3D Gesture Recognition and Tracking for Next Generation of Smart Devices
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Gesture Recognition for Human-Robot Collaboration: a Review
International Journal of Industrial Ergonomics xxx (2017) 1e13 Contents lists available at ScienceDirect International Journal of Industrial Ergonomics journal homepage: www.elsevier.com/locate/ergon Gesture recognition for human-robot collaboration: A review * Hongyi Liu, Lihui Wang KTH Royal Institute of Technology, Department of Production Engineering, Stockholm, Sweden article info abstract Article history: Recently, the concept of human-robot collaboration has raised many research interests. Instead of robots Received 20 July 2016 replacing human workers in workplaces, human-robot collaboration allows human workers and robots Received in revised form working together in a shared manufacturing environment. Human-robot collaboration can release hu- 15 November 2016 man workers from heavy tasks with assistive robots if effective communication channels between Accepted 15 February 2017 humans and robots are established. Although the communication channels between human workers and Available online xxx robots are still limited, gesture recognition has been effectively applied as the interface between humans and computers for long time. Covering some of the most important technologies and algorithms of Keywords: Human-robot collaboration gesture recognition, this paper is intended to provide an overview of the gesture recognition research Gesture and explore the possibility to apply gesture recognition in human-robot collaborative manufacturing. In Gesture recognition this paper, an overall model of gesture recognition for human-robot collaboration is also proposed. There are four essential technical components in the model of gesture recognition for human-robot collabo- ration: sensor technologies, gesture identification, gesture tracking and gesture classification. Reviewed approaches are classified according to the four essential technical components. Statistical analysis is also presented after technical analysis. -
Inverse Kinematic Infrared Optical Finger Tracking
Inverse Kinematic Infrared Optical Finger Tracking Gerrit Hillebrand1, Martin Bauer2, Kurt Achatz1, Gudrun Klinker2 1Advanced Realtime Tracking GmbH ∗ Am Oferl¨ 3 Weilheim, Germany 2Technische Universit¨atM¨unchen Institut f¨urInformatik Boltzmannstr. 3, 85748 Garching b. M¨unchen, Germany e·mail: [email protected] Abstract the fingertips are tracked. All further parameters, such as the angles between the phalanx bones, are Human hand and finger tracking is an important calculated from that. This approach is inverse input modality for both virtual and augmented compared to the current glove technology and re- reality. We present a novel device that overcomes sults in much better accuracy. Additionally, the the disadvantages of current glove-based or vision- device is lightweight and comfortable to use. based solutions by using inverse-kinematic models of the human hand. The fingertips are tracked by an optical infrared tracking system and the pose of the phalanxes is calculated from the known anatomy of the hand. The new device is lightweight and accurate and allows almost arbitrary hand movement. Exami- nations of the flexibility of the hand have shown that this new approach is practical because ambi- guities in the relationship between finger tip posi- tions and joint positions, which are theoretically possible, occur only scarcely in practical use. Figure 1: The inverse kinematic infrared optical finger tracker Keywords: Optical Tracking, Finger Tracking, Motion Capture 1 Introduction 1.1 Related Work While even a standard keyboard can be seen For many people, the most important and natural as an input device based on human finger mo- way to interact with the environment is by using tion, we consider only input methods that allow their hands. -
Study of Gesture Recognition Methods and Augmented Reality
Study of Gesture Recognition methods and augmented reality Amol Palve ,Sandeep Vasave Department of Computer Engineering MIT COLLEGE OF ENGINEERING ( [email protected] ,[email protected]) Abstract: However recognizing the gestures in the noisy background has always been a With the growing technology, we difficult task to achieve. In the proposed humans always need something that stands system, we are going to use one such out from the other thing. Gestures are most technique called Augmentation in Image desirable source to Communicate with the processing to control Media Player. We will Machines. Human Computer Interaction recognize Gestures using which we are going finds its importance when it comes to to control the operations on Media player. working with the Human gestures to control Augmentation usually is one step ahead when the computer applications. Usually we control it comes to virtual reality. It has no the applications using mouse, keyboard, laser restrictions on the background. Moreover it pointers etc. but , with the recent advent in also doesn’t rely on certain things like gloves, the technology it has even left behind their color pointers etc. for recognizing the gesture. usage by introducing more efficient This system mainly appeals to those users techniques to control applications. There are who always looks out for a better option that many Gesture Recognition techniques that makes their interaction with computer more have been implemented using image simpler or easier. processing in the past. Keywords: Gesture Recognition, Human Computer Interaction, Augmentation I.INTRODUCTION: Augmentation in controlling the media player operations. Media player always In Human Computer interaction, the first requires mouse or keyboard to control it. -
