DOCSLIB.ORG

3D Gesture-Based Interaction for Immersive Experience in Mobile VR

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
3D Gesture-Based Interaction for Immersive Experience in Mobile VR 2016 23rd International Conference on Pattern Recognition (ICPR) Cancún Center, Cancún, México, December 4-8, 2016 3D Gesture-based Interaction for Immersive Experience in Mobile VR Shahrouz Yousefi∗, Mhretab Kidaney, Yeray Delgadoy, Julio Chanay and Nico Reski∗ ∗Department of Media Technology, Linnaeus University, Vaxjo, Sweden Email: shahrouz.yousefi@lnu.se, [email protected] yManoMotion AB, Stockholm, Sweden Email: [email protected], [email protected], [email protected] Abstract—In this paper we introduce a novel solution for and will not satisfy the users’ needs in the near future. The real-time 3D hand gesture analysis using the embedded 2D presented research results enable large-scale and real-time 3D camera of a mobile device. The presented framework is based gesture analysis. This can be used for user-device interaction in on forming a large database of hand gestures including the ground truth information of hand poses and details of finger real-time applications in mobile and wearable devices, where joints in 3D. For a query frame captured by the mobile device’s intuitive, and instant 3D interaction are important. VR, AR, camera in real time, the gesture analysis system finds the and 3D gaming are among the areas that directly benefit from best match from the database. Once the best match is found, the 3D interaction technology. Specifically, with the presented the corresponding ground truth information will be used for research results we plan to observe how the integration of interaction in the designed interface. The presented framework performs an extremely efficient gesture analysis (more than 30 new frameworks such as Search methods to existing computer fps) in flexible lighting condition and complex background with vision solutions facilitates the high degrees of freedom gesture dynamic movement of the mobile device. The introduced work analysis. In the presented work, Gesture Search Engine, as an is implemented in Android and tested in Gear VR headset. innovative framework, for 3D gesture analysis is introduced and used in mobile platforms to facilitate the AR/VR appli- I. INTRODUCTION cations. Prototypes in simple application scenarios have been The rapid development and wide adoption of mobile devices demonstrated based on the proposed technology. in recent years have been mainly driven by the introduction of novel interaction and visualization technologies. Although II. RELATED WORK touchscreens have significantly enhanced the human device One of the current enabling technologies to build gesture- interaction, clearly for next generation of smart devices such as based interfaces is hand tracking and gesture recognition. Virtual/Augmented Reality (VR/AR) headsets, smart watches, The major technology bottleneck lies in the difficulty of and future smartphones/tablets, users will no longer be satis- capturing and analyzing the articulated hand motions. One fied with just performing interaction over the limited space of of the existing solutions is to employ glove-based devices, 2D touchscreen or using extra controllers. They will demand which directly measure the finger positions and joint angles more natural interactions performed by the bare hands in the by using a set of sensors (i.e. electromagnetic or fiber-optical free space around the smart device [1]. Thus, the next gener- sensors) [8], [9]. However, the glove-based solutions are too ation of smart devices will require a gesture-based interface intrusive and expensive for natural interaction with smart to facilitate the bare hands for manipulating digital content devices. To overcome these limitations, vision-based hand directly. In general, 3D hand gesture recognition and tracking tracking solutions need to be developed and video sequences have been considered as classical computer vision and pattern should be analyzed. Capturing hand and finger motions in recognition problems. Although substantial research has been video sequences is a highly challenging task in computer conducted in this area, the state-of-the-art research results are vision due to the large number of DOF of the hand kinematics. mainly limited to global hand tracking and low-resolution Recently, Microsoft demonstrated how to capture full body gesture analysis [2]. However, in order to facilitate the natural motions using Kinect [10], [11]. Substantial development of gesture-based interaction, full analysis of the hand and fingers hand tracking and gesture recognition are based on using depth will be required, which in total incorporates 27 degrees of sensors. Sridhar et al., [12] use RGB and depth data for track- freedom (DOF) for each hand [3]. The main objectives behind ing of articulated hand motion based on color information and this work are to introduce new frameworks and methods for part-based hand model. Oikonomidis et al., [13] and Taylor intuitive interaction with future smart devices. We aim to re- et al., [14] track the articulated hand motion using RGBD produce the real-life experiences in the digital space with high information from Kinect. Papoutsakis et al., [15] analyze accuracy hand gesture analysis. Based on the comprehensive limited number of hand gestures with RGBD sensor. Model- studies [4], [5], [6], [7], and feedback from top researchers in based hand tracking using depth sensor is among the com- the field and major technology developers, it has been clearly mon proposed solutions [14], [16]. Oikonomidis et al., [13], verified that today’s 3D gesture analysis methods are limited introduce articulated hand tracking using calibrated multi- 978-1-5090-4846-5/16/$31.00 ©2016 IEEE 2122 camera system and optimization methods. Balan et al., [17] the 3D hand model with the observed image from the camera, propose a model-based solution to estimate the pose of two and minimizing the discrepancy between them. Generally, it is hands using discriminative salient points. Here, the question easier to achieve real-time performance with appearance-based