Gesture Recognition Technology Research Articles

Gesture Recognition Control System Based on Raspberry PI

With the development of science and technology, people have been searching for a new way of man-machine interaction to break through the constraints of mouse and keyboard. Gesture recognition technology has been widely studied because of its natural and convenient interpersonal communication. As early as 1993, Thomas proposed to recognize gestures with the help of data gloves or special colored auxiliary marks pasted on the hands. Thus, people began to explore the field of gesture recognition. Through more than ten years of development, especially the rapid development of computer vision, people have gradually stepped forward from pasting special color auxiliary marks to naked hand recognition technology. Based on this, the author combined gesture control board with image recognition method to carry out two-factor authentication of gesture recognition. The proposed gesture recognition system has an accuracy rate of 90% and can be constructed by using raspberry PI as the control center.

P‐15.4: Virtual reality gesture recognition based on depth information

With the rapid development of computer technology, the information revolution is constantly affecting and changing our lives. Today's virtual reality technology is no longer just a virtual model for viewing. Experiencers are more willing to personally interact with the virtual environment in a virtual scene to increase their sense of gaming experience.Today's virtual reality interaction methods mainly interact through the handles that come with VR helmets. Previously, some scholars have researched gesture interaction in virtual environments. They use data gloves to obtain information about hand movements. This method makes users have to wear heavy data gloves, which limits the natural movement of their hands. To overcome this shortcoming, the need for low‐cost vision‐based virtual reality gesture interaction is becoming more and more urgent.Traditional gesture recognition technologies based on artificial modeling include template‐based matching, data‐based gloves, and machine learning. As machine learning gesture classifiers, Hidden Markov Model (HMM) and Support Vector Machine (SVM) [1] have been widely used. However, in the hand motion detection of ordinary cameras, the image is prone to speckles, lighting changes, and other factors, which seriously affects the performance of the classifier, making it difficult to accurately and efficiently detect the first image when the background is complex and the lighting is affected. In recent years, deep learning‐based object detection methods have achieved great breakthroughs [2]. In the past decade, many deep learning‐based dynamic gesture recognition algorithms have been introduced. In the case of changing lighting conditions, in order to improve the classification accuracy of gesture recognition [3]. Therefore, this article chooses a depth camera combined with HTC VIVE device for gesture recognition in virtual reality. First obtain the depth image of the hand through the depth camera, then label the depth image, and then learn through the deep learning network hand movement training [4], preprocess the detected human hand image, and recognize it by hand detection The position of the hand in the image is obtained, and the movement of the hand is recognized as a result of recognition.The algorithm in this paper uses the advanced deep learning network YOLOv3, and changes the network structure and multiscale structure based on the original network. This algorithm is compared with other deep learning algorithms for gesture recognition, and the recognition effect is better than other algorithms in the same category.

Multi-Features Capacitive Hand Gesture Recognition Sensor: A Machine Learning Approach

Gesture recognition technology enables machines to understand human gestures. The technology is considered as a key enabler for gaming and virtual reality applications. In this paper, we propose an effective, low-cost capacitive sensor device to recognize hand gestures. In particular, we designed a prototype of a wearable capacitive sensor unit to capture the capacitance values from the electrodes placed on finger phalanges. The sensor captures finger capacitance values. Each gesture has specific finger capacitance values. We applied a running median filter to the output of the sensor and extracted 15 features for gesture classification training and testing tasks. Subsequently, various analyses were performed to provide more insights into the sensing data. We applied and compared two machine learning algorithms: Error Correction Output Code Support Vector Machines (ECOC-SVM) and ${K}$ -Nearest Neighbour (KNN) classifiers. The training and testing recognition rates were observed for both intra-participant and inter-participant data sets. Further, we introduced a feature compression approach derived from correlation analysis to reduce the complexity of the machine learning algorithms. Using cross validation, we achieved a classification rate of approximately 99% for intra-participant data. We achieved a lower recognition rate of 97% (average cross validation testing) for compressed feature data set using both machine learning approaches. For the inter-participant data, the recognition rate was 99% (normalized feature data) using KNN and 97% using ECOC-SVM. The research findings show that our recognition system is competitive and has an immense potential for further study.

