Hand gesture recognition and voice conversion for deaf and Dumb

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

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.

Gesture recognition has multiple applications in medical and engineering fields. The problem of hand gesture recognition consists of identifying, at any moment, a given gesture performed by the hand. In this work, we propose a new model for hand gesture recognition in real time. The input of this model is the surface electromyography measured by the commercial sensor the Myo armband placed on the forearm. The output is the label of the gesture executed by the user at any time. The proposed model is based on the Λ-nearest neighbor and dynamic time warping algorithms. This model can learn to recognize any gesture of the hand. To evaluate the performance of our model, we measured and compared its accuracy at recognizing 5 classes of gestures to the accuracy of the proprietary system of the Myo armband. As a result of this evaluation, we determined that our model performs better (86% accurate) than the Myo system (83%).

A hidden Markov model (HMM) has been widely used as a cost-effective solution for time series based applications such as hand gesture recognition. A large number of parameters in an HMM often result in a degradation of classification accuracy due to overfitting. Furthermore, the accuracy is often affected by the initialization values of the parameters. Left-right banded (LRB) HMM is one of the representative models which improve the classification accuracy by reducing the number of parameters. However, LRB HMM still suffers from false negative misclassification when it is applied for hand gesture recognition. Misclassification in hand gesture recognition arise from similar trajectories of multiple gestures that are represented by hidden states. This paper proposes a new initialization process that reduces not only state transition parameters but also hidden state observation parameters. To this end, the gesture trajectory is predicted and then the parameters are masked out when their probabilities are negligibly small in the predicted trajectory. Experiment results show that the confusion rate is nearly 14% lower than the LRB HMM while maintaining the classification accuracy.

Human hand recognition plays an important role in a wide range of applications ranging from sign language translators, gesture recognition, augmented reality, surveillance and medical image processing to various Human Computer Interaction (HCI) domains. Human hand is a complex articulated object consisting of many connected parts and joints. Therefore, for applications that involve HCI one can find many challenges to establish a system with high detection and recognition accuracy for hand posture and/or gesture. Hand posture is defined as a static hand configuration without any movement involved. Meanwhile, hand gesture is a sequence of hand postures connected by continuous motions. During the past decades, many approaches have been presented for hand posture and/or gesture recognition. In this paper, we provide a survey on approaches which are based on Hidden Markov Models (HMM) for hand posture and gesture recognition for HCI applications.

Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

In this paper, we propose a hand gesture-based interface for navigating 3D virtual space using Leap Motion and machine learning. Using the interface, the user can control an avatar in a first-person perspective by making it move in 4 directions, rotate its viewpoint, grasp/release an object, and adjust moving/rotating speed. After defining 9 types of hand gesture, we used the leap motion device to capture 150 hand skeletal datasets for each type, and converted them into feature datasets, with which we developed a heuristic algorithm and a machine learning (multilayer perceptron) model for hand gesture recognition. Experimental results showed that the machine learning model was much more accurate than the heuristic algorithm. We also implemented 3 methods for 3D virtual space navigation: the use of keyboard and mouse (KB) and the use of the leap motion device with the heuristic algorithm (HA) and the machine learning model (ML). After conducting a user study in which each of 15 subjects was guided to find a target object and move it to a start position with the methods, we found from the user study that the method ML outperformed the others in task time and in most of qualitative aspects.

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.

The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems. However, real-time recognition of dynamic micro hand gestures from video streams is challenging for in-vehicle scenarios since (i) the gestures should be performed naturally without distracting the driver, (ii) micro hand gestures occur within very short time intervals at spatially constrained areas, (iii) the performed gesture should be recognized only once, and (iv) the entire architecture should be designed lightweight as it will be deployed to an embedded system. In this work, we propose an HCI system for dynamic recognition of driver micro hand gestures, which can have a crucial impact in automotive sector especially for safety related issues. For this purpose, we initially collected a dataset named Driver Micro Hand Gestures (DriverMHG), which consists of RGB, depth and infrared modalities. The challenges for dynamic recognition of micro hand gestures have been addressed by proposing a lightweight convolutional neural network (CNN) based architecture which operates online efficiently with a sliding window approach. For the CNN model, several 3-dimensional resource efficient networks are applied and their performances are analyzed. Online recognition of gestures has been performed with 3D-MobileNetV2, which provided the best offline accuracy among the applied networks with similar computational complexities. The final architecture is deployed on a driver simulator operating in real-time. We make DriverMHG dataset and our source code publicly available 1.1https://www.mmk.ei.tum.de/DriverMHG/

For hand gesture recognition (HGR) sensing based on frequency modulated continuous wave (FMCW) radar, it is challenging to cope with the ambiguity and variability of gesture motions performed by different peoples due to the private habits or position, which inevitably reduce the robustness of HGR sensing. To solve this problem, we propose a novel dual-flow fusion deformable residual network (DFDRN). More detailed gesture information, time frequency maps and spectrum map videos, are fed into DFDRN, which improves the generalization and motion modeling ability of HGR through the extensive feature space. To further improve the robustness of HGR sensing, the deformable block is exploited to replace the convolution. To focus on the important features of dual-flow fusion, the convolutional block attention module (CBAM) is embedded in the backbone of DFDRN. Experimental results show that the accuracy of DFDRN, which can reach 97.5%, is obviously better than the state-of-the-art HGR methods.