Airpen: a Touchless Fingertip Based Gestural Interface for Smartphones and Head-Mounted Devices
AirPen: A Touchless Fingertip Based Gestural Interface for Smartphones and Head-Mounted Devices Varun Jain Ramya Hebbalaguppe [email protected] [email protected] TCS Research TCS Research New Delhi, India New Delhi, India ABSTRACT Hand gestures are an intuitive, socially acceptable, and a non-intrusive interaction modality in Mixed Reality (MR) and smartphone based applications. Unlike speech interfaces, they tend to perform well even in shared and public spaces. Hand gestures can also be used to interact with smartphones in situations where the user’s ability to physically touch the device is impaired. However, accurate gesture recognition can be achieved through state-of-the-art deep learning models or with the use of expensive sensors. Despite the robustness of these deep learning models, they are computationally heavy and memory hungry, and obtaining real-time performance on-device without additional hardware is still a challenge. To address this, we propose AirPen: an analogue to pen on paper, but in air, for in-air writing and gestural commands that works seamlessly in First and Second Person View. The models are trained on a GPU machine and ported on an Android smartphone. AirPen comprises of three deep learning models that work in tandem: MobileNetV2 for hand localisation, our custom fingertip regression architecture followed by a Bi-LSTM model for gesture classification. The overall framework works in real-time on mobile devices and achieves a classification accuracy of 80% with an average latency of only 0:12 s. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. -
Auraring: Precise Electromagnetic Finger Tracking
AuraRing: Precise Electromagnetic Finger Tracking FARSHID SALEMI PARIZI∗, ERIC WHITMIRE∗, and SHWETAK PATEL, University of Washington Fig. 1. AuraRing is 5-DoF electromagnetic tracker that enables precise, accurate, and fine-grained finger tracking for AR, VR and wearable applications. Left: A user writes the word "hello" in the air. Right: Using AuraRing to play a songinamusic application on a smart glass. Wearable computing platforms, such as smartwatches and head-mounted mixed reality displays, demand new input devices for high-fidelity interaction. We present AuraRing, a wearable magnetic tracking system designed for tracking fine-grained finger movement. The hardware consists of a ring with an embedded electromagnetic transmitter coil and a wristband with multiple sensor coils. By measuring the magnetic fields at different points around the wrist, AuraRing estimates thefive degree-of-freedom pose of the ring. We develop two different approaches to pose reconstruction—a first-principles iterative approach and a closed-form neural network approach. Notably, AuraRing requires no runtime supervised training, ensuring user and session independence. AuraRing has a resolution of 0:1 mm and a dynamic accuracy of 4:4 mm, as measured through a user evaluation with optical ground truth. The ring is completely self-contained and consumes just 2:3 mW of power. CCS Concepts: • Human-centered computing → Interaction devices; Ubiquitous and mobile computing. Additional Key Words and Phrases: Electromagnetic tracking, mixed reality, wearable, input, finger tracking ACM Reference Format: Farshid Salemi Parizi, Eric Whitmire, and Shwetak Patel. 2019. AuraRing: Precise Electromagnetic Finger Tracking. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 4, Article 150 (December 2019), 28 pages. -
Hand Interface Using Deep Learning in Immersive Virtual Reality
electronics Article DeepHandsVR: Hand Interface Using Deep Learning in Immersive Virtual Reality Taeseok Kang 1, Minsu Chae 1, Eunbin Seo 1, Mingyu Kim 2 and Jinmo Kim 1,* 1 Division of Computer Engineering, Hansung University, Seoul 02876, Korea; [email protected] (T.K.); [email protected] (M.C.); [email protected] (E.S.) 2 Program in Visual Information Processing, Korea University, Seoul 02841, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-2-760-4046 Received: 28 September 2020; Accepted: 4 November 2020; Published: 6 November 2020 Abstract: This paper proposes a hand interface through a novel deep learning that provides easy and realistic interactions with hands in immersive virtual reality. The proposed interface is designed to provide a real-to-virtual direct hand interface using a controller to map a real hand gesture to a virtual hand in an easy and simple structure. In addition, a gesture-to-action interface that expresses the process of gesture to action in real-time without the necessity of a graphical user interface (GUI) used in existing interactive applications is proposed. This interface uses the method of applying image classification training process of capturing a 3D virtual hand gesture model as a 2D image using a deep learning model, convolutional neural network (CNN). The key objective of this process is to provide users with intuitive and realistic interactions that feature convenient operation in immersive virtual reality. To achieve this, an application that can compare and analyze the proposed interface and the existing GUI was developed. Next, a survey experiment was conducted to statistically analyze and evaluate the positive effects on the sense of presence through user satisfaction with the interface experience. -