Review and perspective on 3D Gesture Recognition Technologies for Automotive Industry

The paper reviews 3D Gesture Recognition technologies, that can be potentially used in future automotive applications. The purpose of the article is to find the best 3D gesture recognition technology for mass scale adaptation in future vehicles. Described gesture recognition technologies are divided into six categories: Vision Systems, Wearable sensors, Infrared, Electric field, Radar and Ultrasonic Waves. The article identifies principles of operation for each of chosen methods, compares advantages and disadvantages of the technologies.

Research Status of Gesture Recognition Based on Vision: A Review

Gesture control, as a new type of interactive method, has the characteristics of rich expression, convenient control, and fast. It has huge application prospects in entertainment, home furnishing, and industry. Gesture recognition is the basis of gesture control. Gesture recognition technology based on visual detection acquires gesture information in a non-contact manner, which enables the operator to have a better operating experience and is favored by scholars at home and abroad. In order to fully understand the existing research methods of visual gesture recognition, firstly, the basic process of visual gesture recognition is explained. According to the principle of the gesture recognition method, it is divided into gesture recognition based on traditional methods and gesture recognition based on deep learning. And the specific methods are analyzed and summarized in detail. Finally, the technical difficulties of visual gesture recognition are analyzed and discussed, and the development trend of gesture recognition based on vision is prospected.

Retracted] Research and Application of Multifeature Gesture Recognition in Human‐Computer Interaction Based on Virtual Reality Technology

Objective. To explore the research and application of multifeature gesture recognition in virtual reality human‐computer interaction and to explore the gesture recognition technology scheme to achieve better human‐computer interaction experience. Methods. Through the study of the technical difficulties of gesture recognition, comparative static gesture feature recognition and feature fusion algorithms are applied, in the process of research on gesture partition, and adjust the contrast of characteristic parameters, combined with the feature of space‐time dynamic gesture tracking trajectory and dynamic gesture recognition and gesture recognition effect under different scheme. Results. The central region was divided into 0 regions, and the central region was divided into 1‐4 regions in counterclockwise direction. Compared with the traditional gesture changes, the overlapping problem in the four partition modes was reduced, the gesture was better displayed, and the operation and use of gesture processing were realized more efficiently. Conclusion. Gesture recognition requires the combination of static gesture feature information recognition, gesture feature fusion, spatiotemporal trajectory feature, and dynamic gesture trajectory feature to achieve a better human‐computer interaction experience.

Approach for Improving User Interface Based on Gesture Recognition

Gesture recognition technology based on visual detection to acquire gestures information is obtained in a non-contact manner. There are two types of gesture recognition: independent and continuous gesture recognition. The former aims to classify videos or other types of gesture sequences that only contain one isolated gesture instance in each sequence (e.g., RGB-D or skeleton data). In this study, we review existing research methods of visual gesture recognition and will be grouped according to the following family: static, dynamic, based on the supports (Kinect, Leap…etc), works that focus on the application of gesture recognition on robots and works on dealing with gesture recognition at the browser level. Following that, we take a look at the most common JavaScript-based deep learning frameworks. Then we present the idea of defining a process for improving user interface control based on gesture recognition to streamline the implementation of this mechanism.

Retracted: Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images

With the rapid development of sensor technology and artificial intelligence, the video gesture recognition technology under the background of big data makes human–computer interaction more natural and flexible, bringing the richer interactive experience to teaching, on-board control, electronic games etc. To perform robust recognition under the conditions of illumination change, background clutter, rapid movement, and partial occlusion, an algorithm based on multi-level feature fusion of two-stream convolutional neural network is proposed, which includes three main steps. Firstly, the Kinect sensor obtains red–green–blue-depth (RGB-D) images to establish a gesture database. At the same time, data enhancement is performed on the training set and test set. Then, a model of multi-level feature fusion of a two-stream convolutional neural network is established and trained. Experiments show that the proposed network model can robustly track and recognise gestures under complex backgrounds (such as similar complexion, illumination changes, and occlusion), and compared with the single-channel model, the average detection accuracy is improved by 1.08%, and mean average precision is improved by 3.56%.

Tecnología de reconocimiento de gestos en actividades de aprendizaje lúdico que mejore el área motriz fina de los niños con síndrome de down.

