Multiview Hand Gesture Recognition From Generated Gestures Using Conditional Adversarial Network

Hand gesture recognition (HGR) systems using electromyography (EMG) bracelet-type sensors are currently largely used over other HGR technologies. However, bracelets are susceptible to electrode rotation, causing a decrease in HGR performance. In this work, HGR systems with an algorithm for orientation correction are proposed. The proposed orientation correction method is based on the computation of the maximum energy channel using a synchronization gesture. Then, the channels of the EMG are rearranged in a new sequence which starts with the maximum energy channel. This new sequence of channels is used for both training and testing. After the EMG channels are rearranged, this signal passes through the following stages: pre-processing, feature extraction, classification, and post-processing. We implemented user-specific and user-general HGR models based on a common architecture which is robust to rotations of the EMG bracelet. Four experiments were performed, taking into account two different metrics which are the classification and recognition accuracy for both models implemented in this work, where each model was evaluated with and without rotation of the bracelet. The classification accuracy measures how well a model predicted which gesture is contained somewhere in a given EMG, whereas recognition accuracy measures how well a model predicted when it occurred, how long it lasted, and which gesture is contained in a given EMG. The results of the experiments (without and with orientation correction) executed show an increase in performance from 44.5% to 81.2% for classification and from 43.3% to 81.3% for recognition in user-general models, while in user-specific models, the results show an increase in performance from 39.8% to 94.9% for classification and from 38.8% to 94.2% for recognition. The results obtained in this work evidence that the proposed method for orientation correction makes the performance of an HGR robust to rotations of the EMG bracelet.

MuViH: Multi-View Hand gesture dataset and recognition pipeline for human–robot interaction in a collaborative robotic finishing platform

In today’s ever-evolving healthcare landscape, our objective is to bring technology closer to human experience. In this paper, a vision-based hand gesture recognition (HGR) system is introduced as a human computer interface (HCI) that transforms natural movements into intuitive medium for device and application manipulation. This innovative approach is designed to empower the elderly and enhance patient monitoring by offering a seamless, nonintrusive interface. By bridging compassion with cutting-edge technology, this approach aims to redefine care and create a more connected, responsive healthcare environment. The study begins by reviewing both traditional and contemporary HGR methodologies, with a focus on vision-based systems, which have shown greater potential for practical applications compared to conventional sensor-based systems. Traditional approaches, such as contour analysis, colour segmentation, and template matching, have demonstrated limitations. In contrast, deep learning approaches have gained mass adaptation in vision-based systems for their ability to learn hierarchical features and better handling complex, dynamic scenarios. However, existing models struggle in real-time performance due to computational inefficiencies and environmental variations. This study introduces a deep learning-based detection framework which enables the practical application of HGR. For example, it supports the development of gesture-based virtual assistants for interacting with digital devices and allows elderly residents in care homes to give instructions to remote caregivers. The framework consists of two deep learning models, one for region of interest (ROI) detection and another for classification. The recognition process begins with capturing RGB images, chosen for versatility, affordability, and compatibility, which then passes through YOLOv8 (You Only Look Once version 8) for detection. The YOLOv8 model is trained on a subset, only 4.5%, of the HaGRID dataset, comprises over 550,000 RGB images, to accurately locate hand regions. Once detection is complete, several image processing techniques are applied to overcome typical HGR constraints. These include ROI extraction and gray scaling for faster computation, resizing for model optimization and distance viability, HSV conversion for lighting independence, and histogram equalization for enhanced feature extraction. The processed image is then fed into a convolutional neural network (CNN) for gesture classification. The network is designed with several convolutional layers, followed by dense layers with ReLU activation to introduce non-linearity, and SoftMax activation for classification. To improve the model's robustness, data augmentation techniques such as random flipping and rotation are applied. The HGR system demonstrates a staggering accuracy of 97% for training & validation. The proposed framework effectively addresses the limitations of conventional HGR systems—many of which have been developed and tested in controlled environments—demonstrating its applicability in real-life situations.

