OMap: An assistive solution for identifying and localizing objects in a semi-structured environment

A system capable of detection and localization of objects of interest in a semi-structured environment will enhance the quality of life of people who are blind or visually impaired. Towards building such a system, this thesis presents a personalized real-time system called O\u27Map that finds misplaced/moved personal items and localizes them with respect to known landmarks. First, we adopted a participatory design approach to identify users’ need and functionalities of the system. Second, we used the concept from system thinking and design thinking to develop a real-time object recognition engine that was optimized to run on low form factor devices. The object recognition engine finds robust correspondences between the query image and item templates using K-D tree of invariant feature descriptor with two nearest neighbors and ratio test. Quantitative evaluation demonstrates that O\u27Map identifies object of interest with an average F-measure of 0.9650

A Hybrid Visual Control Scheme to Assist the Visually Impaired with Guided Reaching Tasks

In recent years, numerous researchers have been working towards adapting technology developed for robotic control to use in the creation of high-technology assistive devices for the visually impaired. These types of devices have been proven to help visually impaired people live with a greater degree of confidence and independence. However, most prior work has focused primarily on a single problem from mobile robotics, namely navigation in an unknown environment. In this work we address the issue of the design and performance of an assistive device application to aid the visually-impaired with a guided reaching task. The device follows an eye-in-hand, IBLM visual servoing configuration with a single camera and vibrotactile feedback to the user to direct guided tracking during the reaching task. We present a model for the system that employs a hybrid control scheme based on a Discrete Event System (DES) approach. This approach avoids significant problems inherent in the competing classical control or conventional visual servoing models for upper limb movement found in the literature. The proposed hybrid model parameterizes the partitioning of the image state-space that produces a variable size targeting window for compensatory tracking in the reaching task. The partitioning is created through the positioning of hypersurface boundaries within the state space, which when crossed trigger events that cause DES-controller state transition that enable differing control laws. A set of metrics encompassing, accuracy ($D$), precision ($\theta_{e}$), and overall tracking performance ($\psi$) are also proposed to quantity system performance so that the effect of parameter variations and alternate controller configurations can be compared. To this end, a prototype called \texttt{aiReach} was constructed and experiments were conducted testing the functional use of the system and other supporting aspects of the system behaviour using participant volunteers. Results are presented validating the system design and demonstrating effective use of a two parameter partitioning scheme that utilizes a targeting window with additional hysteresis region to filtering perturbations due to natural proprioceptive limitations for precise control of upper limb movement. Results from the experiments show that accuracy performance increased with the use of the dual parameter hysteresis target window model ($0.91 \leq D \leq 1$, $\mu(D)=0.9644$, $\sigma(D)=0.0172$) over the single parameter fixed window model ($0.82 \leq D \leq 0.98$, $\mu(D)=0.9205$, $\sigma(D)=0.0297$) while the precision metric, $\theta_{e}$, remained relatively unchanged. In addition, the overall tracking performance metric produces scores which correctly rank the performance of the guided reaching tasks form most difficult to easiest

Design, modeling and analysis of object localization through acoustical signals for cognitive electronic travel aid for blind people

El objetivo de la tesis consiste en el estudio y análisis de la localización de objetos en el entorno real mediante sonidos, así como la posterior integración y ensayo de un dispositivo real basado en tal técnica y destinado a personas con discapacidad visual. Con el propósito de poder comprender y analizar la localización de objetos se ha realizado un profundo estado de arte sobre los Sistemas de Navegación desarrollados durante las últimas décadas y orientados a personas con distintos grados de discapacidad visual. En el citado estado del arte, se han analizado y estructurado los dispositivos de navegación existentes, clasificándolos de acuerdo con los componentes de adquisición de datos del entorno utilizados. A este respecto, hay que señalar que, hasta el momento, se conocen tres clases de dispositivos de navegación: 'detectores de obstáculos', que se basan en dispositivos de ultrasonidos y sensores instalados en los dispositivos electrónicos de navegación con el objetivo de detectar los objetos que aparecen en el área de trabajo del sistema; 'sensores del entorno' - que tienen como objetivo la detección del objeto y del usuario. Esta clase de dispositivos se instalan en las estaciones de autobús, metro, tren, pasos de peatones etc., de forma que cuando el sensor del usuario penetra en el área de alcance de los sensores instalados en la estación, éstos informan al usuario sobre la presencia de la misma. Asimismo, el sensor del usuario detecta también los medios de transporte que tienen instalado el correspondiente dispositivo basado en láser o ultrasonidos, ofreciendo al usuario información relativa a número de autobús, ruta etc La tercera clase de sistemas electrónicos de navegación son los 'dispositivos de navegación'. Estos elementos se basan en dispositivos GPS, indicando al usuario tanto su locación, como la ruta que debe seguir para llegar a su punto de destino. Tras la primera etapa de elaboración del estaDunai ., L. (2010). Design, modeling and analysis of object localization through acoustical signals for cognitive electronic travel aid for blind people [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8441Palanci

Coaching Imagery to Athletes with Aphantasia

We administered the Plymouth Sensory Imagery Questionnaire (Psi-Q) which tests multi-sensory imagery, to athletes (n=329) from 9 different sports to locate poor/aphantasic (baseline scores <4.2/10) imagers with the aim to subsequently enhance imagery ability. The low imagery sample (n=27) were randomly split into two groups who received the intervention: Functional Imagery Training (FIT), either immediately, or delayed by one month at which point the delayed group were tested again on the Psi-Q. All participants were tested after FIT delivery and six months post intervention. The delayed group showed no significant change between baseline and the start of FIT delivery but both groups imagery score improved significantly (p=0.001) after the intervention which was maintained six months post intervention. This indicates that imagery can be trained, with those who identify as having aphantasia (although one participant did not improve on visual scores), and improvements maintained in poor imagers. Follow up interviews (n=22) on sporting application revealed that the majority now use imagery daily on process goals. Recommendations are given for ways to assess and train imagery in an applied sport setting