El estudio evalúa a la tecnología de reconocimiento de gestos en actividades de aprendizaje lúdico en el desarrollo de la motricidad fina en los niños con síndrome de Down. Apoyado con el método analítico - sintético y el tipo de investigación descriptiva, se determinó las ventajas de la aplicación desarrollada con esta tecnología, logrando una percepción de mejora en la habilidad motora fina de los niños con Down y con prácticas incluso fuera de su institución educativa.

Comparison of Machine Learning Models: Gesture Recognition Using a Multimodal Wrist Orthosis for Tetraplegics

Many tetraplegics must wear wrist braces to support paralyzed wrists and hands. However, current wrist orthoses have limited functionality to assist a person’s ability to perform typical activities of daily living other than a small pocket to hold utensils. To enhance the functionality of wrist orthoses, gesture recognition technology can be applied to control mechatronic tools attached to a novel fabricated wrist brace. Gesture recognition is a growing technology for providing touchless human-computer interaction that can be particularly useful for tetraplegics with limited upper-extremity mobility. In this study, three gesture recognition models were compared—two dynamic time-warping models and a hidden Markov model—in terms of their classification accuracy of gestures from a gesture lexicon known to be accessible to tetraplegics. Gesture data from participants with and without spinal cord injuries was collected using a prototype wrist orthosis. Leave-one-subject-out cross-validation was used to develop a user-independent gesture recognition library. The trained models were then tested using a combination of data from both populations and data separated by population. The classification accuracy and classification time were computed and compared to determine the optimal gesture recognition model.

Manipulation of Web Using Gestures

Gesture recognition is a type of perceptual computing user interface that allows computers to capture and interpret human gestures as commands. Gestures are generally the movement of the hands which is a form of non-verbal communication. We use gestures as input to control devices or applications or websites. By using the gestures, we directly open or manipulate the website for the users. When the user uses the gesture, the system captures the gesture and compares it with the stored gesture data. If the gesture matches the data, then the required website will be manipulated. These gestures are widely helpful for the people who are blind and unable to type. The main objective of the project is to deliver user friendly access to the web contents. The vision based technology of hand gesture recognition is an important part of human-computer interaction (HCI), thus stepping into the path of future and our project is one such kind of stepping stone. Our project really concerns the people who are not able to search contents on the web through text search. We provide an improvisable solution where it paves a way for research and development.

A brief analysis of gesture recognition in VR

With the rapid development of artificial intelligence technology, human‐computer interaction, as the core of virtual reality system, has become an increasingly important part of modern science and technology products. At present, human‐computer interaction is mainly based on contact, which must be completed through direct contact between hand and receiving equipment, rather than through indirect contact between hand and equipment. Direct interaction can not give full play to the advantages of flexible human hand, so gesture recognition becomes extremely important in interaction.The development of gesture recognition technology based on vision is a process from 2D to 3D. Early gesture recognition is based on the recognition technology of two‐dimensional color image, which means to get two‐dimensional static image after taking the scene with ordinary camera, and then recognize the content of the image through the computer graphic algorithm. With the development of camera and sensor technology, the depth information of gesture can be captured, and 3D gesture recognition technology can recognize various hand shapes, gestures and actions.Virtual reality is a kind of simulated virtual environment generated by computer according to reality and imagination space, which makes users have immersive feeling in the real world. Through various interactive behaviors, it stimulates users’ perception systems such as vision, touch and hearing, so as to establish a comprehensive sensory experience. Nowadays, virtual reality interaction is more based on the handle of virtual reality device or operating gloves, but using these devices is too complex, so gesture interaction based on bare hands will have a broader prospect.The virtual reality interaction technology based on gesture recognition is to recognize human hand movements with cameras and other devices and interact with the virtual scene. Users can control the virtual scene and interact through simple gestures. Gesture recognition is now applied in many industries, but the interaction of gesture in virtual reality is not so popular. This paper will analyze the development status of gesture recognition in virtual reality technology, and prospect the development trend of virtual reality technology based on gesture recognition.

