Query: Gesture Recognition review

title: Real Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review
abstract: Today, daily life is composed of many computing systems, therefore interacting with them in a natural way makes the communication process more comfortable. Human Computer Interaction (HCI) has been developed to overcome the communication barriers between humans and computers. One form of HCI is Hand Gesture Recognition (HGR) which predicts the class and the instant of execution of a given movement of the hand. One possible input for these models is surface electromyography (EMG) which records the electrical activity of skeletal muscles. EMG signals contain information about the intention of movement generated by the human brain. This systematic literature review analyses the state of the art of real time hand gesture recognition models using EMG data and machine learning. We selected and assessed 65 primary studies following the Kitchenham methodology. Based on a common structure of machine learning based systems, we analyzed the structure of the proposed models and standardized concepts in regard to the types of models, data acquisition, segmentation, preprocessing, feature extraction, classification, postprocessing, real time processing, types of gestures, and evaluation metrics. Finally, we also identified trends and gaps that could open new directions of work for future research in the area of gesture recognition using EMG.

title: Hand Gesture Recognition Based on Computer Vision: A Review of Techniques
abstract: Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf mute people, robot control, human computer interaction (HCI) home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.

title: Perspective and Evolution of Gesture Recognition for Sign Language: A Review
abstract: This review analyses the different gesture recognition systems through a timeline, showing the different types of technology, and specifying which are the most important features and their achieved recognition rates. At the end of the review, Leap Motion sensor possibilities are described in detail, in order to consider its application on the field of sign language. This device has many positive characteristics that make it a good option for sign language. One of the most important conclusions is the ability of the Leap Motion sensor to provide 3D information from the hands for due identification.

title: A Review on Machine Learning and Gesture Recognition
abstract: This is a review on Machine Learning and Gesture Recognition based on researches on Machine Learning and Gesture Recognition. It also includes my personal interpretation and ideas for the future. By conducted an indepth study on the gesture recognition technology based on machine learning, researched on accurate segmentation algorithm of multi gesture images based on skin color, analyzed the distribution characteristics of skin color in different color spaces under different brightness, by combining gestures in actual scenes Segmentation quality to find the best color space for building skin model, researchers also developed and implemented a gesture recognition control system for home scenes: elaborating the hardware and software environment of the gesture recognition control system and explaining the gesture collection system and gesture recognition function. The system was tested in terms of function, performance, and the technical advantages to make sure the overall advantages of the system.

title: Interaction with Smartwatches Using Gesture Recognition: A Systematic Literature Review
abstract: Smartwatches are wearable devices of emerging mobile technology and are increasingly in people's daily lives. They allow you to perform various tasks; however, the interaction with these devices still proves to be a significant challenge. Several studies carried out interaction studies with smartwatches using gesture recognition. Thus, this work presents a comprehensive Systematic Review of Literature (SRL) on interaction with smartwatches using gesture recognition, showing what has already been developed and what is state of the art. We searched in four databases with relevant scientific scope: ACM Digital Library, IEEE Xplore Digital Library, Science Direct, and Scopus. Work on this theme is diverse; gesture recognition is used to perform operations on smartwatches itself or even control other devices or virtual environments. We hope that this work will provide a rich base on the methods developed and that it will become a source of research for future researchers and that can support the development of other methods and applications.

title: Hand Gesture Recognition Using Machine Learning Techniques: A Systematic Review
abstract: Hand gesture recognition framework got extraordinary consideration in the ongoing barely any years due to its complexness applications and the capacity to collaborate with machine effectively through human PC cooperation. Right now overview of ongoing hand gesture recognition frameworks is introduced. Key issues of hand gesture recognition framework are given difficulties of gesture framework. Audit techniques for late stances and gestures recognition framework introduced also. Outline of research aftereffects of hand gesture strategies, databases, and examination between principle gesture recognition stages are likewise given. Favorable circumstances and downsides of the talked about frameworks are clarified at last.

title: A Review of Sign Language Hand Gesture Recognition Algorithms
abstract: Research in hand gesture recognition has been going on for several decades due to its many application areas. One of the significant application areas is sign language recognition. Sign language recognition enables to facilitate in bridging the communication barrier that exists between the deaf and hearing people. However, obtaining a robust sign language hand gesture recognition system is still an onerous challenge. Therefore, this paper presents a comparative analysis of the recent algorithms that are applicable to sign language hand gesture recognition with respect to image segmentation and tracking, feature extraction, and gesture classification phases of a sign language hand gesture recognition system. This makes this study unique because many other reviews on hand gesture recognition are not specific to sign language application area. This paper is anticipated to offer important insights to researchers, developers and other interested parties on sign language hand gesture recognition algorithms.

title: Comparative Analysis of Hand Gesture Recognition Techniques: A Review
abstract: Computer vision technologies are rapidly evolving the way humans interact with computing devices and adapting to the constantly increasing demands of new age computer systems. Hand gesture recognition systems are becoming important in user interfaces as they are not only incredibly useful in sign language communication but also other applications such as automotive systems and air gesture classifiers in new age smartphones. There have been various attempts at solving this challenge; however, various factors such as changing lighting and environmental conditions along with other factors add to the complexity of the problem. In this paper, we present a comparative study of hand gesture recognition classification systems with a Creative Senz3D Camera. We implement and compare various algorithms like convolutional neural network, k nearest neighbors, support vector machines and logistic regression on our dataset.

title: A Review on Hand Gesture Recognition Using Machine Learning Techniques
abstract: In the earlier times, a gesture was the first mediumthat had been used for communication. By theevolution of time, verbal communication has beenwidely used by humans but non verbalcommunication is equally important not only forphysically handicapped people but also in variousapplications such as sign language recognition,gaming interface, etc. This is the part of HumanComputer Interaction (HCI) which provides interfacefor communication without use of any physical device.The paper reviews the comparative study between themethods that have been used for hand gesturerecognition. The primary steps to perform handgesture recognition are image acquisition, handdetection, feature extraction and gesture recognitionin the form of speech. The basic goal is to providenatural interaction where the image is given as aninput to the camera which after pre processing isbeing compared and recognized using classificationalgorithm.

title: Human Activity and Gesture Recognition: A Review
abstract: Human activity recognition is the method of extracting and predicting the movements of the human body often indoors by using any hardware device such as a camera or sensor based device. At an earlier stage collecting data from sensors is quite expensive but recently we have smartphones and other personal devices which have accelerometer based sensors that track our activities. HAR is a classification method in which people have a great interest because we can find different actions of the human body like sitting, walking, running, jumping, jogging etc. by using body worn sensors like accelerometer, gyroscope and applying methods like convolution neural network and other deep learning methods. The main objective of this review is to study different human activities, compounds and methods that are used to recognize actions and position of the body.