Lightweight Palm and Finger Tracking for Real-Time 3D Gesture Control
Lightweight Palm and Finger Tracking for Real-Time 3D Gesture Control Georg Hackenberg* Rod McCall‡ Wolfgang BrollD Fraunhofer FIT Fraunhofer FIT Fraunhofer FIT Freehand multi-touch has been explored within various ABSTRACT approaches, e.g. Oblong’s g-speak1, after initially having been We present a novel technique implementing barehanded introduced in the popular Hollywood movie “Minority Report”. interaction with virtual 3D content by employing a time-of-flight They usually depended heavily on hand-worn gloves, markers, camera. The system improves on existing 3D multi-touch systems wrists, or other input devices, and typically did not achieve the by working regardless of lighting conditions and supplying a intuitiveness, simplicity and efficiency of surface (2D) based working volume large enough for multiple users. Previous multi-touch techniques2. In contrast to those approaches, the goal systems were limited either by environmental requirements, of our approach was to use barehanded interaction as a working volume, or computational resources necessary for real- replacement for surface based interaction. time operation. By employing a time-of-flight camera, the system Only a vision-based approach will allow for freehand and is capable of reliably recognizing gestures at the finger level in barehanded 3D multi-touch interaction. The system must also real-time at more than 50 fps with commodity computer hardware provide sufficient solutions for the following four steps: detection using our newly developed precision hand and finger-tracking of hand position without prior knowledge of existence; for each algorithm. Building on this algorithm, the system performs appearance determine pose from image cues; track appearances gesture recognition with simple constraint modeling over over time; recognize gestures based on their trajectory and pose statistical aggregations of the hand appearances in a working information. -
Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision
Improving Touch Interfaces CHI 2017, May 6–11, 2017, Denver, CO, USA Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision Radu-Daniel Vatavu MintViz Lab MANSiD Research Center University Stefan cel Mare of Suceava Suceava 720229, Romania [email protected] Figure 1. Gesture articulations produced by people with low vision present more variation than gestures produced by people without visual impairments, which negatively affects recognizers’ accuracy rates. Ten (10) superimposed executions of a “star” gesture are shown for six people: a person without visual impairments (a); three people with congenital nystagmus and high myopia (b), (c), (d); and two people with chorioretinal degeneration (e), (f). ABSTRACT INTRODUCTION We contribute in this work on gesture recognition to improve Today’s touch screen devices are little accessible to people the accessibility of touch screens for people with low vision. with visual impairments, who need to employ workaround We examine the accuracy of popular recognizers for gestures strategies to be able to use them effectively and indepen- produced by people with and without visual impairments, and dently [17,18,42]. Because smart devices expose touch screens we show that the user-independent accuracy of $P, the best not adapted to non-visual input, touch and gesture inter- recognizer among those evaluated, is small for people with action pose many challenges to people with visual impair- low vision (83.8%), despite $P being very effective for ges- ments [11,12,20,30], which can only be addressed with a thor- tures produced by people without visual impairments (95.9%). -
Controlling Robots with Wii Gestures
WiiRobot: Controlling Robots with Wii Gestures Aysun Bas¸c¸etinc¸elik Department of Computer Science Brown University Providence, RI, 02906 [email protected] May 15, 2009 Abstract In this project, we introduce WiiRobot, a sim- ple and reproducable gesture based robot con- troller. Our proposed system uses the Nintendo Wii Remote Controller to interact with a robot. The user first trains the system using Wii Re- mote in order to recognize future gestures. For the recognition process, we implemented two simple algorithms; K-Nearest Neighbour and sum of square differences. Our results show that using only a few number of repetitions per gesture is enough for recognition. 1 Introduction Finding a natural and effective way of communication with robots is important in order to enhance their contribution in our daily lives. For this purpose, there have been various developments in the area of Human- Figure 1: User controlling the robot with Wii Remote Robot Interaction. It is known that using a computer, a gamepad or a keyboard can be teleoperative but it is still unnatural and difficult to control. Hence, it is our fingers or with a mouse. We use gestures while we still one of the major research directions to design ease- are doing work with our laptops and phones. Moreoever, of-use and intuitive modes of communication with robots. after the introduction of Nintendo Wii Console, we were introduced to a new hardware, Wii Remote Controller, Another form of communication is gestures. Currently, which has the capability of creating gestures and using it gestures are not just limited to face, hand and body for a different intuitive control of video games. -