A Research Review on Indian Sign Language Recognition using Machine Learning

Sign language is the best communication media to interact with the deaf and dumb people. As sign language is completely different from our regular languages, someone has to learn that language and they are called as sign language interpreters. As per the survey of Indian Sign Language Research and Training Center (ISLRTC), there are around 466 million people worldwide with hearing loss and 34 million of these are youth. If we will think about India, only 250 interpreters are available from India, roughly 1 for 20284 deaf and dumb people. So, education of deaf and dumb people is very big question as interpreters, schools/colleges/any learning institutes available for them are very few and not available at every place. So, this problem can be solved by having online educational system for deaf and dumb people. Because it is one of the important purposes of this system that system should be accessed by deaf and dumb people from anywhere or at any place. Already online systems are available for deaf and dumb people. But problem in existing systems is that, more focus is given on just character recognition, gesture recognition etc. But I would like to contribute more additional work regarding educational courses (ex. like NPTEL, Coursera, Udemy) for deaf and dumb people instead of just gesture recognition in existing systems. So, more focus is given on Dynamic gesture recognition and Web-based technology for the implantation point of view. As well as vision-based method is chosen for implementation as it is cheaper and more comfortable for user than sensor-based method. As per the Persons with Disability Act 1995, one percent jobs in government and private sectors are reserved for them. But those jobs have conditions like people with disability should have degree certificates from known universities across India. With the help of this application, they can learn as well as can give exams using mobile or laptop camera. If they will clear the exams successfully then certificates will be provided to them. On the basis of those certificates, they can get a job as they will get certified education. This system itself can test the deaf and dumb users in exams as it is using Machine Learning instead of any external help of sign language interpreters. Various machine learning algorithms like Support Vector Machine (SVM) and Artificial Neural Network (ANN), Hidden Markov Model (HMM), Convolutional Neural Network (CNN), Finite State Machine (FSM) are used in existing systems.

Virtual reality gesture recognition based on depth information

With the rapid development of computer technology, the information revolution is constantly affecting and changing our lives. Today’s virtual reality technology is no longer just a virtual model for viewing. Experiencers are more willing to personally interact with the virtual environment in a virtual scene to increase their sense of gaming experience.Today’s virtual reality interaction methods mainly interact through the handles that come with VR helmets. Previously, some scholars have researched gesture interaction in virtual environments. They use data gloves to obtain information about hand movements. This method makes users have to wear heavy data gloves, which limits the natural movement of their hands. To overcome this shortcoming, the need for low‐cost vision‐based virtual reality gesture interaction is becoming more and more urgent.Traditional gesture recognition technologies based on artificial modeling include template‐based matching, data‐based gloves, and machine learning. As machine learning gesture classifiers, Hidden Markov Model (HMM) and Support Vector Machine (SVM) [1] have been widely used. However, in the hand motion detection of ordinary cameras, the image is prone to speckles, lighting changes, and other factors, which seriously affects the performance of the classifier, making it difficult to accurately and efficiently detect the first image when the background is complex and the lighting is affected. In recent years, deep learning‐based object detection methods have achieved great breakthroughs [2]. In the past decade, many deep learning‐based dynamic gesture recognition algorithms have been introduced. In the case of changing lighting conditions, in order to improve the classification accuracy of gesture recognition [3]. Therefore, this article chooses a depth camera combined with HTC VIVE device for gesture recognition in virtual reality. First obtain the depth image of the hand through the depth camera, then label the depth image, and then learn through the deep learning network hand movement training [4], preprocess the detected human hand image, and recognize it by hand detection The position of the hand in the image is obtained, and the movement of the hand is recognized as a result of recognition.The algorithm in this paper uses the advanced deep learning network YOLOv3, and changes the network structure and multi‐scale structure based on the original network. This algorithm is compared with other deep learning algorithms for gesture recognition, and the recognition effect is better than other algorithms in the same category.

HVAC and Audio Control System using Gesture Recognition in Cars

Gesture recognition technology entails a wide variety of touch-free interaction capabilities which controls notably contribute to easing our interaction with devices, reducing the need for a keys, or button. To recognize the different hand gestures for different control system in cars is done through image processing. A new method for the hand gestures is that, the hand part gets extracted from the background using background subtraction algorithm using raspberry pi, there is no need of buttons for using of some equipments in different vehicles by using an advanced technology. In gesture recognition technology we can control the audio and HVAC system automatically instead of searching for a particular button, which causes distraction while driving.

