A Vision-Based Human Hand Gesture Recognition Interface For Image Browsing Application

Computer mouse has been an efficient input device. However, the mouse usage limits user’s freedom. Besides, the devices are easily contaminated with bacteria and spreading disease among users. The contactless vision-based hand gesture recognition is one of the solutions to the freedom and hygiene problem. But it faces challenges of usability in term of cost and environmental variation like lighting. This thesis proposes and implements hand gesture recognition methods in image browsing application, to allow users views pictures contactless from input device in real time

Hand features extractor using hand contour – a case study

Hand gesture recognition is an important topic in natural user interfaces (NUI). Hand features extraction is the first step for hand gesture recognition. This work proposes a novel real time method for hand features recognition. In our framework we use three cameras and the hand region is extracted with the background subtraction method. Features like arm angle and fingers positions are calculated using Y variations in the vertical contour image. Wrist detection is obtained by calculating the bigger distance from a base line and the hand contour, giving the main features for the hand gesture recognition. Experiments on our own data-set of about 1800 images show that our method performs well and is highly efficient

Gestures in Machine Interaction

Vnencumbered-gesture-interaction (VGI) describes the use of unrestricted gestures in machine interaction. The development of such technology will enable users to interact with machines and virtual environments by performing actions like grasping, pinching or waving without the need of peripherals. Advances in image-processing and pattern recognition make such interaction viable and in some applications more practical than current modes of keyboard, mouse and touch-screen interaction provide. VGI is emerging as a popular topic amongst Human-Computer Interaction (HCI), Computer-vision and gesture research; and is developing into a topic with potential to significantly impact the future of computer-interaction, robot-control and gaming. This thesis investigates whether an ergonomic model of VGI can be developed and implemented on consumer devices by considering some of the barriers currently preventing such a model of VGI from being widely adopted. This research aims to address the development of freehand gesture interfaces and accompanying syntax. Without the detailed consideration of the evolution of this field the development of un-ergonomic, inefficient interfaces capable of placing undue strain on interface users becomes more likely. In the course of this thesis some novel design and methodological assertions are made. The Gesture in Machine Interaction (GiMI) syntax model and the Gesture-Face Layer (GFL), developed in the course of this research, have been designed to facilitate ergonomic gesture interaction. The GiMI is an interface syntax model designed to enable cursor control, browser navigation commands and steering control for remote robots or vehicles. Through applying state-of-the-art image processing that facilitates three-dimensional (3D) recognition of human action, this research investigates how interface syntax can incorporate the broadest range of human actions. By advancing our understanding of ergonomic gesture syntax, this research aims to assist future developers evaluate the efficiency of gesture interfaces, lexicons and syntax

Gaze, Posture and Gesture Recognition to Minimize Focus Shifts for Intelligent Operating Rooms in a Collaborative Support System

This paper describes the design of intelligent, collaborative operating rooms based on highly intuitive, natural and multimodal interaction. Intelligent operating rooms minimize surgeon’s focus shifts by minimizing both the focus spatial offset (distance moved by surgeon’s head or gaze to the new target) and the movement spatial offset (distance surgeon covers physically). These spatio-temporal measures have an impact on the surgeon’s performance in the operating room. I describe how machine vision techniques are used to extract spatio-temporal measures and to interact with the system, and how computer graphics techniques can be used to display visual medical information effectively and rapidly. Design considerations are discussed and examples showing the feasibility of the different approaches are presented

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation

Sistema de miografia óptica para reconhecimento de gestos e posturas de mão

Orientador: Éric FujiwaraDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: Nesse projeto, demonstrou-se um sistema de miografia óptica como uma alternativa promissora para monitorar as posturas da mão e os gestos do usuário. Essa técnica se fundamenta em acompanhar as atividades musculares responsáveis pelos movimentos da mão com uma câmera externa, relacionando a distorção visual verificada no antebraço com a contração e o relaxamento necessários para dada postura. Três configurações de sensores foram propostas, estudadas e avaliadas. A primeira propôs monitorar a atividade muscular analisando a variação da frequência espacial de uma textura de listras uniformes impressa sobre a pele, enquanto que a segunda se caracteriza pela contagem de pixels de pele visível dentro da região de interesse. Ambas as configurações se mostraram inviáveis pela baixa robustez e alta demanda por condições experimentais controladas. Por fim, a terceira recupera o estado da mão acompanhando o deslocamento de uma série de marcadores coloridos distribuídos ao longo do antebraço. Com um webcam de 24 fps e 640 × 480 pixels, essa última configuração foi validada para oito posturas distintas, explorando principalmente a flexão e extensão dos dedos e do polegar, além da adução e abdução do último. Os dados experimentais, adquiridos off-line, são submetidos a uma rotina de processamento de imagens para extrair a informação espacial e de cor dos marcadores em cada quadro, dados esses utilizados para rastrear os mesmos marcadores ao longo de todos os quadros. Para reduzir a influência das vibrações naturais e inerentes ao corpo humano, um sistema de referencial local é ainda adotado dentro da própria região de interesse. Finalmente, os dados quadro a quadro com o ground truth são alimentados a uma rede neural artificial sequencial, responsável pela calibração supervisionada do sensor e posterior classificação das posturas. O desempenho do sistema para a classificação das oito posturas foi avaliado com base na validação cruzada com 10-folds, com a câmera monitorando o antebraço pela superfície interna ou externa. O sensor apresentou uma precisão de ?92.4% e exatidão de ?97.9% para o primeiro caso, e uma precisão de ?75.1% e exatidão de ?92.5% para o segundo, sendo comparável a outras técnicas de miografia, demonstrando a viabilidade do projeto e abrindo perspectivas para aplicações em interfaces humano-robôAbstract: In this work, an optical myography system is demonstrated as a promising alternative to monitor hand posture and gestures of the user. This technique is based on accompanying muscular activities responsible for hand motion with an external camera, and relating the visual deformation observed on the forearm to the muscular contractions/relaxations for a given posture. Three sensor designs were proposed, studied and evaluated. The first one intended to monitor muscular activity by analyzing the spatial frequency variation of a uniformly distributed stripe pattern stamped on the skin, whereas the second one is characterized by reckoning visible skin pixels inside the region of interest. Both designs are impracticable due to their low robustness and high demand for controlled experimental conditions. At last, the third design retrieves hand configuration by tracking visually the displacements of a series of color markers distributed over the forearm. With a webcam of 24 fps and 640 × 480 pixels, this design was validated for eight different postures, exploring fingers and thumb flexion/extension, plus thumb adduction/abduction. The experimental data are acquired offline and, then, submitted to an image processing routine to extract color and spatial information of the markers in each frame; the extracted data is subsequently used to track the same markers along all frames. To reduce the influence of human body natural and inherent vibrations, a local reference frame is yet adopted in the region of interest. Finally, the frame by frame data, along with the ground truth posture, are fed into a sequential artificial neural network, responsible for sensor supervised calibration and subsequent posture classification. The system performance was evaluated in terms of eight postures classification via 10-fold cross-validation, with the camera monitoring either the underside or the back of the forearm. The sensor presented a ?92.4% precision and ?97.9% accuracy for the former, and a ?75.1% precision and ?92.5% accuracy for the latter, being thus comparable to other myographic techniques; it also demonstrated that the project is feasible and offers prospects for human-robot interaction applicationsMestradoEngenharia MecanicaMestre em Engenharia Mecânica33003017CAPE

