Visual Echolocation Concept for the Colorophone Sensory Substitution Device Using Virtual Reality

The Topo-Speech sensory substitution system as a method of conveying spatial information to the blind and vision impaired

Humans, like most animals, integrate sensory input in the brain from different sensory modalities. Yet humans are distinct in their ability to grasp symbolic input, which is interpreted into a cognitive mental representation of the world. This representation merges with external sensory input, providing modality integration of a different sort. This study evaluates the Topo-Speech algorithm in the blind and visually impaired. The system provides spatial information about the external world by applying sensory substitution alongside symbolic representations in a manner that corresponds with the unique way our brains acquire and process information. This is done by conveying spatial information, customarily acquired through vision, through the auditory channel, in a combination of sensory (auditory) features and symbolic language (named/spoken) features. The Topo-Speech sweeps the visual scene or image and represents objects' identity by employing naming in a spoken word and simultaneously conveying the objects' location by mapping the x-axis of the visual scene or image to the time it is announced and the y-axis by mapping the location to the pitch of the voice. This proof of concept study primarily explores the practical applicability of this approach in 22 visually impaired and blind individuals. The findings showed that individuals from both populations could effectively interpret and use the algorithm after a single training session. The blind showed an accuracy of 74.45%, while the visually impaired had an average accuracy of 72.74%. These results are comparable to those of the sighted, as shown in previous research, with all participants above chance level. As such, we demonstrate practically how aspects of spatial information can be transmitted through non-visual channels. To complement the findings, we weigh in on debates concerning models of spatial knowledge (the persistent, cumulative, or convergent models) and the capacity for spatial representation in the blind. We suggest the present study's findings support the convergence model and the scenario that posits the blind are capable of some aspects of spatial representation as depicted by the algorithm comparable to those of the sighted. Finally, we present possible future developments, implementations, and use cases for the system as an aid for the blind and visually impaired.

Multi-obstacle aware smart navigation system for visually impaired people in fog connected IoT-cloud environment

Design of smart navigation for visually impaired/blind people is a hindering task. Existing researchers analyzed it in either indoor or outdoor environment and also it's failed to focus on optimum route selection, latency minimization and multi-obstacle presence. In order to overcome these challenges and to provide precise assistance to visually impaired people, this paper proposes smart navigation system for visually impaired people based on both image and sensor outputs of the smart wearable. The proposed approach involves the upcoming processes: (i) the input query of the visually impaired people (users) is improved by the query processor in order to achieve accurate assistance. (ii) The safest route from source to destination is provided by implementing Environment aware Bald Eagle Search Optimization algorithm in which multiple routes are identified and classified into three different classes from which the safest route is suggested to the users. (iii) The concept of fog computing is leveraged and the optimal fog node is selected in order to minimize the latency. The fog node selection is executed by using Nearest Grey Absolute Decision Making Algorithm based on multiple parameters. (iv) The retrieval of relevant information is performed by means of computing Euclidean distance between the reference and database information. (v) The multi-obstacle detection is carried out by YOLOv3 Tiny in which both the static and dynamic obstacles are classified into small, medium and large obstacles. (vi) The decision upon navigation is provided by implementing Adaptive Asynchronous Advantage Actor-Critic (A3C) algorithm based on fusion of both image and sensor outputs. (vii) Management of heterogeneous is carried out by predicting and pruning the fault data in the sensor output by minimum distance based extended kalman filter for better accuracy and clustering the similar information by implementing Spatial-Temporal Optics Clustering Algorithm to reduce complexity. The proposed model is implemented in NS 3.26 and the results proved that it outperforms other existing works in terms of obstacle detection and task completion time.

Smart Glass System Using Deep Learning for the Blind and Visually Impaired

Individuals suffering from visual impairments and blindness encounter difficulties in moving independently and overcoming various problems in their routine lives. As a solution, artificial intelligence and computer vision approaches facilitate blind and visually impaired (BVI) people in fulfilling their primary activities without much dependency on other people. Smart glasses are a potential assistive technology for BVI people to aid in individual travel and provide social comfort and safety. However, practically, the BVI are unable move alone, particularly in dark scenes and at night. In this study we propose a smart glass system for BVI people, employing computer vision techniques and deep learning models, audio feedback, and tactile graphics to facilitate independent movement in a night-time environment. The system is divided into four models: a low-light image enhancement model, an object recognition and audio feedback model, a salient object detection model, and a text-to-speech and tactile graphics generation model. Thus, this system was developed to assist in the following manner: (1) enhancing the contrast of images under low-light conditions employing a two-branch exposure-fusion network; (2) guiding users with audio feedback using a transformer encoder–decoder object detection model that can recognize 133 categories of sound, such as people, animals, cars, etc., and (3) accessing visual information using salient object extraction, text recognition, and refreshable tactile display. We evaluated the performance of the system and achieved competitive performance on the challenging Low-Light and ExDark datasets.

Colorophone 2.0: A Wearable Color Sonification Device Generating Live Stereo-Soundscapes-Design, Implementation, and Usability Audit

The successful development of a system realizing color sonification would enable auditory representation of the visual environment. The primary beneficiary of such a system would be people that cannot directly access visual information—the visually impaired community. Despite the plethora of sensory substitution devices, developing systems that provide intuitive color sonification remains a challenge. This paper presents design considerations, development, and the usability audit of a sensory substitution device that converts spatial color information into soundscapes. The implemented wearable system uses a dedicated color space and continuously generates natural, spatialized sounds based on the information acquired from a camera. We developed two head-mounted prototype devices and two graphical user interface (GUI) versions. The first GUI is dedicated to researchers, and the second has been designed to be easily accessible for visually impaired persons. Finally, we ran fundamental usability tests to evaluate the new spatial color sonification algorithm and to compare the two prototypes. Furthermore, we propose recommendations for the development of the next iteration of the system.