Hand gesture recognition and voice controlled robot

This work addresses the robot controlled by hand gesture and voice control. In the technology era, the space between the physical and the digital world is brought closer by the introduction of gesture concept. For all the dangerous tasks we prefer technology rather than people. Even though these robots are being controlled manually in the early stages, these can now be controlled via voice and gestures. This technology of gesture and voice recognition can be defined by the interaction between the computer and the body language of human beings. This constructs the communication link between technology and mankind. The target of this work is to upgrade the complete security to the robot and to simplify the controlling mechanism.

Enhanced Multi-Channel Feature Synthesis for Hand Gesture Recognition Based on CNN With a Channel and Spatial Attention Mechanism

Millimeter-wave (MMW) radar hand gesture recognition technology is becoming important in many electronic device control applications. Currently, most existing approaches utilize the radical and micro-Doppler features from single-channel MMW radar, which ignores the different importance of the information contained in the micro-Doppler feature background or target areas. In this paper, we propose an algorithm for hand gesture recognition jointly using multi-channel signatures. The algorithm blends the information of both micro-Doppler features and instantaneous angles (azimuth and elevation) to accomplish hand gesture recognition performed with the convolutional neural network (CNN). To have a better features fusion and make CNN focus on the most important target signal regions and suppress the unnecessary noise areas, we apply the channel and spatial attention-based feature refinement modules. We also employ gesture movement mechanism-based data augmentation for more effective training to alleviate potential overfitting. Extensive experiments demonstrate the effectiveness and superiorities of the proposed algorithm. This method achieves a correct classification rate of 96.61%, approximately 5% higher than that of the single-channel-based recognition strategy as measured based on MMW radar datasets.

A Novel Communication System For Deaf And Dumb People using gesture

Human Beings know each other and contact with themselves through thoughts and ideas.The best way to present our idea is through speech. Some people don’t have the power of speech; the only way they communicate with others is through sign language. Now a days technology has reduced the gap through systems which can be used to change the sign language used by these people to speech. Sign language recognition (SLR) and gesture-based control are two major applications used for hand gesture recognition technologies. On the other side the controller converts the sign language in to the text and speech which gets converted with the help of text to speech conversion and analog to digital conversion. A Dumb person throughout the world uses sign language for the communication.The best way to present our idea is through speech. Some people don’t have the power of speech; the only way they communicate with others is through sign language. Now a days technology has reduced the gap through systems which can be used to change the sign language used by these people to speech. Sign language recognition (SLR) and gesture-based control are two major applications used for hand gesture recognition technologies. On the other side the controller converts the sign language in to the text and speech which gets converted with the help of text to speech conversion and analog to digital conversion. A Dumb person throughout the world uses sign language for the communication.

Hand Gesture Recognition Using 3-D Depth Perception

The paper provides information on hand gesture recognition through its various methods but primarily focused on 3D depth-perceiving sensor technology for hand tracking and gesture recognition. Hand gesture recognition provides new opportunities for human-computer interactions (HCI) and over the past decade has emerged in various fields and technologies. The paper lures an likeness amongst the present technologies in the market and the workings behind their operations. It differentiates between the old and newer mechanics employed in the products. Furthermore, the paper discusses the existing uses of gesture recognition with their limitations. It provides proposed ideas in various fields, their use and future developmental paths.

Exploration of Feature Extraction Methods and Dimension for sEMG Signal Classification

It is necessary to complete the two parts of gesture recognition and wireless remote control to realize the gesture control of the automatic pruning machine. To realize gesture recognition, in this paper, we have carried out the research of gesture recognition technology based on surface electromyography signal, and discussed the influence of different numbers and different gesture combinations on the optimal size. We have calculated the 630-dimensional eigenvector from the benchmark scientific database of sEMG signals and extracted the features using principal component analysis (PCA). Discriminant analysis (DA) has been used to compare the processing effects of each feature extraction method. The experimental results have shown that the recognition rate of four gestures can reach 100.0%, the recognition rate of six gestures can reach 98.29%, and the optimal size is 516~523 dimensions. This study lays a foundation for the follow-up work of the pruning machine gesture control, and p rovides a compelling new way to promote the creative and human computer interaction process of forestry machinery.