767 research outputs found
Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature
Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applicationsBeca Conacyt Doctorado No de CVU: 64683
Assistive technology design and development for acceptable robotics companions for ageing years
© 2013 Farshid Amirabdollahian et al., licensee Versita Sp. z o. o. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs license, which means that the text may be used for non-commercial purposes, provided credit is given to the author.A new stream of research and development responds to changes in life expectancy across the world. It includes technologies which enhance well-being of individuals, specifically for older people. The ACCOMPANY project focuses on home companion technologies and issues surrounding technology development for assistive purposes. The project responds to some overlooked aspects of technology design, divided into multiple areas such as empathic and social human-robot interaction, robot learning and memory visualisation, and monitoring persons’ activities at home. To bring these aspects together, a dedicated task is identified to ensure technological integration of these multiple approaches on an existing robotic platform, Care-O-Bot®3 in the context of a smart-home environment utilising a multitude of sensor arrays. Formative and summative evaluation cycles are then used to assess the emerging prototype towards identifying acceptable behaviours and roles for the robot, for example role as a butler or a trainer, while also comparing user requirements to achieved progress. In a novel approach, the project considers ethical concerns and by highlighting principles such as autonomy, independence, enablement, safety and privacy, it embarks on providing a discussion medium where user views on these principles and the existing tension between some of these principles, for example tension between privacy and autonomy over safety, can be captured and considered in design cycles and throughout project developmentsPeer reviewe
Real-Time Obstacle Detection System in Indoor Environment for the Visually Impaired Using Microsoft Kinect Sensor
Any mobility aid for the visually impaired people should be able to accurately detect and warn about nearly obstacles. In this paper, we present a method for support system to detect obstacle in indoor environment based on Kinect sensor and 3D-image processing. Color-Depth data of the scene in front of the user is collected using the Kinect with the support of the standard framework for 3D sensing OpenNI and processed by PCL library to extract accurate 3D information of the obstacles. The experiments have been performed with the dataset in multiple indoor scenarios and in different lighting conditions. Results showed that our system is able to accurately detect the four types of obstacle: walls, doors, stairs, and a residual class that covers loose obstacles on the floor. Precisely, walls and loose obstacles on the floor are detected in practically all cases, whereas doors are detected in 90.69% out of 43 positive image samples. For the step detection, we have correctly detected the upstairs in 97.33% out of 75 positive images while the correct rate of downstairs detection is lower with 89.47% from 38 positive images. Our method further allows the computation of the distance between the user and the obstacles
An Overview of Self-Adaptive Technologies Within Virtual Reality Training
This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Automated haptic interaction can enable tele-presence and virtual artefact tactile interaction from either remote or simulated environments. Automation, machine learning and data driven features play an important role in providing trainee-specific individual adaptive training content. Data from trainee assessment can form an input to autonomous systems for customised training and automated difficulty levels to match individual requirements. Self-adaptive technology has been developed previously within individual technologies of VR training. One of the conclusions of this research is that while it does not exist, an enhanced portable framework is needed and it would be beneficial to combine automation of core technologies, producing a reusable automation framework for VR training
Human robot interaction in a crowded environment
Human Robot Interaction (HRI) is the primary means of establishing natural and affective communication between humans and robots. HRI enables robots to act in a way similar to humans in order to assist in activities that are considered to be laborious, unsafe, or repetitive. Vision based human robot interaction is a major component of HRI, with which visual information is used to interpret how human interaction takes place. Common tasks of HRI include finding pre-trained static or dynamic gestures in an image, which involves localising different key parts of the human body such as the face and hands. This information is subsequently used to extract different gestures. After the initial detection process, the robot is required to comprehend the underlying meaning of these gestures [3].
Thus far, most gesture recognition systems can only detect gestures and identify a person in relatively static environments. This is not realistic for practical applications as difficulties may arise from people‟s movements and changing illumination conditions. Another issue to consider is that of identifying the commanding person in a crowded scene, which is important for interpreting the navigation commands. To this end, it is necessary to associate the gesture to the correct person and automatic reasoning is required to extract the most probable location of the person who has initiated the gesture. In this thesis, we have proposed a practical framework for addressing the above issues. It attempts to achieve a coarse level understanding about a given environment before engaging in active communication. This includes recognizing human robot interaction, where a person has the intention to communicate with the robot. In this regard, it is necessary to differentiate if people present are engaged with each other or their surrounding environment. The basic task is to detect and reason about the environmental context and different interactions so as to respond accordingly. For example, if individuals are engaged in conversation, the robot should realize it is best not to disturb or, if an individual is receptive to the robot‟s interaction, it may approach the person.
