Systematic literature review of hand gestures used in human computer interaction interfaces

Gestures, widely accepted as a humans' natural mode of interaction with their surroundings, have been considered for use in human-computer based interfaces since the early 1980s. They have been explored and implemented, with a range of success and maturity levels, in a variety of fields, facilitated by a multitude of technologies. Underpinning gesture theory however focuses on gestures performed simultaneously with speech, and majority of gesture based interfaces are supported by other modes of interaction. This article reports the results of a systematic review undertaken to identify characteristics of touchless/in-air hand gestures used in interaction interfaces. 148 articles were reviewed reporting on gesture-based interaction interfaces, identified through searching engineering and science databases (Engineering Village, Pro Quest, Science Direct, Scopus and Web of Science). The goal of the review was to map the field of gesture-based interfaces, investigate the patterns in gesture use, and identify common combinations of gestures for different combinations of applications and technologies. From the review, the community seems disparate with little evidence of building upon prior work and a fundamental framework of gesture-based interaction is not evident. However, the findings can help inform future developments and provide valuable information about the benefits and drawbacks of different approaches. It was further found that the nature and appropriateness of gestures used was not a primary factor in gesture elicitation when designing gesture based systems, and that ease of technology implementation often took precedence

GazeDrone: Mobile Eye-Based Interaction in Public Space Without Augmenting the User

Gaze interaction holds a lot of promise for seamless human-computer interaction. At the same time, current wearable mobile eye trackers require user augmentation that negatively impacts natural user behavior while remote trackers require users to position themselves within a confined tracking range. We present GazeDrone, the first system that combines a camera-equipped aerial drone with a computational method to detect sidelong glances for spontaneous (calibration-free) gaze-based interaction with surrounding pervasive systems (e.g., public displays). GazeDrone does not require augmenting each user with on-body sensors and allows interaction from arbitrary positions, even while moving. We demonstrate that drone-supported gaze interaction is feasible and accurate for certain movement types. It is well-perceived by users, in particular while interacting from a fixed position as well as while moving orthogonally or diagonally to a display. We present design implications and discuss opportunities and challenges for drone-supported gaze interaction in public

Design and Evaluation of a Contact-Free Interface for Minimally Invasive Robotics Assisted Surgery

Robotic-assisted minimally invasive surgery (RAMIS) is becoming increasingly more common for many surgical procedures. These minimally invasive techniques offer the benefit of reduced patient recovery time, mortality and scarring compared to traditional open surgery. Teleoperated procedures have the added advantage of increased visualization, and enhanced accuracy for the surgeon through tremor filtering and scaling down hand motions. There are however still limitations in these techniques preventing the widespread growth of the technology. In RAMIS, the surgeon is limited in their movement by the operating console or master device, and the cost of robotic surgery is often too high to justify for many procedures. Sterility issues arise as well, as the surgeon must be in contact with the master device, preventing a smooth transition between traditional and robotic modes of surgery. This thesis outlines the design and analysis of a novel method of interaction with the da Vinci Surgical Robot. Using the da Vinci Research Kit (DVRK), an open source research platform for the da Vinci robot, an interface was developed for controlling the robotic arms with the Leap Motion Controller. This small device uses infrared LEDs and two cameras to detect the 3D positions of the hand and fingers. This data from the hands is mapped to the da Vinci surgical tools in real time, providing the surgeon with an intuitive method of controlling the instruments. An analysis of the tracking workspace is provided, to give a solution to occlusion issues. Multiple sensors are fused together in order to increase the range of trackable motion over a single sensor. Additional work involves replacing the current viewing screen with a virtual reality (VR) headset (Oculus Rift), to provide the surgeon with a stereoscopic 3D view of the surgical site without the need for a large monitor. The headset also provides the user with a more intuitive and natural method of positioning the camera during surgery, using the natural motions of the head. The large master console of the da Vinci system has been replaced with an inexpensive vision based tracking system, and VR headset, allowing the surgeon to operate the da Vinci Surgical Robot with more natural movements for the user. A preliminary evaluation of the system is provided, with recommendations for future work

