Hand gesture recognition using Kinect.

Hand gesture recognition (HGR) is an important research topic because some situations require silent communication with sign languages. Computational HGR systems assist silent communication, and help people learn a sign language. In this thesis. a novel method for contact-less HGR using Microsoft Kinect for Xbox is described, and a real-time HCR system is implemented with Microsoft Visual Studio 2010. Two different scenarios for HGR are provided: the Popular Gesture with nine gestures, and the Numbers with nine gestures. The system allows the users to select a scenario, and it is able to detect hand gestures made by users. to identify fingers, and to recognize the meanings of gestures, and to display the meanings and pictures on screen. The accuracy of the HGR system is from 84% to 99% with single hand gestures, and from 90% to 100% if both hands perform the same gesture at the same time. Because the depth sensor of Kinect is an infrared camera, the lighting conditions. signers\u27 skin colors and clothing, and background have little impact on the performance of this system. The accuracy and the robustness make this system a versatile component that can be integrated in a variety of applications in daily life

Dynamic gesture recognition using transformation invariant hand shape recognition

In this thesis a detailed framework is presented for accurate real time gesture recognition. Our approach to develop a hand-shape classifier, trained using computer animation, along with its application in dynamic gesture recognition is described. The system developed operates in real time and provides accurate gesture recognition. It operates using a single low resolution camera and operates in Matlab on a conventional PC running Windows XP. The hand shape classifier outlined in this thesis uses transformation invariant subspaces created using Principal Component Analysis (PCA). These subspaces are created from a large vocabulary created in a systematic maimer using computer animation. In recognising dynamic gestures we utilise both hand shape and hand position information; these are two o f the main features used by humans in distinguishing gestures. Hidden Markov Models (HMMs) are trained and employed to recognise this combination of hand shape and hand position features. During the course o f this thesis we have described in detail the inspiration and motivation behind our research and its possible applications. In this work our emphasis is on achieving a high speed system that works in real time with high accuracy

Mobile to cloud co-processing of ASL finger spelling to text conversion

Computer recognition of American Sign Language (ASL) is a computationally intensive task. Although it has generally been performed using powerful lab workstations, this research investigates transcription of static ASL signs using an application on a consumer-level mobile device. The application provides real-time sign to text translation by processing a live video stream to detect the ASL alphabet as well as custom signs to perform tasks on the device. In this work several avenues for classification and processing were ex-plored to evaluate performance for mobile ASL transcription. The cho-sen classification algorithm uses locality preserving projections (LPP) with trained support vector machines (SVMs). Processing was investigated using either the mobile device only or with cloud assistance. In comparison to the native mobile application, the cloud-assisted application increased classification speed, reduced memory usage, and kept the network usage low while barely increasing the power required. A distributed solution has been created that will provide a new way of interacting with the mobile device in a native way to a hard-of-hearing person while also considering the network, power and processing constraints of the mobile device

Continual Learing of Hand Gestures for Human Robot Interaction

Human communication is multimodal. For years, natural language processing has been studied as a form of human-machine or human-robot interaction. In recent years, computer vision techniques have been applied to the recognition of static and dynamic gestures, and progress is being made in sign language recognition too. The typical way to train a machine learning algorithm to perform a classification task is to provide training examples for all the classes that need to be identified by the model. In a real-world scenario, such as in the use of assistive robots, it is useful to learn new concepts from interaction. However, unlike biological brains, artificial neural networks suffer from catastrophic forgetting, and as a result, are not good at incrementally learning new classes. In this thesis, the HAnd Gesture Incremental Learning (HAGIL) framework is proposed as a method to incrementally learn to classify static hand gestures. We show that HAGIL is able to incrementally learn up to 36 new symbols using only 5 samples for each old symbol, achieving a final average accuracy of over 90%. In addition to that, the incremental training time is reduced to a 10% of the time required when using all data available

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Development of speech prostheses: current status and recent advances

This is an Accepted Manuscript of an article published by Taylor & Francis in Expert Review of Medical Devices on September, 2010, available online: http://www.tandfonline.com/10.1586/erd.10.34.Brain–computer interfaces (BCIs) have been developed over the past decade to restore communication to persons with severe paralysis. In the most severe cases of paralysis, known as locked-in syndrome, patients retain cognition and sensation, but are capable of only slight voluntary eye movements. For these patients, no standard communication method is available, although some can use BCIs to communicate by selecting letters or words on a computer. Recent research has sought to improve on existing techniques by using BCIs to create a direct prediction of speech utterances rather than to simply control a spelling device. Such methods are the first steps towards speech prostheses as they are intended to entirely replace the vocal apparatus of paralyzed users. This article outlines many well known methods for restoration of communication by BCI and illustrates the difference between spelling devices and direct speech prediction or speech prosthesis