Finally, if the user is moving in the environment, it can analyse further to understand if any help can be offered in assisting this user. The method proposed in this thesis combines multiple visual cues in a Bayesian framework to identify people in a scene and determine potential intentions. For improving system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. The results achieved demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment [7]
DetecciĂłn y modelado de escaleras con sensor RGB-D para asistencia personal
La habilidad de avanzar y moverse de manera efectiva por el entorno resulta natural para la mayorĂa de la gente, pero no resulta fácil de realizar bajo algunas circunstancias, como es el caso de las personas con problemas visuales o cuando nos movemos en entornos especialmente complejos o desconocidos. Lo que pretendemos conseguir a largo plazo es crear un sistema portable de asistencia aumentada para ayudar a quienes se enfrentan a esas circunstancias. Para ello nos podemos ayudar de cámaras, que se integran en el asistente. En este trabajo nos hemos centrado en el mĂłdulo de detecciĂłn, dejando para otros trabajos el resto de mĂłdulos, como podrĂa ser la interfaz entre la detecciĂłn y el usuario. Un sistema de guiado de personas debe mantener al sujeto que lo utiliza apartado de peligros, pero tambiĂ©n deberĂa ser capaz de reconocer ciertas caracterĂsticas del entorno para interactuar con ellas. En este trabajo resolvemos la detecciĂłn de uno de los recursos más comunes que una persona puede tener que utilizar a lo largo de su vida diaria: las escaleras. Encontrar escaleras es doblemente beneficioso, puesto que no sĂłlo permite evitar posibles caĂdas sino que ayuda a indicar al usuario la posibilidad de alcanzar otro piso en el edificio. Para conseguir esto hemos hecho uso de un sensor RGB-D, que irá situado en el pecho del sujeto, y que permite captar de manera simultánea y sincronizada informaciĂłn de color y profundidad de la escena. El algoritmo usa de manera ventajosa la captaciĂłn de profundidad para encontrar el suelo y asĂ orientar la escena de la manera que aparece ante el usuario. Posteriormente hay un proceso de segmentaciĂłn y clasificaciĂłn de la escena de la que obtenemos aquellos segmentos que se corresponden con "suelo", "paredes", "planos horizontales" y una clase residual, de la que todos los miembros son considerados "obstáculos". A continuaciĂłn, el algoritmo de detecciĂłn de escaleras determina si los planos horizontales son escalones que forman una escalera y los ordena jerárquicamente. En el caso de que se haya encontrado una escalera, el algoritmo de modelado nos proporciona toda la informaciĂłn de utilidad para el usuario: cĂłmo esta posicionada con respecto a Ă©l, cuántos escalones se ven y cuáles son sus medidas aproximadas. En definitiva, lo que se presenta en este trabajo es un nuevo algoritmo de ayuda a la navegaciĂłn humana en entornos de interior cuya mayor contribuciĂłn es un algoritmo de detecciĂłn y modelado de escaleras que determina toda la informaciĂłn de mayor relevancia para el sujeto. Se han realizado experimentos con grabaciones de vĂdeo en distintos entornos, consiguiendo buenos resultados tanto en precisiĂłn como en tiempo de respuesta. Además se ha realizado una comparaciĂłn de nuestros resultados con los extraĂdos de otras publicaciones, demostrando que no sĂłlo se consigue una eciencia que iguala al estado de la materia sino que tambiĂ©n se aportan una serie de mejoras. Especialmente, nuestro algoritmo es el primero capaz de obtener las dimensiones de las escaleras incluso con obstáculos obstruyendo parcialmente la vista, como puede ser gente subiendo o bajando. Como resultado de este trabajo se ha elaborado una publicaciĂłn aceptada en el Second Workshop on Assitive Computer Vision and Robotics del ECCV, cuya presentaciĂłn tiene lugar el 12 de Septiembre de 2014 en ZĂşrich, Suiza
Assistive technology design and development for acceptable robotics companions for ageing years.
A new stream of research and development responds to changes in life expectancy across the world. It includes technologies which enhance well-being of individuals, specifically for older people. The ACCOMPANY project focuses on home companion technologies and issues surrounding technology development for assistive purposes. The project responds to some overlooked aspects of technology design, divided into multiple areas such as empathic and social human-robot interaction, robot learning and memory visualisation, and monitoring persons’ activities at home. To bring these aspects together, a dedicated task is identified to ensure technological integration of these multiple approaches on an existing robotic platform, Care-O-Bot®3 in the context of a smart-home environment utilising a multitude of sensor arrays. Formative and summative evaluation cycles are then used to assess the emerging prototype towards identifying acceptable behaviours and roles for the robot, for example role as a butler or a trainer, while also comparing user requirements to achieved progress. In a novel approach, the project considers ethical concerns and by highlighting principles such as autonomy, independence, enablement, safety and privacy, it embarks on providing a discussion medium where user views on these principles and the existing tension between some of these principles, for example tension between privacy and autonomy over safety, can be captured and considered in design cycles and throughout project developments
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