Lightweight palm and finger tracking for real-time 3D gesture control

peer reviewedWe present a novel technique implementing barehanded interaction with virtual 3D content by employing a time-of-flight camera. The system improves on existing 3D multi-touch systems by working regardless of lighting conditions and supplying a working volume large enough for multiple users. Previous systems were limited either by environmental requirements, working volume, or computational resources necessary for realtime operation. By employing a time-of-flight camera, the system is capable of reliably recognizing gestures at the finger level in real-time at more than 50 fps with commodity computer hardware using our newly developed precision hand and finger-tracking algorithm. Building on this algorithm, the system performs gesture recognition with simple constraint modeling over statistical aggregations of the hand appearances in a working volume of more than 8 cubic meters. Two iterations of user tests were performed on a prototype system, demonstrating the feasibility and usability of the approach as well as providing first insights regarding the acceptance of true barehanded touch-based 3D interaction

Using Pinch Gloves(TM) for both Natural and Abstract Interaction Techniques in Virtual Environments

Usable three-dimensional (3D) interaction techniques are difficult to design, implement, and evaluate. One reason for this is a poor understanding of the advantages and disadvantages of the wide range of 3D input devices, and of the mapping between input devices and interaction techniques. We present an analysis of Pinch Gloves™ and their use as input devices for virtual environments (VEs). We have developed a number of novel and usable interaction techniques for VEs using the gloves, including a menu system, a technique for text input, and a two-handed navigation technique. User studies have indicated the usability and utility of these techniques

Automated Tracking of Hand Hygiene Stages

The European Centre for Disease Prevention and Control (ECDC) estimates that 2.5 millioncases of Hospital Acquired Infections (HAIs) occur each year in the European Union. Handhygiene is regarded as one of the most important preventive measures for HAIs. If it is implemented properly, hand hygiene can reduce the risk of cross-transmission of an infection in the healthcare environment. Good hand hygiene is not only important for healthcare settings. Therecent ongoing coronavirus pandemic has highlighted the importance of hand hygiene practices in our daily lives, with governments and health authorities around the world promoting goodhand hygiene practices. The WHO has published guidelines of hand hygiene stages to promotegood hand washing practices. A significant amount of existing research has focused on theproblem of tracking hands to enable hand gesture recognition. In this work, gesture trackingdevices and image processing are explored in the context of the hand washing environment.Hand washing videos of professional healthcare workers were carefully observed and analyzedin order to recognize hand features associated with hand hygiene stages that could be extractedautomatically. Selected hand features such as palm shape (flat or curved); palm orientation(palms facing or not); hand trajectory (linear or circular movement) were then extracted andtracked with the help of a 3D gesture tracking device - the Leap Motion Controller. These fea-tures were further coupled together to detect the execution of a required WHO - hand hygienestage,Rub hands palm to palm, with the help of the Leap sensor in real time. In certain conditions, the Leap Motion Controller enables a clear distinction to be made between the left andright hands. However, whenever the two hands came into contact with each other, sensor data from the Leap, such as palm position and palm orientation was lost for one of the two hands.Hand occlusion was found to be a major drawback with the application of the device to this usecase. Therefore, RGB digital cameras were selected for further processing and tracking of the hands. An image processing technique, using a skin detection algorithm, was applied to extractinstantaneous hand positions for further processing, to enable various hand hygiene poses to be detected. Contour and centroid detection algorithms were further applied to track the handtrajectory in hand hygiene video recordings. In addition, feature detection algorithms wereapplied to a hand hygiene pose to extract the useful hand features. The video recordings did not suffer from occlusion as is the case for the Leap sensor, but the segmentation of one handfrom another was identified as a major challenge with images because the contour detectionresulted in a continuous mass when the two hands were in contact. For future work, the datafrom gesture trackers, such as the Leap Motion Controller and cameras (with image processing)could be combined to make a robust hand hygiene gesture classification system

Pictures in Your Mind: Using Interactive Gesture-Controlled Reliefs to Explore Art

Tactile reliefs offer many benefits over the more classic raised line drawings or tactile diagrams, as depth, 3D shape, and surface textures are directly perceivable. Although often created for blind and visually impaired (BVI) people, a wider range of people may benefit from such multimodal material. However, some reliefs are still difficult to understand without proper guidance or accompanying verbal descriptions, hindering autonomous exploration. In this work, we present a gesture-controlled interactive audio guide (IAG) based on recent low-cost depth cameras that can be operated directly with the hands on relief surfaces during tactile exploration. The interactively explorable, location-dependent verbal and captioned descriptions promise rapid tactile accessibility to 2.5D spatial information in a home or education setting, to online resources, or as a kiosk installation at public places. We present a working prototype, discuss design decisions, and present the results of two evaluation studies: the first with 13 BVI test users and the second follow-up study with 14 test users across a wide range of people with differences and difficulties associated with perception, memory, cognition, and communication. The participant-led research method of this latter study prompted new, significant and innovative developments

Manoeuvring drone (Tello Talent) using eye gaze and or fingers gestures

The project aims to combine hands and eyes to control a Tello Talent drone based on computer vision, machine learning and an eye tracking device for gaze detection and interaction. The main purpose of this project is gaming, experimental and educational for next coming generation, in addition it is very useful for the peoples who cannot use their hands, they can maneuver the drone by their eyes movement, and hopefully this will bring them some fun. The idea of this project is inspired by the progress and development in the innovative technologies such as machine learning, computer vision and object detection that offer a large field of applications which can be used in diverse domains, there are many researcher are improving, instructing and innovating the new intelligent manner for controlling the drones by combining computer vision, machine learning, artificial intelligent, etc. This project can help anyone even the people who they don¿t have any prior knowledge of programming or Computer Vision or theory of eye tracking system, they learn the basic knowledge of drone concept, object detection, programing, and integrating different hardware and software involved, then playing. As a final objective, they can able to build simple application that can control the drones by using movements of hands, eyes or both, during the practice they should take in consideration the operating condition and safety required by the manufacturers of drones and eye tracking device. The concept of Tello Talent drone is based on a series of features, functions and scripts which are already been developed, embedded in autopilot memories and are accessible by users via an SDK protocol. The SDK is used as an easy guide to developing simple and complex applications; it allows the user to develop several flying mission programs. There are different experiments were studied for checking which scenario is better in detecting the hands movement and exploring the keys points in real-time with low computing power computer. As a result, I find that the Google artificial intelligent research group offers an open source platform dedicated for developing this application; the platform is called MediaPipe based on customizable machine learning solution for live streaming video. In this project the MediaPipe and the eye tracking module are the fundamental tools for developing and realizing the application

Interaction Methods for Smart Glasses : A Survey

Since the launch of Google Glass in 2014, smart glasses have mainly been designed to support micro-interactions. The ultimate goal for them to become an augmented reality interface has not yet been attained due to an encumbrance of controls. Augmented reality involves superimposing interactive computer graphics images onto physical objects in the real world. This survey reviews current research issues in the area of human-computer interaction for smart glasses. The survey first studies the smart glasses available in the market and afterwards investigates the interaction methods proposed in the wide body of literature. The interaction methods can be classified into hand-held, touch, and touchless input. This paper mainly focuses on the touch and touchless input. Touch input can be further divided into on-device and on-body, while touchless input can be classified into hands-free and freehand. Next, we summarize the existing research efforts and trends, in which touch and touchless input are evaluated by a total of eight interaction goals. Finally, we discuss several key design challenges and the possibility of multi-modal input for smart glasses.Peer reviewe