Perception-Action Loop in the Experience of Virtual Environments
Perception-Action Loop in the Experience of Virtual Environments by Seung Wook Kim A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Architecture and the Designated Emphasis in New Media in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Yehuda E. Kalay, Chair Professor Galen Cranz Professor John F. Canny Fall 2009 Abstract Perception-Action Loop in the Experience of Virtual Environments by Seung Wook Kim Doctor of Philosophy in Architecture and the Designated Emphasis in New Media University of California, Berkeley Professor Yehuda Kalay, Chair The goal of this work is to develop an approach to the design of natural and immersive interaction methods for three-dimensional virtual environments. My thesis is that habitation and presence in any environment are based on a continuous process of perception and action between a person and his/her surroundings. The current practice of virtual environments, however, disconnects this intrinsic loop, separating perception and action into two different ‘worlds’—a physical one (for perception) and a virtual one (for action). This research is aimed at bridging the gap between those two worlds. Being drawn from perceptual philosophy and psychology, the theoretical study in this dissertation identifies three embodiments of natural perception-action loop: direct perceptual acts, proprioceptive locomotion, and motor intentionality. These concepts form the basis for the interaction methods proposed in this work, and I demonstrate these methods by implementing pertinent prototype systems. First, I suggest a view-dependent, non-planar display space that supports natural perceptual actions, thereby enhancing our field of view as well as depth perception. -
A Wii-Based Gestural Interface for Computer Conducting Systems
A Wii-based gestural interface for computer conducting systems Lijuan Peng David Gerhard Computer Science Department Computer Science Department University of Regina University of Regina SK, Canada, S4S 0A2 SK, Canada, S4S 0A2 [email protected] [email protected] Abstract This system is developed using Max/MSP/Jitter 1 and Java. With the increase of sales of Wii game consoles, it is be- The user stands in front of the computer and the Wii remote coming commonplace for the Wii remote to be used as an is mounted with the camera pointing toward the user. The alternative input device for other computer systems. In this accelerometer functionality and buttons on the Wii remote paper, we present a system which makes use of the infrared are ignored. Unlike other video-camera-based infrared sys- camera within the Wii remote to capture the gestures of a tems, no positional configuration is required. The infrared conductor using a baton with an infrared LED and battery. baton is held in the user’s right hand. It should be noted that Our system then performs data analysis with gesture classi- as long as the camera in the Wii remote can ”see” the entire fication and following, and finally displays the gestures us- conducting window, the specific location of the Wii remote ing visual baton trajectories and audio feedback. Gesture is irrelevant. The setup should be configured so that the user trajectories are displayed in real time and can be compared has full range of conducting motion and is comfortable dur- to the corresponding diagram shown in a textbook. -
Analysis and Categorization of 2D Multi-Touch Gesture Recognition Techniques THESIS Presented in Partial Fulfillment of the Requ
Analysis and Categorization of 2D Multi-Touch Gesture Recognition Techniques THESIS Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Aditi Singhal Graduate Program in Computer Science and Engineering The Ohio State University 2013 Master's Examination Committee: Dr. Rajiv Ramnath, Adviser Dr. Jay Ramanathan, Co-Adviser Copyright by Aditi Singhal 2013 Abstract Various techniques have been implemented for wide variety of gesture recognition applications. However, discerning the best technique to adopt for a specific gesture application is still a challenge. Wide variety, complexity in the gesture and the need for complex algorithms make implementation of recognition difficult even for basic gesture applications. In this thesis, different techniques for two dimensional (2D) touch gestures are reviewed, compared and categorized based on user requirements of the gesture applications. This work introduces two main paradigms for gesture applications: a) gestures for direct manipulation and navigation, b) gesture-based languages. These two paradigms have specific and separate roles in 2D touch gesture systems. To provide the clear distinction between two paradigms, three different algorithms are implemented for basic to complex 2D gestures using simple sometimes, as well as complex techniques such as linear regression and Hidden Markov Models. Thereafter, these algorithms are analyzed based on their performance and their fit with application requirements. ii Dedication I lovingly dedicate this thesis to my husband Shashnk Agrawal, who supported me each step of the way, my son Aryan Agrawal who in his 3 year old wisdom knew that mommy should not be disturbed while studying, and my late father Dr.