Portuguese sign language recognition via computer vision and depth sensor

Sign languages are used worldwide by a multitude of individuals. They are mostly used by the deaf communities and their teachers, or people associated with them by ties of friendship or family. Speakers are a minority of citizens, often segregated, and over the years not much attention has been given to this form of communication, even by the scientific community. In fact, in Computer Science there is some, but limited, research and development in this area. In the particular case of sign Portuguese Sign Language-PSL that fact is more evident and, to our knowledge there isn’t yet an efficient system to perform the automatic recognition of PSL signs. With the advent and wide spreading of devices such as depth sensors, there are new possibilities to address this problem. In this thesis, we have specified, developed, tested and preliminary evaluated, solutions that we think will bring valuable contributions to the problem of Automatic Gesture Recognition, applied to Sign Languages, such as the case of Portuguese Sign Language. In the context of this work, Computer Vision techniques were adapted to the case of Depth Sensors. A proper gesture taxonomy for this problem was proposed, and techniques for feature extraction, representation, storing and classification were presented. Two novel algorithms to solve the problem of real-time recognition of isolated static poses were specified, developed, tested and evaluated. Two other algorithms for isolated dynamic movements for gesture recognition (one of them novel), have been also specified, developed, tested and evaluated. Analyzed results compare well with the literature.As Línguas Gestuais são utilizadas em todo o Mundo por uma imensidão de indivíduos. Trata-se na sua grande maioria de surdos e/ou mudos, ou pessoas a eles associados por laços familiares de amizade ou professores de Língua Gestual. Tratando-se de uma minoria, muitas vezes segregada, não tem vindo a ser dada ao longo dos anos pela comunidade científica, a devida atenção a esta forma de comunicação. Na área das Ciências da Computação existem alguns, mas poucos trabalhos de investigação e desenvolvimento. No caso particular da Língua Gestual Portuguesa - LGP esse facto é ainda mais evidente não sendo nosso conhecimento a existência de um sistema eficaz e efetivo para fazer o reconhecimento automático de gestos da LGP. Com o aparecimento ou massificação de dispositivos, tais como sensores de profundidade, surgem novas possibilidades para abordar este problema. Nesta tese, foram especificadas, desenvolvidas, testadas e efectuada a avaliação preliminar de soluções que acreditamos que trarão valiosas contribuições para o problema do Reconhecimento Automático de Gestos, aplicado às Línguas Gestuais, como é o caso da Língua Gestual Portuguesa. Foram adaptadas técnicas de Visão por Computador ao caso dos Sensores de Profundidade. Foi proposta uma taxonomia adequada ao problema, e apresentadas técnicas para a extração, representação e armazenamento de características. Foram especificados, desenvolvidos, testados e avaliados dois algoritmos para resolver o problema do reconhecimento em tempo real de poses estáticas isoladas. Foram também especificados, desenvolvidos, testados e avaliados outros dois algoritmos para o Reconhecimento de Movimentos Dinâmicos Isolados de Gestos(um deles novo).Os resultados analisados são comparáveis à literatura.Las lenguas de Signos se utilizan en todo el Mundo por una multitud de personas. En su mayoría son personas sordas y/o mudas, o personas asociadas con ellos por vínculos de amistad o familiares y profesores de Lengua de Signos. Es una minoría de personas, a menudo segregadas, y no se ha dado en los últimos años por la comunidad científica, la atención debida a esta forma de comunicación. En el área de Ciencias de la Computación hay alguna pero poca investigación y desarrollo. En el caso particular de la Lengua de Signos Portuguesa - LSP, no es de nuestro conocimiento la existencia de un sistema eficiente y eficaz para el reconocimiento automático. Con la llegada en masa de dispositivos tales como Sensores de Profundidad, hay nuevas posibilidades para abordar el problema del Reconocimiento de Gestos. En esta tesis se han especificado, desarrollado, probado y hecha una evaluación preliminar de soluciones, aplicada a las Lenguas de Signos como el caso de la Lengua de Signos Portuguesa - LSP. Se han adaptado las técnicas de Visión por Ordenador para el caso de los Sensores de Profundidad. Se propone una taxonomía apropiada para el problema y se presentan técnicas para la extracción, representación y el almacenamiento de características. Se desarrollaran, probaran, compararan y analizan los resultados de dos nuevos algoritmos para resolver el problema del Reconocimiento Aislado y Estático de Posturas. Otros dos algoritmos (uno de ellos nuevo) fueran también desarrollados, probados, comparados y analizados los resultados, para el Reconocimiento de Movimientos Dinámicos Aislados de los Gestos