Hand gesture recognition is a quickly developing field with many uses in human-computer interaction, sign language recognition, virtual reality, gaming, and robotics. This paper reviews different ways to model hands, such as vision-based, sensor-based, and data glove-based techniques. It emphasizes the importance of accurate hand modeling and feature extraction for capturing and analyzing gestures. Key features like motion, depth, color, shape, and pixel values and their relevance in gesture recognition are discussed. Challenges faced in hand gesture recognition include lighting variations, complex backgrounds, noise, and real-time performance. Machine learning algorithms are used to classify and recognize gestures based on extracted features. The paper emphasizes the need for further research and advancements to improve hand gesture recognition systems' robustness, accuracy, and usability. This review offers valuable insights into the current state of hand gesture recognition, its applications, and its potential to revolutionize human-computer interaction and enable natural and intuitive interactions between humans and machines. In simpler terms, hand gesture recognition is a way for computers to understand what people are saying with their hands. It has many potential applications, such as allowing people to control computers without touching them or helping people with disabilities communicate. The paper reviews different ways to develop hand gesture recognition systems and discusses the challenges and opportunities in this area.

Hand gesture recognition is a relevant study topic for a reason that sometimes we may not be in position to communicate verbally. There is need to design Hand gesture recognition systems in order to help people adopt to nonverbal communication mainly sign language. However, there is no clue to understand the meaning of gesture through the computers directly. So this calls for definitions that generalize models in a computer. That is why the machine-learning approaches are implemented in recognition systems. There are generally two types of hand gestures recognition systems which researches have concentrated on. These include static and dynamic Hand gesture recognition systems. However, in building Hand gesture recognition systems, various machine learning approaches have been used. For implementing the proposed system, MS Kinect depth sensor was used as a hardware. The Kinect depth sensor is composed of an infrared camera. This is an advantage to the systems that are designed basing on the depth sensing because factors like color, clothing and background have less effect on the performance. So Kinect based depth sensor systems have a high accuracy and performance making them relevant and applicable in our daily lives. In this paper, we propose a static hand gesture recognition system in real time using two machine learning methods namely Support Vector Machine and Artificial Neural Networks. We use of the newly launched Microsoft Kinect sensor for image extraction. The sensor helps us to extract the hand images. We implement the system on a Matlab platform for reasons that Matlab is widely used by researchers in different fields and that can handle complex computations. In the training of the model, we collect a hundred depth-based Histogram of Oriented Gradient features per alphabet from the hand gesture images which we trained, tested and validated using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). From this dataset, we can generate the generalized gesture model for each alphabet image. For the proposed system, the classification with ANN proves a higher performance then SVM.

Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations. This paper looked at research on vision-based hand gesture recognition systems from 2012 to 2022. Its goal is to find areas that are getting better and those that need more work. We used specific keywords to find 108 articles in well-known online databases. In this article, we put together a collection of the most notable research works related to gesture recognition. We suggest different categories for gesture recognition-related research with subcategories to create a valuable resource in this domain. We summarize and analyze the methodologies in tabular form. After comparing similar types of methodologies in the gesture recognition field, we have drawn conclusions based on our findings. Our research also looked at how well the vision-based system recognized hand gestures in terms of recognition accuracy. There is a wide variation in identification accuracy, from 68% to 97%, with the average being 86.6 percent. The limitations considered comprise multiple text and interpretations of gestures and complex non-rigid hand characteristics. In comparison to current research, this paper is unique in that it discusses all types of gesture recognition techniques.

Human-computer interaction introduces critical open door with the proceeds with improvement of wearable gadgets. Gesture recognition through smart devices is becoming a popular research direction. This paper proposes a hand gesture recognition and real-time game control system that is capable of continues human-computer interaction in view of an off-the-rack business wearable wristband. We utilize three-axis accelerator and gyroscope sensors embedded in smart band to collect hand motion information and use Kinect camera capture video information for manual segmentation during the training model phase. A continuous gesture segmentation algorithm based on sliding window and DTW algorithm is developed to detect meaningful gestures in the real-time game control stage. In addition, an android game named Fly Birds which is controlled by gesture recognition result is presented to simulate real-time human-computer interaction. Then, we classify the data in the window using common classifiers. Finally, our experimental results show that, we can accurately identify the designed gestures during the stage of static gesture recognition, and we also achieve a perfect interactive effect in the process of dynamic real-time game control. The experiment outcomes will advance the ascent of human-PC cooperation in view of hand gesture recognition and related applications will rise in vast numbers.

DeReFNet: Dual-stream Dense Residual Fusion Network for static hand gesture recognition

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 2012

Hand motion tracking and gesture recognition are a fundamental technology in the field of proactive computing for a better human computer interaction system. In this paper, we have developed a 3-D hand motion tracking and gesture recognition system via a data glove (namely the KHU-1 data glove consisting of three tri-axis accelerometer sensors, one controller, and one Bluetooth). The KHU-1 data glove is capable of transmitting hand motion signals to a PC through wireless communication via Bluetooth. Also we have implemented a 3-D digital hand model for hand motion tracking and recognition. The implemented 3-D digital hand model is based on the kinematic chain theory utilizing ellipsoids and joints. Finally, we have utilized a rule-based algorithm to recognize simple hand gestures namely scissor, rock, and paper using the 3-D digital hand model and the KHU-1 data glove. Some preliminary experimental results are presented in this paper.

