63 research outputs found
State of the art review on walking support system for visually impaired people
The technology for terrain detection and walking support system for blind people has
rapidly been improved the last couple of decades but to assist visually impaired people may have
started long ago. Currently, a variety of portable or wearable navigation system is available in the
market to help the blind for navigating their way in his local or remote area. The focused
category in this work can be subgroups as electronic travel aids (ETAs), electronic orientation
aids (EOAs) and position locator devices (PLDs). However, we will focus mainly on electronic
travel aids (ETAs). This paper presents a comparative survey among the various portable or
wearable walking support systems as well as informative description (a subcategory of ETAs or
early stages of ETAs) with its working principal advantages and disadvantages so that the
researchers can easily get the current stage of assisting blind technology along with the
requirement for optimising the design of walking support system for its users
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
Helping the Blind to Get through COVID-19: Social Distancing Assistant Using Real-Time Semantic Segmentation on RGB-D Video
The current COVID-19 pandemic is having a major impact on our daily lives. Social distancing is one of the measures that has been implemented with the aim of slowing the spread of the disease, but it is difficult for blind people to comply with this. In this paper, we present a system that helps blind people to maintain physical distance to other persons using a combination of RGB and depth cameras. We use a real-time semantic segmentation algorithm on the RGB camera to detect where persons are and use the depth camera to assess the distance to them; then, we provide audio feedback through bone-conducting headphones if a person is closer than 1.5 m. Our system warns the user only if persons are nearby but does not react to non-person objects such as walls, trees or doors; thus, it is not intrusive, and it is possible to use it in combination with other assistive devices. We have tested our prototype system on one blind and four blindfolded persons, and found that the system is precise, easy to use, and amounts to low cognitive load
Recommended from our members
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
Recommended from our members
Mobile assistive technologies for the visually impaired
There are around 285 million visually impaired people worldwide, and around 370,000 people are registered as blind or partially sighted in the UK. Ongoing advances in information technology (IT) are increasing the scope for IT-based mobile assistive technologies to facilitate the independence, safety, and improved quality of life of the visually impaired. Research is being directed at making mobile phones and other handheld devices accessible via our haptic (touch) and audio sensory channels. We review research and innovation within the field of mobile assistive technology for the visually impaired and, in so doing, highlight the need for successful collaboration between clinical expertise, computer science, and domain users to realize fully the potential benefits of such technologies. We initially reflect on research that has been conducted to make mobile phones more accessible to people with vision loss. We then discuss innovative assistive applications designed for the visually impaired that are either delivered via mainstream devices and can be used while in motion (e.g., mobile phones) or are embedded within an environment that may be in motion (e.g., public transport) or within which the user may be in motion (e.g., smart homes)
A Smart Context-Aware Hazard Attention System to Help People with Peripheral Vision Loss
Peripheral vision loss results in the inability to detect objects in the peripheral visual field which affects the ability to evaluate and avoid potential hazards. A different number of assistive navigation systems have been developed to help people with vision impairments using wearable and portable devices. Most of these systems are designed to search for obstacles and provide safe navigation paths for visually impaired people without any prioritisation of the degree of danger for each hazard. This paper presents a new context-aware hybrid (indoor/outdoor) hazard classification assistive technology to help people with peripheral vision loss in their navigation using computer-enabled smart glasses equipped with a wide-angle camera. Our proposed system augments users’ existing healthy vision with suitable, meaningful and smart notifications to attract the user’s attention to possible obstructions or hazards in their peripheral field of view. A deep learning object detector is implemented to recognise static and moving objects in real time. After detecting the objects, a Kalman Filter multi-object tracker is used to track these objects over time to determine the motion model. For each tracked object, its motion model represents its way of moving around the user. Motion features are extracted while the object is still in the user’s field of vision. These features are then used to quantify the danger using five predefined hazard classes using a neural network-based classifier. The classification performance is tested on both publicly available and private datasets and the system shows promising results with up to 90% True Positive Rate (TPR) associated with as low as 7% False Positive Rate (FPR), 13% False Negative Rate (FNR) and an average testing Mean Square Error (MSE) of 8.8%. The provided hazard type is then translated into a smart notification to increase the user’s cognitive perception using the healthy vision within the visual field. A participant study was conducted with a group of patients with different visual field defects to explore their feedback about the proposed system and the notification generation stage. The real-world outdoor evaluation of human subjects is planned to be performed in our near future work
Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired
This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the
energy autonomy in the dierent operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects oered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%
Computational Personalization through Physical and Aesthetic Featured Digital Fabrication
Thesis (Master of Science in Informatics)--University of Tsukuba, no. 41269, 2019.3.2
Instructional eLearning technologies for the vision impaired
The principal sensory modality employed in learning is vision, and that not only increases the difficulty for vision impaired students from accessing existing educational media but also the new and mostly visiocentric learning materials being offered through on-line delivery mechanisms. Using as a reference Certified Cisco Network Associate (CCNA) and IT Essentials courses, a study has been made of tools that can access such on-line systems and transcribe the materials into a form suitable for vision impaired learning. Modalities employed included haptic, tactile, audio and descriptive text. How such a multi-modal approach can achieve equivalent success for the vision impaired is demonstrated. However, the study also shows the limits of the current understanding of human perception, especially with respect to comprehending two and three dimensional objects and spaces when there is no recourse to vision
- …