DOCSLIB.ORG

Study of Gesture Recognition Methods and Augmented Reality

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read 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. and foremost thing is the interfacing. There Every media player application when its are many techniques that have been active has the basic operations on it. It has implemented so far as interfacing also got some shortcut keys that requires the techniques. Gesture Plays an important role keyboard, to perform some action related while interacting with the Computer with every key. But when the keyboard applications. It enables humans to malfunctions or not working these things communicate with the machine and interact become somewhat difficult to control. In without any mechanical device. In Gesture this case using Gestures one can handle any Recognition , there has been always an issue application. Augmented reality makes it about how to interact with the Computer. very user friendly to control any application Various bodily gestures are used to interact using gestures. with Computer, they include, hands , face, legs, eyes etc but most commonly used are II.Existing system: hands and face. Hand gestures can be used as various commands to operate certain Gesture Recognition aims to interpret applications. But main problem in Hand human gestures via Mathematical gestures is they totally depends on the Algorithms. There have been many gesture database that is the storage space for their recognition techniques that are presently gesture matching. They are static gestures worked on such as Color pointer techniques that needs memory to store the images and many more. learn a particular gesture. For tackling this A.Hand gesture one of the problem, Augmentation, was introduced as Human Computer Interaction Modeling of hand gesture mainly require technology, that aims to combine the 2D or gesture regniation which provide new way ta 3D virtual objects with the real world access machine[4].for modeling mainly pictures[1]. Augmentation also called as consider two methods which famously Augmented Reality or Virtual Reality, is knows as spatial and temporial method .this more user friendly. So far we were very two method used in different situation used too on the devices like keyboard, spatial used where shape is important factor mouse etc. This is one such technique that while temporial used where motion of hand eliminate their usage. The proposed paper is consider[4].in 2D and 3D Implementation focuses on Gesture Recognition using of hand modeling spatial is important one[5]. Fig no1.Hand modeling method Shape having two types geometric and non color and other facters.there are many geometric[6].geometric mainly related to the methods given below which is helpful for motion and finger postion and palm hand modeling system properties.while non-geometric consider Fig no 2.Methods Various hand modeling methods to represent hand posture [7]. (a) 3D volumetric model. (b) 3Dgeometric model. (c) 3D Skeleton model. (d) Colored marker based model. (e) Non-geometric shape model(Binary silhouette [5]). (f) 2D deformable template model . Motion based model. We study different techniques color area the it may confuse to select pointer technique which involves using object[8].also there are skeleton based various color pointers. These color pointers method where noise hand diction is very are used on the fingers for Hand gesture difficult one task[8].also all this method recognition[8]. A very popular example of affecting with lighting condition [8].and also this has been covered by the Pranav Mistry all are static in nature where base image on the Sixth Sense. Another very popular required. scale problem come where system that exists is the wearable glove that different size of hand comes in picture .back is used for tracking fingers and hand which ground is also important one[9]. so we have is a Computer vision based recognition. to come up with new idea which There exist the optic light based technology considerably remove all of this drawback in which does not usually require user to that case augmented reality is better interact with device by physically touching solution. anything, simple hand motions and gestures can be used to interact with the device[2]. III.Augmented reality The color models that were used so far was Augmented reality is combination of real totally based on the RGB .but all methods world with virtual environment .where it is having some limit on like data glove in used in many areas like medical and other which it is compulsory to wear data glove areas. in gaming system it play important every time and also which are more role[10]. All the draw back which face expensive one[8].while color pointer previously can remove here basic system require that to wear color pointer on hand architecture [10]. and if same color appear within regnition Fig no3.Architeture IV.Proposed System for hand detection: Ppp Fig no4.system architecture In the Proposed system, the main focus is on After capture image we have to blur image Recognizing Dynamic Gestures of the user so sharpness reduce so that we get accurete via a Webcam which could be built-in or