The development of a human-robot interface for industrial collaborative system

Industrial robots have been identified as one of the most effective solutions for optimising output and quality within many industries. However, there are a number of manufacturing applications involving complex tasks and inconstant components which prohibit the use of fully automated solutions in the foreseeable future. A breakthrough in robotic technologies and changes in safety legislations have supported the creation of robots that coexist and assist humans in industrial applications. It has been broadly recognised that human-robot collaborative systems would be a realistic solution as an advanced production system with wide range of applications and high economic impact. This type of system can utilise the best of both worlds, where the robot can perform simple tasks that require high repeatability while the human performs tasks that require judgement and dexterity of the human hands. Robots in such system will operate as “intelligent assistants”. In a collaborative working environment, robot and human share the same working area, and interact with each other. This level of interface will require effective ways of communication and collaboration to avoid unwanted conflicts. This project aims to create a user interface for industrial collaborative robot system through integration of current robotic technologies. The robotic system is designed for seamless collaboration with a human in close proximity. The system is capable to communicate with the human via the exchange of gestures, as well as visual signal which operators can observe and comprehend at a glance. The main objective of this PhD is to develop a Human-Robot Interface (HRI) for communication with an industrial collaborative robot during collaboration in proximity. The system is developed in conjunction with a small scale collaborative robot system which has been integrated using off-the-shelf components. The system should be capable of receiving input from the human user via an intuitive method as well as indicating its status to the user ii effectively. The HRI will be developed using a combination of hardware integrations and software developments. The software and the control framework were developed in a way that is applicable to other industrial robots in the future. The developed gesture command system is demonstrated on a heavy duty industrial robot

Collaborative robot control with hand gestures

Mestrado de dupla diplomação com a Université Libre de TunisThis thesis focuses on hand gesture recognition by proposing an architecture to control a collaborative robot in real-time vision based on hand detection, tracking, and gesture recognition for interaction with an application via hand gestures. The first stage of our system allows detecting and tracking a bar e hand in a cluttered background using skin detection and contour comparison. The second stage allows recognizing hand gestures using a Machine learning method algorithm. Finally an interface has been developed to control the robot over. Our hand gesture recognition system consists of two parts, in the first part for every frame captured from a camera we extract the keypoints for every training image using a machine learning algorithm, and we appoint the keypoints from every image into a keypoint map. This map is treated as an input for our processing algorithm which uses several methods to recognize the fingers in each hand. In the second part, we use a 3D camera with Infrared capabilities to get a 3D model of the hand to implement it in our system, after that we track the fingers in each hand and recognize them which made it possible to count the extended fingers and to distinguish each finger pattern. An interface to control the robot has been made that utilizes the previous steps that gives a real-time process and a dynamic 3D representation.Esta dissertação trata do reconhecimento de gestos realizados com a mão humana, propondo uma arquitetura para interagir com um robô colaborativo, baseado em visão computacional, rastreamento e reconhecimento de gestos. O primeiro estágio do sistema desenvolvido permite detectar e rastrear a presença de uma mão em um fundo desordenado usando detecção de pele e comparação de contornos. A segunda fase permite reconhecer os gestos das mãos usando um algoritmo do método de aprendizado de máquina. Finalmente, uma interface foi desenvolvida para interagir com robô. O sistema de reconhecimento de gestos manuais está dividido em duas partes. Na primeira parte, para cada quadro capturado de uma câmera, foi extraído os pontos-chave de cada imagem de treinamento usando um algoritmo de aprendizado de máquina e nomeamos os pontos-chave de cada imagem em um mapa de pontos-chave. Este mapa é tratado como uma entrada para o algoritmo de processamento que usa vários métodos para reconhecer os dedos em cada mão. Na segunda parte, foi utilizado uma câmera 3D com recursos de infravermelho para obter um modelo 3D da mão para implementá-lo em no sistema desenvolvido, e então, foi realizado os rastreio dos dedos de cada mão seguido pelo reconhecimento que possibilitou contabilizar os dedos estendidos e para distinguir cada padrão de dedo. Foi elaborado uma interface para interagir com o robô manipulador que utiliza as etapas anteriores que fornece um processo em tempo real e uma representação 3D dinâmica