is whether using 3D depth cameras can potentially solve the approaches due to the fact of simpler 2D image features [2]. problem of 3D hand tracking and gesture recognition. This However, this type of approaches can only handle simple hand problem has been greatly simplified by the introduction of gestures, like detection and tracking of fingertips. In contrast, real-time depth cameras. However, the technologies based on 3D hand model based approaches offer a rich description that depth information for hand tracking and gesture recognition potentially allows a wide class of hand gestures. The bad news still face major challenges for mobile applications. In fact, is that the 3D hand model is a complex articulated deformable mobile applications have at least two critical requirements: object with 27 DOF. To cover all the characteristic hand computational efficiency and robustness. Therefore, feedback images under different views, a very large image database is and interaction in a timely fashion is assumed and any latency required. Matching the query images from the video input with should not be perceived as unnatural to the human participant. all hand images in the database is computationally expensive. It is doubtful if most existing technical approaches, including This is why the most existing model-based approaches focus the one used in Kinect body tracking system would be the on real-time tracking for global hand motions with restricted direction leading to the technical development for future lighting and background conditions. To handle the challenging smart devices due to their inherent resource-intensive nature. search problem in a high dimensional space of human hands, Another issue is the robustness. The solutions for mobile the efficient index technologies used in information retrieval applications should always work no matter indoor or outdoor. field, have been tested. Zhou et al. proposed an approach This may somehow exclude the possibility of using Kinect- that integrates the powerful text retrieval tools with computer type sensors in uncontrolled environments. Therefore, the vision techniques in order to improve the efficiency for image original problem is how to provide effective hand tracking and retrieval [26]. An Okapi-Chamfer matching algorithm is used gesture recognition with video cameras. A critical question is in their work based on the inverted index technique. Athitsos whether we could develop alternative video-based solutions et al., proposed a method that can generate a ranked list of that may fit future mobile applications better. 3D hand configurations that best match an input image [27]. 1) Bare-hand Gesture Recognition and Tracking: Algo- Hand pose estimation is achieved by searching for the closest rithms of hand tracking and gesture recognition can be grouped matches for an input hand image from a large database of syn- into two categories: appearance-based approaches and 3D thetic hand images. The novelty of their system is the ability hand model-based approaches [3], [18], [19], [20], [21], [22]. to handle the presence of clutter. Imai et al. proposed a 2D Appearance-based approaches are based on a direct compar- appearance-based method to estimate 3D hand posture [28]. In ison of hand gestures with 2D image features. The popular their method, the variations of possible hand contours around image features used to detect human hands include hand the registered typical appearances are trained from a number of colors and shapes, local hand features, optical flow and so graphical images generated from a 3D hand model. Although on. The early works on hand tracking belong to this type of the methods based on retrieval are very promising, they are approaches [4], [5], [23]. The gesture analysis step usually too few to be visible in the field. The reason might be that includes feature extraction, gesture detection, motion analysis the approach is too primary, or the results are not impressive and tracking. Pattern recognition methods for detecting and due to the tests just over very limited size of database. analyzing the hand gestures are mainly based on local or Moreover, it might be also a consequence of the success of 3D global image features.
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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%).
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Gesture Recognition Using 3D Appearance and Motion Features
    Gesture Recognition Using 3D Appearance and Motion Features Guangqi Ye, Jason J. Corso, Gregory D. Hager Computational Interaction and Robotics Laboratory The Johns Hopkins University [email protected] Abstract Alternatively, methods that attempt to capture the 3D infor- mation of the hand [11] have been proposed. However, it is We present a novel 3D gesture recognition scheme that com- well-known that, in general circumstances, the stereo prob- bines the 3D appearance of the hand and the motion dynam- lem is difficult to solve reliably and efficiently. ics of the gesture to classify manipulative and controlling Human hands and arms are highly articulate and de- gestures. Our method does not directly track the hand. In- formable objects and hand gestures normally consist of 3D stead, we take an object-centered approach that efficiently global and local motion of the hands and the arms. Manip- computes the 3D appearance using a region-based coarse ulative and interaction gestures [14] have a temporal nature stereo matching algorithm in a volume around the hand. that involve complex changes of hand configurations. The The motion cue is captured via differentiating the appear- complex spatial properties and dynamics of such gestures ance feature. An unsupervised learning scheme is carried render the problem too difficult for pure 2D (e.g. template out to capture the cluster structure of these feature-volumes. matching) methods. Ideally we would capture the full 3D Then, the image sequence of a gesture is converted to a se- information of the hands to model the gestures [11]. How- ries of symbols that indicate the cluster identities of each ever, the difficulty and computational complexity of visual image pair.