Tracking moving objects is an area increasingly known in computer vision field. It plays a very important role in human-computer interaction. In this context we have developed a hand tracking and gesture recognition system that allows interaction with the machine in an intuitive and natural way. To ensure the tracking we apply the Kalman filter and detect the optimal points of the hand in order to determine the gesture expressed by user.

With the escalating role of computers in educational system, human computer interaction, is becoming gradually more important part of it. The general believe is that with the progress in computing speed, communication technologies, and display techniques the existing HCI techniques may become a constraint in the effectual utilization of the existing information flow. The development of user interfaces influences the changes in the Human-Computer Interaction (HCI). Human hand gestures have been a mode of non verbal interaction widely used. The vocabulary of hand gesture communication has many variations. It ranges from simple action of using our finger to point at and using hands to move objects around for more complex expressions for the feelings and communicating with others. Also the hand gestures play a prominent role in teaching considering the explanations and exemplifications being highly dependent on hand gestures. Naturalistic and intuitiveness of the hand gesture has been an immense motivating aspect for the researchers in the field of Human Computer Interaction to put their efforts to research and develop the more promising means of interaction involving human and computers. The pursuance for the Human Computer Interaction research is moved by the central dogma of removing the complex and cumbersome interaction devices and replacing them with more obvious and expressive means of interaction which easily comes to the users with least cognitive burden like hand gestures. This paper designs a simple, natural system for gestural interaction between the user and computer for providing a dynamic user interface. The gesture recognition system uses image processing techniques for detection, segmentation, tracking and recognition of hand gestures for converting it to a meaningful command. This hand gesture recognition system has been proposed, designed and developed with the intensions to make it a substitute for mouse while making dynamic user interface between human and machine. Hence instead of making effort to develop a new vocabulary of hand gesture we have matched control instruction set of mouse to subset of most discriminating hand gestures, so that we get a robust interface. The interface being proposed here can be substantially applied towards different applications like image browser, games etc.

Gesture recognition plays a vital role in multiple applications; the applications may be related to simple aspects such as playing video games to complex tasks such as related to communication using sign symbols. In this paper, we propose a robust hand gesture recognition system using skin detection technique. Gesture recognition plays a key role for different applications. In this regard, we attempt to take out the type features of the fingers, for instance their orientation, their status in terms of raised or folded fingers and their respective image position, which require the processing of images as a matter of principle. In order to recognize the terms used for contact the program uses a complex approach to distinguish different hand movements. The dynamic sequence of the images that function as a contribution to the hand gesture recognition system is captures using the camera. GoogLeNet is used for hand gesture recognition training and detection. In order to train GoogLeNet, we use transfer learning. We also used American Sign Language (ASL) to map the sign to a particular alphabet.

Hand gesture recognition is used in human–computer interaction, with multiple applications in assistive technologies, virtual reality, and smart systems. While vision-based methods are commonly employed, they are often computationally intensive, sensitive to environmental conditions, and raise privacy concerns. This work proposes a hardware/software co-optimized system for real-time hand gesture recognition using accelerometer data, designed for a portable, low-cost platform. A Convolutional Neural Network from TinyML is implemented on a Xilinx Zynq-7000 SoC-FPGA, utilizing fixed-point arithmetic to minimize computational complexity while maintaining classification accuracy. Additionally, combined architectural optimizations, including pipelining and loop unrolling, are applied to enhance processing efficiency. The final system achieves a 62× speedup over an unoptimized floating-point implementation while reducing power consumption, making it suitable for embedded and battery-powered applications.

Recently much attention has been paid to the design of intelligent and natural user-computer interfaces. Hand Gesture Recognition systems has been developed continuously as its ability to interact with the machines. Now-a-days the news of metaverse ecosystem has increased the number of system in gesture recognition. Gestures are used to communicate with the PCs in a virtual environment. In this project Hand gestures are used to communicate information non-verbally which are free of expression to do a particular task. Here the hand gestures are recognized by using hand skeleton recognition using mediapipe library in python. In this project, the PC camera will record live video and recognizes the hand gestures based on which a particular functionality will take place. This project will present virtual keyboard, calculator and control system’s volume using hand gestures recognition technique by coding in Python using the OpenCV library.