dectcion. externally used Camera. Analyzing the gestures and applying the Image Processing B.RGB TO HSV – algorithms like RBG to HSV conversion, RGB gives more specific value related to blurring, thresholding, blob detection etc[1]. image where we require saturated value Algorithms- related that image so we convert into HSV. A.Bluring image- C. Thresholding - Where segmentation can be done easily with the help of thresholding. D. Blob detecting – operations of the application .Virtual menu will be displayed on the live captured feed which mainly used to find out particular area for action selection. User can directly point from the image. towards the button/action for dynamic input. User can also point with the bare hands or using glove or any other object as well. V.Application- There will be no restrictions on the background. So Augmentation has the Using Augmented Reality the gesture will advantage for its efficiency. be detected for carrying out certain PLAY STOP PREVIOUS NEXT Fig no5.augmented system In the following diagram, there is a virtual interface between the user and the computer. menu displayed on the screen , the User User can control any applications in the near gesture will be feeder through the Webcam future using Augmented Reality. User can and based on the position of the user hands, directly point to the screen and feed the the gesture will analyzed for the particular gestures to control the various functions of Media Player function to be performed. the Media Player. There is more accuracy Media Player has to be a active application rate in the Augmented Reality. while using this system. Augmentation surely is most promising Human Computer Interaction Technologies VI.Conclusion: to come across. It has a great potential in the This paper presents the system can handle coming future to mark a better efficiency in with a Augmented Reality technique. This the e-commerce systems as well. also focuses on a more User friendly This system has the is no need of the databases as it is totally advantage over other system that, it does not works on dynamic gestures behaviour. have restrictions on the background. There Dynamic Gestures through live video feed and directly applying the image processing [7] Mohamed B. Kaaniche, (2009) “Human to give output. Also the color model that is Gesture Recognition” PowerPoint slides. used is very strong. Gesture Recognition as [8]Z Ren,J Yuan,J Meng and Z a user interface also applies to a wide range Zhang(2013)”Robust Part-based Hand of consumer electronics using Gesture Recognition Using Kinect Augmentation. Sensor”IEEE Transaction on multimedia vol,15 no 5,Augest 2013. [9] Takahiro Nishi, Yoichi Sato and Hideki REFERENCES: Koike” Interactive Object Registration and Recognition for Augmented Desk Interface” [1]. Waghmare Amit B., Sonawane Kunal Extended Abstract of ACM SIGCHI 2001, S., Chavan Puja A.,”Augmented Reality for pp. 371-372, April 2001. Information kiosk” [10] Yoki Ariyana#*1, Aciek Ida Wuryandari #2.” Basic 3D Interaction Techniques [2]. James Cannan and Huosheng Hu , in Augmented Reality”, 2012 International Human-Machine Interaction (HMI): Conference on System Engineering and A Survey Technology September 11-12, 2012, Bandung, Indonesia [3] www.wikipedia.com [4] P.
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]
  • 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]
  • Bringing Gesture Recognition to All Devices
    Bringing Gesture Recognition To All Devices Bryce Kellogg†, Vamsi Talla†, and Shyamnath Gollakota University of Washington † Co-primary Student Authors Abstract Existing gesture-recognition systems consume signifi- cant power and computational resources that limit how they may be used in low-end devices. We introduce AllSee, the first gesture-recognition system that can op- erate on a range of computing devices including those with no batteries. AllSee consumes three to four or- ders of magnitude lower power than state-of-the-art sys- tems and can enable always-on gesture recognition for smartphones and tablets. It extracts gesture information from existing wireless signals (e.g., TV transmissions), but does not incur the power and computational over- heads of prior wireless approaches. We build AllSee prototypes that can recognize gestures on RFID tags and power-harvesting sensors. We also integrate our hard- Figure 1: AllSee Prototype. It has two main compo- ware with an off-the-shelf Nexus S phone and demon- nents: a pluggable receiver that extracts the amplitude strate gesture recognition in through-the-pocket scenar- of wireless signals (e.g., TV and RFID transmissions), ios. Our results show that AllSee achieves classification and our classification logic implemented on a microcon- accuracies as high as 97% over a set of eight gestures. troller. It also comes with LEDs and a UART interface. use power-intensive analog components such as oscilla- 1 Introduction tors and high-speed ADCs. Second, they require rich sig- nal processing capabilities — computing optical flows, There is growing interest in using human gestures as a FFTs, frequency-time Doppler profiles, etc., is compu- means of interaction with computing devices.
    [Show full text]