Hand Gesture Recognition Using Deep Learning

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 (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

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.

Any human–computer interaction application needs to be able to recognize gestures. Hand gesture detection systems that recognize gestures in real time can improve human–computer interaction by making it more intuitive and natural. Colour gloves and skin colour detection are two prominent hand segmentation and detection technologies, but each has its own set of benefits and drawbacks. For physically challenged people, gesture identification is a crucial technique of sharing information. Support vector machine algorithm with principal component analysis, hidden Markov model, superposed network with multiple restricted Boltzmann machines, growing neural gas algorithm, convolutional neural network, double channel convolutional neural network, artificial neural network, and linear support vector machine with gesture dataset matches the interaction of gestures for various postures in real time. Although this method can recognize a huge number of gestures, it does have certain downsides, such as missing movements due to the accuracy of the categorization algorithms. Furthermore, matching the vast dataset takes a longer time. The main contribution of this work lies in a conceptual framework based on the findings of a systematic literature review that provides fruitful implications based on recent research findings and insights that can be used to direct and initiate future research initiatives in the field of hand gesture recognition and artificial intelligence. As a result, a novel study based on a hybrid recurrent neural network (RNN) with chaos game optimization may reduce classification mistakes, increase stability, maximize resilience, and efficiently use the hand gestures recognition system.

Hand gesture recognition is a challenging task due to the complexity of hand movements and to the variety among the same gesture performed by distinct subjects. Recent technologies, such as Kinect sensor, provide new opportunities, allowing to capture both RGB and depth (RGB-D) images, which offer high discriminant information for efficient hand gesture recognition. In the aspect of feature extraction, the traditional methods process the RGB and depth information independently. In this paper, we propose a robust hand gesture recognition system based on a new feature extraction method, fusing RGB images and depth information simultaneously, by using the quaternion algebra that provide a more robust and holistical representation. In fact, we introduce, for the first time, a novel type of feature extraction method, named quaternion Tchebichef moment invariants. The novelty of the proposed method in this paper lies in the direct derivation of invariants from their orthogonal moments, based on the algebraic properties of the discrete Tchebichef polynomials. The proposed approach based on quaternion algebra is suggested to process the four components holistically, for a robust and efficient hand gesture recognition system. The obtained experimental and theoretical results demonstrate that the present approach is very effective for addressing the problem of hand gesture recognition and have proved its robustness against geometrical distortion, noisy conditions and complex background compared to the state of the art, indicating that it could be highly useful for many computer vision applications.

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

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.

This paper presents a lightweight convolutional neural network (CNN) model for hand gesture recognition and implements it on a nano drone for flight control. Specifically, the nano drone captures images and determines its flight direction based on the hand gesture recognition results of the captured images. Due to limited hardware resources, the nano drone has only a 64KB L1 cache, a 512KB L2 cache, and a RISC-V CPU, which makes CNN deployment challenging. To address this issue, we design and optimize a 10-layer simplified CNN model and quantize the computations to INT8. Experimental results show that the proposed model achieves 90% accuracy in 5-class hand gesture recognition while maintaining a compact size of only 72.5 KB. Furthermore, it operates at a recognition speed of 7 FPS, enabling real-time performance.

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.

Automatic hand gesture recognition is the most important part of sign language translation. Its importance increases with the growth of deaf and hard of hearing population and cognitive computing. In this article, we propose an efficient system for automatic hand gesture recognition based on deep learning. The proposed system is based on a convolutional neural network (CNN). It employs a transfer learning of 3D CNN for hand gesture recognition. Three different datasets are used to evaluate the proposed system in signer dependent and signer independent modes.

At the present time, hand gestures recognition system could be used as a more\nexpected and useable approach for human computer interaction. Automatic hand\ngesture recognition system provides us a new tactic for interactive with the\nvirtual environment. In this paper, a face and hand gesture recognition system\nwhich is able to control computer media player is offered. Hand gesture and\nhuman face are the key element to interact with the smart system. We used the\nface recognition scheme for viewer verification and the hand gesture\nrecognition in mechanism of computer media player, for instance, volume\ndown/up, next music and etc. In the proposed technique, first, the hand gesture\nand face location is extracted from the main image by combination of skin and\ncascade detector and then is sent to recognition stage. In recognition stage,\nfirst, the threshold condition is inspected then the extracted face and gesture\nwill be recognized. In the result stage, the proposed technique is applied on\nthe video dataset and the high precision ratio acquired. Additional the\nrecommended hand gesture recognition method is applied on static American Sign\nLanguage (ASL) database and the correctness rate achieved nearby 99.40%. also\nthe planned method could be used in gesture based computer games and virtual\nreality.\n

Gesture recognition has been studied for a while within the fields of computer vision and pattern recognition. A gesture can be defined as a meaningful physical movement of the fingers, hands, arms, or other parts of the body with the purpose to convey information for the environment interaction. For instance, hand gesture recognition (HGR) can be used to recognize sign language which is the primary means of communication by the deaf and mute. Vision-based HGR is critical in its application; however, there are challenges that will need to be overcome such as variations in the background, illuminations, hand orientation and size and similarities among gestures. The traditional machine learning approach has been widely used in vision-based HGR in recent years but the complexity of its processing has been a major challenge—especially on the handcrafted feature extraction. The effectiveness of the handcrafted feature extraction technique was not proven across various datasets in comparison to deep learning techniques. Therefore, a hybrid network architecture dubbed as Lightweight VGG16 and Random Forest (Lightweight VGG16-RF) is proposed for vision-based hand gesture recognition. The proposed model adopts feature extraction techniques via the convolutional neural network (CNN) while using the machine learning method to perform classification. Experiments were carried out on publicly available datasets such as American Sign Language (ASL), ASL Digits and NUS Hand Posture dataset. The experimental results demonstrate that the proposed model, a combination of lightweight VGG16 and random forest, outperforms other methods.

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.

In recent years, hand gesture recognition (HGR) systems have been extensively developed with the technologies of human-computer interaction (HCI), which enables regular interaction with machines. However, the significance of progress in HGR continues to advance, although the problem of hand segmentation and recognition is challenging due to the unfavorable environment, background illumination, hand size, and shape. To overcome this, we propose an optimal segmentation method for identifying hand gestures from input images, improving recognition performance. For segmenting hand gestures, we compared the segmentation methods of YCbCr, SkinMask, and HSV (hue, saturation, and value). The CR component is extracted from YCbCr, then binarization, erosion, and hole filling are performed. Color segmentation is applied to the SkinMask process that detects pixels that match the color of the hand. In the HSV process, threshold masking determines the dominant features. The Softmax classification is used to classify hand gestures where features are extracted through convolutional neural network (CNN). The proposed segmentation methods are applied to a benchmark dataset and the result shows an improvement in recognition accuracy over state-of-the-art systems.