    [Show full text]
  • Augmented Reality by Using Hand Gesture
    International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 3 Issue 4, April - 2014 Augmented Reality by using Hand Gesture Jyoti Gupta Amruta Bankar Mrunmayi Warankar Anisha Shelke Computer Department Computer Department Computer Department Computer Department Dr. D.Y. Patil COE Pune, Dr. D.Y. Patil COE Pune, Dr. D.Y. Patil COE Pune, Dr. D.Y. Patil COE Pune, India India India India Abstract — In this paper we have discussed a human-computer mixed scene can then be displayed on different devices. interaction interface (HCI) based on Hand gesture recognition. It Between the two prominent AR methods, video-based AR has is a challenging problem in its general form. We consider a set of attracted the most attention from researchers. manual commands and a reasonably structured environment, From study of Augmented Reality we are developing an and develop a simple, yet effective, procedure for gesture application in which we can navigate a virtual 3D model using recognition. Our approach contains steps for segmenting the hand region, locating the fingers, and finally classifying the hand gesture. In this the user interact with webcam and gesture. The algorithm is invariant to translation, rotation, and different virtual symbols on screen are provided for navigating scale of the hand. We demonstrate the effectiveness of the 3D object, user will select a particular button from screen by technique on real imagery. placing his hand in front of that particular virtual key. We use However, technology is progressing. In particular, Augmented Image Processing algorithms like Grayscaling [9], Blurring Reality (AR), an emerging Human-Computer Interaction [6], Thresholding [7], HSV model [15], Blob Detection [8] for technology, which aims to mix or overlap computer generated 2D recognition of hand.
    [Show full text]
  • Chapter 10. Gesture Recognition Matthew Turk 1. Introduction
    Chapter 10. Gesture Recognition Matthew Turk 1. Introduction .................................................................................................................................................................. 1 2. The Nature of Gesture ................................................................................................................................................. 3 3. Representations of Gesture ........................................................................................................................................ 6 3.1 Pen-based gesture recognition............................................................................................................................ 8 3.2 Tracker-based gesture recognition ..................................................................................................................... 9 3.2.1 Instrumented gloves ...................................................................................................................................... 9 3.2.2 Body suits ..................................................................................................................................................... 11 3.3 Passive vision-based gesture recognition....................................................................................................... 12 3.3.1 Head and face gestures ............................................................................................................................... 15 3.3.2 Hand and arm gestures...............................................................................................................................
    [Show full text]
  • Gesture Recognition on Few Training Data Using Slow Feature Analysis and Parametric Bootstrap
    Gesture Recognition on Few Training Data using Slow Feature Analysis and Parametric Bootstrap Patrick Koch, Wolfgang Konen, and Kristine Hein Abstract— Slow Feature Analysis (SFA) has been established clearly differs from traditional game controllers. But even as a robust and versatile technique from the neurosciences to though there exist a large number of games for the Nintendo learn slowly varying functions from quickly changing signals. Wii, recognition of complex gestures is still a challenging Recently, the method has been also applied to classification tasks. Here we apply SFA for the first time to a time series task. In spite of the large number of classification approaches classification problem originating from gesture recognition. The using the Wiimote controller with the infrared device, e.g. the gestures used in our experiments are based on acceleration work from Lee [Lee08], we focus here on approaches where signals of the Bluetooth Wiimote controller (Nintendo). We no infrared device is used. Although the task becomes more show that SFA achieves results comparable to the well-known difficult then, we are not so dependent on the Wii structure Random Forest predictor in shorter computation time, given a sufficient number of training patterns. However – and this and can easily transfer our approach to other applications. is a novelty to SFA classification – we discovered that SFA This can be of high importance when the use of infrared requires the number of training patterns to be strictly greater is not possible for any reason and only acceleration based than the dimension of the nonlinear function space. If too few sensors are available.
    [Show full text]
  • AUTOMATIC RECOGNITION of CHILDREN's TOUCHSCREEN STROKE GESTURES by ALEX SHAW a DISSERTATION PRESENTED to the GRADUATE SCHOOL O
    AUTOMATIC RECOGNITION OF CHILDREN'S TOUCHSCREEN STROKE GESTURES By ALEX SHAW A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2020 c 2020 Alex Shaw ACKNOWLEDGMENTS I would like to thank my advisor, Dr. Lisa Anthony, for her continued support, encouragement, and advice throughout my PhD. I thank my co-advisor, Dr. Jaime Ruiz, as well as the rest of my committee, for their invaluable support through the process. Finally, I thank my labmates for being excellent coworkers and I thank my parents for their support through the long journey that obtaining a PhD has been. 3 TABLE OF CONTENTS page ACKNOWLEDGMENTS..................................3 LIST OF TABLES......................................7 LIST OF FIGURES.....................................8 ABSTRACT......................................... 13 CHAPTER 1 INTRODUCTION................................... 15 1.1 Contributions................................... 17 2 RELATED WORK................................... 19 2.1 Developmentally Appropriate Prompts and Feedback.............. 20 2.2 Selection of Gestures............................... 21 2.3 Recognition and Classification of Children's Gestures.............. 23 2.3.1 Challenges in Studying Children's Gestures............... 24 2.3.2 Summary................................. 26 3 RECOGNITION METHODS.............................. 27 3.1 Template Matchers............................... 27 3.2 Feature-based Statistical
    [Show full text]