0.001), and 76.1% of block patients were opioid-free versus 10.7% who were not (p < 0.001). The effect was concentrated early and attenuated by POD3. In multivariable analysis the block was independently associated with lower POD1 VAS (adjusted β = −1.64, 95% CI −2.00 to −1.29; p < 0.001). Postoperative pneumonia was less frequent in the block group (5.7% vs. 20.1%; p < 0.001). Thirty-day and one-year mortality did not differ significantly. Conclusions: In UVATS, a surgeon-placed intercostal nerve block was associated with lower early postoperative pain that persisted after adjustment for operating surgeon and surgical era, concordant with pooled meta-analytic estimates; associated reductions in opioid use and pneumonia were confounded with surgeon and secular trend and are hypothesis-generating. These single-center, retrospective findings require prospective, protocol-randomized confirmation. Full article

Visually impaired individuals face significant challenges in autonomous mobility and situational awareness. Most existing assistive technologies address isolated tasks, such as object recognition or text reading, while failing to capture broader environmental context. This work addresses this limitation by proposing a scene-sensitive, low-cost assistive system that delivers holistic situational information. We present Vocal-Eyes, an intelligent smart glasses platform that provides periodic audio descriptions of the surrounding environment. The system employs a cloud-based neural processing pipeline in which visual features are extracted using a Transformer-based architecture. Relational context is modeled through scene graph generation, and scene graphs are translated into natural language via a graph-to-text module. A lightweight hardware prototype captures visual data locally, while computationally intensive processing is offloaded to the cloud to reduce power consumption. The experimental results show that relational, scene-based narration produces more coherent and informative descriptions than object-centric approaches while maintaining acceptable periodic latency. Cost analysis further indicates that Vocal-Eyes is significantly more affordable than comparable commercial smart glasses solutions. These results demonstrate that Transformer-based scene understanding with cloud-assisted processing is an effective and practical approach for developing accessible, context-aware assistive technologies for visually impaired users. Full article

Visual impairment affects approximately 2.2 billion people worldwide, yet existing assistive technologies remain fragmented and prohibitively expensive. This paper presents Munir, an integrated multimodal assistive system designed to enhance human–computer interaction through a combination of a mobile application and Bluetooth-enabled smart glasses. Munir leverages a hybrid machine learning architecture to provide inclusive, real-time support for daily living activities. The system integrates ten core capabilities—including face recognition, optical character recognition, and scene description—all accessible through a unified bilingual (Arabic/English) voice interface. By employing on-device processing for biometric tasks, Munir ensures user privacy and trust while maintaining high responsiveness. End-to-end system evaluation on the SCface dataset achieves a 96.69% recognition rate with 0% False Accept Rate. At an estimated first-year total cost of $806, Munir demonstrates a 4–5× cost advantage over commercial alternatives, providing a scalable and affordable multimodal solution for global digital inclusion. Full article

Traditional white canes offer a limited perception range, whereas end-to-end visual models face challenges in real-time deployment on edge devices. To address these limitations, this paper proposes a lightweight real-time assistive system that integrates geometric topology reconstruction with state-adaptive warning logic. The system utilizes YOLOv9 to extract discrete semantic primitives of tactile paving. It constructs a dual-branch perception framework based on Median Absolute Deviation and the Minimum Spanning Tree algorithm to analyze the topological structure of tactile paving. For complex intersections characterized by warning indicators, a one-dimensional connectivity clustering algorithm based on longitudinal topology is proposed. It generates accurate macroscopic feasible directional prompts under field-of-view boundary constraints. Additionally, a hierarchical scheduling framework dynamically orchestrates scenario-specific finite state machines to enable continuous dynamic interaction across typical high-risk scenarios. Evaluated on a custom real-world dataset, the system achieves a 95.21% frame-level comprehensive accuracy for straight-path deviation correction and intersection directional prompting. Dynamic temporal stress tests confirm the temporal stability and logical coherence of state transitions. Furthermore, latency evaluations demonstrate the logic layer’s minimal computational overhead, proving its theoretical feasibility for real-time edge deployment. This approach provides an effective, low-latency solution for delivering directional prompts and hazard warnings to visually impaired users. Full article

Currency recognition is a vital pillar for the financial independence of visually impaired individuals, yet existing solutions often struggle with the trade-off between architectural complexity and real-time performance. This paper introduces a lightweight hybrid framework specifically engineered for Saudi Riyal banknote identification. The primary contribution lies in the strategic integration of MobileNetV2 for deep feature extraction with a kernel-based Support Vector Machine to enhance classification boundaries. Furthermore, this study addresses a significant data gap by curating an updated dataset that includes the 20 SR denomination, which is largely missing from current public repositories. Methodologically, the framework emphasizes computational efficiency without compromising precision, achieving a robust test accuracy of 98.16. By prioritizing a streamlined architecture, this work provides a scalable and effective solution for mobile-based assistive technologies, fostering greater accessibility and autonomy for the visually impaired community in Saudi Arabia. Full article