To naturally interact with virtual environment by hand gesture, this paper presents a robust RGB-D data based recognition method of static and dynamic hand gesture. Firstly, for static hand gesture recognition, starting from the hand gesture contour extraction, the palm center is identified by Distance Transform (DT) algorithm. The fingertips are localized by employing the K-Curvature-Convex Defects Detection algorithm (K-CCD). On the basis, the distances of the pixels on hand gesture contour to palm center and the angle between fingertips are considered as the auxiliary features to construct a multimodal feature vector, and then recognition algorithm is presented to robustly recognize the static hand gestures. Secondly, combining Euclidean distance between hand joints and shoulder center joint with the modulus ratios of skeleton features, this paper generates a unifying feature descriptor for each dynamic hand gesture and proposes an improved dynamic time warping (IDTW) algorithm to obtain recognition results of dynamic hand gestures. Finally, we conduct extensive experiments to test and verify the static and dynamic hand gesture recognition algorithm and realize a low-cost real-time application of natural interaction with virtual environment by hand gestures.

This research intends to understand whether users would adopt the interactive interface of hand gesture recognition for XRSPACE MANOVA in the virtual-reality environment. Different from the traditional joystick control and external sensors, XRSPACE MANOVA’s hand gesture recognition relies on cameras built into the head-mount display to detect users’ hand gestures and interact with the system to provide a more life-like immersive experience. To better understand if users would accept this hand gesture recognition, the current experiment compares users’ experiences with hand gesture recognition and joystick control for XRSPACE MANOVA while controlling for the effects of gender, college major, and the completion time. The results suggest that users of hand gesture recognition have better perceptions of enjoyment, satisfaction, and confirmation, which means that they have a relatively fun and satisfying experience and that their expectations of the system/technology confirm their actual usage. Based on the parametric statistical analyses, user assessments show that perceived usefulness, perceived ease-of-use, attitude, and perception of internal control suggest that, in terms of operating performance, users are more accepting of the traditional joystick control. When considering the length of usage time, this study finds that, when hand gesture recognition is used for a relatively longer time, users’ subjective evaluations of internal control and behavioral intention to use are reduced. This study has, therefore, identified potential issues with hand gesture recognition for XRSPACE MANOVA and discussed how to improve this interactive interface. It is hoped that users of hand gesture recognition will obtain the same level of operating experience as if they were using the traditional joystick control.

In this project we've discussed a system which uses some dynamic hand gesture recognition technique to manage the media players like VLC media player. This project consist of certain modules which segments the foreground a part of the frame using detection of skin colour and approximate median technique. the popularity of hand gesture is finished by creating a call Tree, that uses certain features extracted from the segmented part. This hand gesture recognition technique introduces a replacement, natural thanks to interact with computers and is beneficial to several folks in our day-to-day life. Hand gestures are associated with the human hands in hand gesture recognition. This project is employed for controlling certain operations (Pause, Play, Volume up, Volume Down, Mute) on video player by mere hand gestures without getting in the rigmarole of pressing buttons or tapping onto the screen. this may be directly associated with our day to life like in presentations. We have considered hand gestures and their directional motion defines a gesture for the applying. during this application image retrieving is finished employing a Webcam. Some functions in VLC media players are used most often and these functions uses some predefined gestures. Shows the defined gestures in keeping with the VLC player control operation. We created a VLC Media Player Controller using Hand Gesture Recognition System to form ‘HUMAN LIFE EASY AND BETTER’. This project is implemented using two steps: (1.) Creation of Hand Gesture Recognition System: this is often done using image processing using OpenCV library. (2.) Controlling VLC Media Player using hand gestures: during this step we controlled the player using shell commands which were recalled using python commands through OS library

In this paper we have implemented a wireless vision based mobile robot control through hand gesture recognition based on perceptual color space such as HIS, HSV/HSB, HSL. Vision-based hand gesture recognition is an important problem in the field of human-computer interaction, since hand motions and gestures could potentially be used to interact with computers in more natural ways. The robot control was done purely based on the orientation histograms a simple and fast algorithm on the system which would recognize static hand gestures with HSV color spaces as major parameters. The wireless based mobile robot system using hand gestures is a new innovative user interface that resolves the complications of using numerous remote controls for various applications. Based on one unified set of hand gestures, this system interprets the user hand gestures into pre-defined commands to control the remote robot. The experimental results are very encouraging as the system produces real-time responses and highly accurate recognition towards various gestures under different lighting conditions.

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.