Wearable electronic orientation aid in straight paths for visually impaired people

This project makes use of artificial vision, specifically of different image processing techniques in order to build a guidance aid for visually impaired people. While several navigation systems based on ultra-sound sensors have been developed, it is not the case of real time navigation systems based on edge detection techniques. Therefore the current project builds a prototype based on artificial vision, which is easily wearable. It is light and little cumbersome. The main assumption of this guidance system is that there are many straight paths in different real world scenarios. These straight paths have parallel edges, which when captured as an image seem to converge to a single point called vanishing point. In the current project the vanishing point is calculated as a the crossing point between the two straight lines defining the path and a decision is taken based on the position of this point. This control algorithm has been developed using LabVIEW Real-Time software and has been deployed afterwards in a Real-Time target. The prototype notifies (via an audio record) the visually impaired person about his deviation from the straight path. It also notifies if the person is completely out of the way with an alarm sound. This guidance system consists of an electronic device (which contains a real time processor along with an FPGA), a webcam, a battery and some earphones. In this project the FPGA is used for parallel playback of audio speech while vision processes take place in the Real- Time processor.Este proyecto hace uso de visión artificial, específicamente de distintas técnicas de procesamiento de imagen para construir un sistema de asistencia a la navegación para personas con discapacidades visuales. Mientras muchos sistemas de navegación basados en sensores de ultra sonido se han desarrollado, no es el caso de sistemas de navegación en tiempo real basados en técnicas de detección de bordes. Por tanto, en el presente proyecto se construye un prototipo basado en visión artificial, que es fácilmente portable. Es ligero y se puede llevar puesto cómodamente. La principal suposición de este sistema de guiado es que hay muchos caminos rectos en distintos escenarios del mundo real. Estos caminos rectos poseen bordes paralelos, los cuales, al ser capturados en una imagen, parecen converger en un único punto llamado punto de fuga. En este proyecto el punto de fuga se calcula como el punto de corte entre las dos líneas rectas que definen el camino y se toma una decisión basándose en la posición de este punto. Este algoritmo de control se ha desarrollado usando el software LabVIEW Real-Time y se ha implementado posteriormente en un dispositivo con sistema operativo en tiempo real. El prototipo notifica (a través de una grabación de audio) a la persona con discapacidad visual sobre su desviación del camino en línea recta. También notifica si la persona se ha salido por completo del camino con un sonido de alarma. Este sistema de guiado consiste en un dispositivo electrónico (el cual contiene un procesador en tiempo real junto con una FPGA), una webcam, una batería y unos auriculares. En el proyecto la FPGA se usa para la reproducci on en paralelo de la grabación de audio mientras los procesos de visión tienen lugar en el procesador en tiempo real.Ingeniería Electrónica Industrial y Automátic

Deep reinforcement learning for multi-modal embodied navigation

Ce travail se concentre sur une tâche de micro-navigation en plein air où le but est de naviguer vers une adresse de rue spécifiée en utilisant plusieurs modalités (par exemple, images, texte de scène et GPS). La tâche de micro-navigation extérieure s’avère etre un défi important pour de nombreuses personnes malvoyantes, ce que nous démontrons à travers des entretiens et des études de marché, et nous limitons notre définition des problèmes à leurs besoins. Nous expérimentons d’abord avec un monde en grille partiellement observable (Grid-Street et Grid City) contenant des maisons, des numéros de rue et des régions navigables. Ensuite, nous introduisons le Environnement de Trottoir pour la Navigation Visuelle (ETNV), qui contient des images panoramiques avec des boîtes englobantes pour les numéros de maison, les portes et les panneaux de nom de rue, et des formulations pour plusieurs tâches de navigation. Dans SEVN, nous formons un modèle de politique pour fusionner des observations multimodales sous la forme d’images à résolution variable, de texte visible et de données GPS simulées afin de naviguer vers une porte d’objectif. Nous entraînons ce modèle en utilisant l’algorithme d’apprentissage par renforcement, Proximal Policy Optimization (PPO). Nous espérons que cette thèse fournira une base pour d’autres recherches sur la création d’agents pouvant aider les membres de la communauté des gens malvoyantes à naviguer le monde.This work focuses on an Outdoor Micro-Navigation (OMN) task in which the goal is to navigate to a specified street address using multiple modalities including images, scene-text, and GPS. This task is a significant challenge to many Blind and Visually Impaired (BVI) people, which we demonstrate through interviews and market research. To investigate the feasibility of solving this task with Deep Reinforcement Learning (DRL), we first introduce two partially observable grid-worlds, Grid-Street and Grid City, containing houses, street numbers, and navigable regions. In these environments, we train an agent to find specific houses using local observations under a variety of training procedures. We parameterize our agent with a neural network and train using reinforcement learning methods. Next, we introduce the Sidewalk Environment for Visual Navigation (SEVN), which contains panoramic images with labels for house numbers, doors, and street name signs, and formulations for several navigation tasks. In SEVN, we train another neural network model using Proximal Policy Optimization (PPO) to fuse multi-modal observations in the form of variable resolution images, visible text, and simulated GPS data, and to use this representation to navigate to goal doors. Our best model used all available modalities and was able to navigate to over 100 goals with an 85% success rate. We found that models with access to only a subset of these modalities performed significantly worse, supporting the need for a multi-modal approach to the OMN task. We hope that this thesis provides a foundation for further research into the creation of agents to assist members of the BVI community to safely navigate