A Systematic Review of Urban Navigation Systems for Visually Impaired People

Blind and Visually impaired people (BVIP) face a range of practical difficulties when undertaking outdoor journeys as pedestrians. Over the past decade, a variety of assistive devices have been researched and developed to help BVIP navigate more safely and independently. In~addition, research in overlapping domains are addressing the problem of automatic environment interpretation using computer vision and machine learning, particularly deep learning, approaches. Our aim in this article is to present a comprehensive review of research directly in, or relevant to, assistive outdoor navigation for BVIP. We breakdown the navigation area into a series of navigation phases and tasks. We then use this structure for our systematic review of research, analysing articles, methods, datasets and current limitations by task. We also provide an overview of commercial and non-commercial navigation applications targeted at BVIP. Our review contributes to the body of knowledge by providing a comprehensive, structured analysis of work in the domain, including the state of the art, and guidance on future directions. It will support both researchers and other stakeholders in the domain to establish an informed view of research progress

Review of substitutive assistive tools and technologies for people with visual impairments: recent advancements and prospects

YesThe development of many tools and technologies for people with visual impairment has become a major priority in the field of assistive technology research. However, many of these technology advancements have limitations in terms of the human aspects of the user experience (e.g., usability, learnability, and time to user adaptation) as well as difficulties in translating research prototypes into production. Also, there was no clear distinction between the assistive aids of adults and children, as well as between “partial impairment” and “total blindness”. As a result of these limitations, the produced aids have not gained much popularity and the intended users are still hesitant to utilise them. This paper presents a comprehensive review of substitutive interventions that aid in adapting to vision loss, centred on laboratory research studies to assess user-system interaction and system validation. Depending on the primary cueing feedback signal offered to the user, these technology aids are categorized as visual, haptics, or auditory-based aids. The context of use, cueing feedback signals, and participation of visually impaired people in the evaluation are all considered while discussing these aids. Based on the findings, a set of recommendations is suggested to assist the scientific community in addressing persisting challenges and restrictions faced by both the totally blind and partially sighted people

Wearable obstacle avoidance electronic travel aids for blind and visually impaired individuals : a systematic review

Background Wearable obstacle avoidance electronic travel aids (ETAs) have been developed to assist the safe displacement of blind and visually impaired individuals (BVIs) in indoor/outdoor spaces. This systematic review aimed to understand the strengths and weaknesses of existing ETAs in terms of hardware functionality, cost, and user experience. These elements may influence the usability of the ETAs and are valuable in guiding the development of superior ETAs in the future. Methods Formally published studies designing and developing the wearable obstacle avoidance ETAs were searched for from six databases from their inception to April 2023. The PRISMA 2020 and APISSER guidelines were followed. Results Eighty-nine studies were included for analysis, 41 of which were judged to be of moderate to high quality. Most wearable obstacle avoidance ETAs mainly depend on camera- and ultrasonic-based techniques to achieve perception of the environment. Acoustic feedback was the most common human-computer feedback form used by the ETAs. According to user experience, the efficacy and safety of the device was usually their primary concern. Conclusions Although many conceptualised ETAs have been designed to facilitate BVIs' independent navigation, most of these devices suffer from shortcomings. This is due to the nature and limitations of the various processors, environment detection techniques and human-computer feedback those ETAs are equipped with. Integrating multiple techniques and hardware into one ETA is a way to improve performance, but there is still a need to address the discomfort of wearing the device and the high-cost. Developing an applicable systematic review guideline along with a credible quality assessment tool for these types of studies is also required. © 2013 IEEE

Smart Assistive Technology for People with Visual Field Loss

Visual field loss results in the lack of ability to clearly see objects in the surrounding environment, which affects the ability to determine potential hazards. In visual field loss, parts of the visual field are impaired to varying degrees, while other parts may remain healthy. This defect can be debilitating, making daily life activities very stressful. Unlike blind people, people with visual field loss retain some functional vision. It would be beneficial to intelligently augment this vision by adding computer-generated information to increase the users' awareness of possible hazards by providing early notifications. This thesis introduces a smart hazard attention system to help visual field impaired people with their navigation using smart glasses and a real-time hazard classification system. This takes the form of a novel, customised, machine learning-based hazard classification system that can be integrated into wearable assistive technology such as smart glasses. The proposed solution provides early notifications based on (1) the visual status of the user and (2) the motion status of the detected object. The presented technology can detect multiple objects at the same time and classify them into different hazard types. The system design in this work consists of four modules: (1) a deep learning-based object detector to recognise static and moving objects in real-time, (2) a Kalman Filter-based multi-object tracker to track the detected objects over time to determine their motion model, (3) a Neural Network-based classifier to determine the level of danger for each hazard using its motion features extracted while the object is in the user's field of vision, and (4) a feedback generation module to translate the hazard level into a smart notification to increase user's cognitive perception using the healthy vision within the visual field. For qualitative system testing, normal and personalised defected vision models were implemented. The personalised defected vision model was created to synthesise the visual function for the people with visual field defects. Actual central and full-field test results were used to create a personalised model that is used in the feedback generation stage of this system, where the visual notifications are displayed in the user's healthy visual area. The proposed solution will enhance the quality of life for people suffering from visual field loss conditions. This non-intrusive, wearable hazard detection technology can provide obstacle avoidance solution, and prevent falls and collisions early with minimal information