Fused mechanomyography and inertial measurement for human-robot interface

Human-Machine Interfaces (HMI) are the technology through which we interact with the ever-increasing quantity of smart devices surrounding us. The fundamental goal of an HMI is to facilitate robot control through uniting a human operator as the supervisor with a machine as the task executor. Sensors, actuators, and onboard intelligence have not reached the point where robotic manipulators may function with complete autonomy and therefore some form of HMI is still necessary in unstructured environments. These may include environments where direct human action is undesirable or infeasible, and situations where a robot must assist and/or interface with people. Contemporary literature has introduced concepts such as body-worn mechanical devices, instrumented gloves, inertial or electromagnetic motion tracking sensors on the arms, head, or legs, electroencephalographic (EEG) brain activity sensors, electromyographic (EMG) muscular activity sensors and camera-based (vision) interfaces to recognize hand gestures and/or track arm motions for assessment of operator intent and generation of robotic control signals. While these developments offer a wealth of future potential their utility has been largely restricted to laboratory demonstrations in controlled environments due to issues such as lack of portability and robustness and an inability to extract operator intent for both arm and hand motion. Wearable physiological sensors hold particular promise for capture of human intent/command. EMG-based gesture recognition systems in particular have received significant attention in recent literature. As wearable pervasive devices, they offer benefits over camera or physical input systems in that they neither inhibit the user physically nor constrain the user to a location where the sensors are deployed. Despite these benefits, EMG alone has yet to demonstrate the capacity to recognize both gross movement (e.g. arm motion) and finer grasping (e.g. hand movement). As such, many researchers have proposed fusing muscle activity (EMG) and motion tracking e.g. (inertial measurement) to combine arm motion and grasp intent as HMI input for manipulator control. However, such work has arguably reached a plateau since EMG suffers from interference from environmental factors which cause signal degradation over time, demands an electrical connection with the skin, and has not demonstrated the capacity to function out of controlled environments for long periods of time. This thesis proposes a new form of gesture-based interface utilising a novel combination of inertial measurement units (IMUs) and mechanomyography sensors (MMGs). The modular system permits numerous configurations of IMU to derive body kinematics in real-time and uses this to convert arm movements into control signals. Additionally, bands containing six mechanomyography sensors were used to observe muscular contractions in the forearm which are generated using specific hand motions. This combination of continuous and discrete control signals allows a large variety of smart devices to be controlled. Several methods of pattern recognition were implemented to provide accurate decoding of the mechanomyographic information, including Linear Discriminant Analysis and Support Vector Machines. Based on these techniques, accuracies of 94.5% and 94.6% respectively were achieved for 12 gesture classification. In real-time tests, accuracies of 95.6% were achieved in 5 gesture classification. It has previously been noted that MMG sensors are susceptible to motion induced interference. The thesis also established that arm pose also changes the measured signal. This thesis introduces a new method of fusing of IMU and MMG to provide a classification that is robust to both of these sources of interference. Additionally, an improvement in orientation estimation, and a new orientation estimation algorithm are proposed. These improvements to the robustness of the system provide the first solution that is able to reliably track both motion and muscle activity for extended periods of time for HMI outside a clinical environment. Application in robot teleoperation in both real-world and virtual environments were explored. With multiple degrees of freedom, robot teleoperation provides an ideal test platform for HMI devices, since it requires a combination of continuous and discrete control signals. The field of prosthetics also represents a unique challenge for HMI applications. In an ideal situation, the sensor suite should be capable of detecting the muscular activity in the residual limb which is naturally indicative of intent to perform a specific hand pose and trigger this post in the prosthetic device. Dynamic environmental conditions within a socket such as skin impedance have delayed the translation of gesture control systems into prosthetic devices, however mechanomyography sensors are unaffected by such issues. There is huge potential for a system like this to be utilised as a controller as ubiquitous computing systems become more prevalent, and as the desire for a simple, universal interface increases. Such systems have the potential to impact significantly on the quality of life of prosthetic users and others.Open Acces