Optical flow estimation using steered-L1 norm

Motion is a very important part of understanding the visual picture of the surrounding environment. In image processing it involves the estimation of displacements for image points in an image sequence. In this context dense optical flow estimation is concerned with the computation of pixel displacements in a sequence of images, therefore it has been used widely in the field of image processing and computer vision. A lot of research was dedicated to enable an accurate and fast motion computation in image sequences. Despite the recent advances in the computation of optical flow, there is still room for improvements and optical flow algorithms still suffer from several issues, such as motion discontinuities, occlusion handling, and robustness to illumination changes. This thesis includes an investigation for the topic of optical flow and its applications. It addresses several issues in the computation of dense optical flow and proposes solutions. Specifically, this thesis is divided into two main parts dedicated to address two main areas of interest in optical flow. In the first part, image registration using optical flow is investigated. Both local and global image registration has been used for image registration. An image registration based on an improved version of the combined Local-global method of optical flow computation is proposed. A bi-lateral filter was used in this optical flow method to improve the edge preserving performance. It is shown that image registration via this method gives more robust results compared to the local and the global optical flow methods previously investigated. The second part of this thesis encompasses the main contribution of this research which is an improved total variation L1 norm. A smoothness term is used in the optical flow energy function to regularise this function. The L1 is a plausible choice for such a term because of its performance in preserving edges, however this term is known to be isotropic and hence decreases the penalisation near motion boundaries in all directions. The proposed improved L1 (termed here as the steered-L1 norm) smoothness term demonstrates similar performance across motion boundaries but improves the penalisation performance along such boundaries

An Adaptive Guidance System for Robotic Walking Aids

In the last years, several robotic walking aids to assist elderly users with mobility constraints and thus to react to the growing number of elderly persons in our society have been developed. In order to ensure good support for the user, the robotic walker should adapt to the motion of the user while at the same time not losing the target out of sight. Even though some of the existing active robotic walkers are able to guide their user to a target, during guidance, the input of the user is not considered sufficiently. Therefore a new adaptive guidance system for robotic walkers has been developed. It is able to lead the walking aid user to a given target while considering his inputs during guidance and adapting the path respectively. The guidance system has been implemented on the mobile robot assistant Care-O-bot II and a field test was done in an old people’s residence proving the correct function and usefulness of the guidance system

Iterative Design and Prototyping of Computer Vision Mediated Remote Sighted Assistance

Remote sighted assistance (RSA) is an emerging navigational aid for people with visual impairments (PVI). Using scenario-based design to illustrate our ideas, we developed a prototype showcasing potential applications for computer vision to support RSA interactions. We reviewed the prototype demonstrating real-world navigation scenarios with an RSA expert, and then iteratively refined the prototype based on feedback. We reviewed the refined prototype with 12 RSA professionals to evaluate the desirability and feasibility of the prototyped computer vision concepts. The RSA expert and professionals were engaged by, and reacted insightfully and constructively to the proposed design ideas. We discuss what we learned about key resources, goals, and challenges of the RSA prosthetic practice through our iterative prototype review, as well as implications for the design of RSA systems and the integration of computer vision technologies into RSA

Perception and manipulation for robot-assisted dressing

Assistive robots have the potential to provide tremendous support for disabled and elderly people in their daily dressing activities. This thesis presents a series of perception and manipulation algorithms for robot-assisted dressing, including: garment perception and grasping prior to robot-assisted dressing, real-time user posture tracking during robot-assisted dressing for (simulated) impaired users with limited upper-body movement capability, and finally a pipeline for robot-assisted dressing for (simulated) paralyzed users who have lost the ability to move their limbs. First, the thesis explores learning suitable grasping points on a garment prior to robot-assisted dressing. Robots should be endowed with the ability to autonomously recognize the garment state, grasp and hand the garment to the user and subsequently complete the dressing process. This is addressed by introducing a supervised deep neural network to locate grasping points. To reduce the amount of real data required, which is costly to collect, the power of simulation is leveraged to produce large amounts of labeled data. Unexpected user movements should be taken into account during dressing when planning robot dressing trajectories. Tracking such user movements with vision sensors is challenging due to severe visual occlusions created by the robot and clothes. A probabilistic real-time tracking method is proposed using Bayesian networks in latent spaces, which fuses multi-modal sensor information. The latent spaces are created before dressing by modeling the user movements, taking the user's movement limitations and preferences into account. The tracking method is then combined with hierarchical multi-task control to minimize the force between the user and the robot. The proposed method enables the Baxter robot to provide personalized dressing assistance for users with (simulated) upper-body impairments. Finally, a pipeline for dressing (simulated) paralyzed patients using a mobile dual-armed robot is presented. The robot grasps a hospital gown naturally hung on a rail, and moves around the bed to finish the upper-body dressing of a hospital training manikin. To further improve simulations for garment grasping, this thesis proposes to update more realistic physical properties values for the simulated garment. This is achieved by measuring physical similarity in the latent space using contrastive loss, which maps physically similar examples to nearby points.Open Acces