Gesture Recognition Algorithm Research Articles

Hand Gesture Recognition on a Resource-Limited Interactive Wristband.

Most of the reported hand gesture recognition algorithms require high computational resources, i.e., fast MCU frequency and significant memory, which are highly inapplicable to the cost-effectiveness of consumer electronics products. This paper proposes a hand gesture recognition algorithm running on an interactive wristband, with computational resource requirements as low as Flash < 5 KB, RAM < 1 KB. Firstly, we calculated the three-axis linear acceleration by fusing accelerometer and gyroscope data with a complementary filter. Then, by recording the order of acceleration vectors crossing axes in the world coordinate frame, we defined a new feature code named axis-crossing code. Finally, we set templates for eight hand gestures to recognize new samples. We compared this algorithm’s performance with the widely used dynamic time warping (DTW) algorithm and recurrent neural network (BiLSTM and GRU). The results show that the accuracies of the proposed algorithm and RNNs are higher than DTW and that the time cost of the proposed algorithm is much less than those of DTW and RNNs. The average recognition accuracy is 99.8% on the collected dataset and 97.1% in the actual user-independent case. In general, the proposed algorithm is suitable and competitive in consumer electronics. This work has been volume-produced and patent-granted.

Gesture Recognition Algorithm of Human Motion Target Based on Deep Neural Network

There are some problems in the current human motion target gesture recognition algorithms, such as classification accuracy, overlap ratio, low recognition accuracy and recall, and long recognition time. A gesture recognition algorithm of human motion based on deep neural network was proposed. First, Kinect interface equipment was used to collect the coordinate information of human skeleton joints, extract the characteristics of motion gesture nodes, and construct the whole structure of key node network by using deep neural network. Second, the local recognition region was introduced to generate high-dimensional feature map, and the sampling kernel function was defined. The minimum space-time domain of node structure map was located by sampling in the space-time domain. Finally, the deep neural network classifier was constructed to integrate and classify the human motion target gesture data features to realize the recognition of human motion target. The results show that the proposed algorithm has high classification accuracy and overlap ratio of human motion target gesture, the recognition accuracy is as high as 93%, the recall rate is as high as 88%, and the recognition time is 17.8 s, which can effectively improve the human motion target attitude recognition effect.

A Novel Adaptive Mutation PSO Optimized SVM Algorithm for sEMG-Based Gesture Recognition

In the field of noncontact human-computer interaction, it is of crucial importance to distinguish different surface electromyography (sEMG) gestures accurately for intelligent prosthetic control. Gesture recognition based on low sampling frequency sEMG signal can extend the application of wearable low-cost EMG sensor (for example, MYO bracelet) in motion control. In this paper, a combination of sEMG gesture recognition consisting of feature extraction, genetic algorithm (GA), and support vector machine (SVM) model is proposed. Particularly, a novel adaptive mutation particle swarm optimization (AMPSO) algorithm is proposed to optimize the parameters of SVM; moreover, a new calculation method of mutation probability is also defined. The AMPSO-SVM model based on combination processing is successfully applied to MYO bracelet dataset, and four gesture classifications are carried out. Furthermore, AMPSO-SVM is compared with PSO-SVM, GS-SVM, and BP. The sEMG gesture recognition rate of AMPSO-SVM is 0.975, PSO-SVM is 0.9463, GS-SVM is 0.9093, and BP is 0.9019. The experimental results show that AMPSO-SVM is effective for low-frequency sEMG signals of different gestures.

Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System

In this study, we propose the gesture recognition algorithm using support vector machines (SVM) and histogram of oriented gradient (HOG). Besides, we also use the CNN model to classify gestures. We approach and select techniques of applying problem controlling for the robotic system. The goal of the algorithm is to detect gestures with real-time processing speed, minimize interference, and reduce the ability to capture unintentional gestures. Static gesture controls are used in this study including on, off, increasing, and decreasing. Besides, it uses motion gestures including turning on the status switch and increasing and decreasing the volume. Results show that the algorithm is up to 99% accuracy with a 70-millisecond execution time per frame that is suitable for industrial applications.

Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey.

Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, exoskeletons for augmentation, sign language recognition, human-computer interaction, and user authentication. Results showed that electrical, mechanical, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.

UWB-gestures, a public dataset of dynamic hand gestures acquired using impulse radar sensors

In the past few decades, deep learning algorithms have become more prevalent for signal detection and classification. To design machine learning algorithms, however, an adequate dataset is required. Motivated by the existence of several open-source camera-based hand gesture datasets, this descriptor presents UWB-Gestures, the first public dataset of twelve dynamic hand gestures acquired with ultra-wideband (UWB) impulse radars. The dataset contains a total of 9,600 samples gathered from eight different human volunteers. UWB-Gestures eliminates the need to employ UWB radar hardware to train and test the algorithm. Additionally, the dataset can provide a competitive environment for the research community to compare the accuracy of different hand gesture recognition (HGR) algorithms, enabling the provision of reproducible research results in the field of HGR through UWB radars. Three radars were placed at three different locations to acquire the data, and the respective data were saved independently for flexibility.

An Improved Hand Gesture Recognition Algorithm based on image contours to Identify the American Sign Language

It is an open era of research to provide the assistance communication to hearing and visually impaired people. For the same, this paper proposed a recognition and classification of hand gesture to identify the correct denotation with maximum accurateness for standard American Sign Language. The proposal intelligently used the information based on image contours to identify the character’s representation of hand gesture. The proposal optimizes the performance overhead through identifications of 17 characters and 6 symbols based on image contours and convexity measurement of Standard American Sign Language without using complex algorithms and specialized hardware devices. Accuracy measurement done through simulation, which shows how our proposal provide more accuracy with minimum complexity in comparison to other state-of-art works.

Gesture recognition device based on cross reticulated graphene strain sensors

Perception and recognition of hand gestures have received much attention for their applications in the human–computer fields. The data-glove application in gesture recognition makes it expediently to track the movements of human fingers. Whereas, the development of the data-glove for hand gesture still face numerous technical difficulties, such as the sensitivity, flexible ability, and system integration under low-cost. In this paper, a simple gesture recognition glove system was developed to characterize the sign language, which consists of cross reticulated graphene (CRG) flexible sensors, a multi-channel data acquisition module, and an artificial neural network algorithm. The designed acquisition module has several channels, so that signs from five sensors can be collected and transferred to the intelligent terminal to execute the hand gesture recognition algorithm. Besides, the recognition results suggest that the recognition rate of the neural network for 22 letters in the English alphabet can reach more than 90% and the overall recognition has an over 86% accuracy.

Footballer Action Tracking and Intervention Using Deep Learning Algorithm.

Fédération Internationale de Football Association is the governing body of the football world cup. The international tournament of football requires extensive training of all football players and athletes. In the training process of footballers, players and coaches recognize the training actions completed by footballers. The training actions are compared with standard actions, calculate losses, and scientifically intervene in the training processes. This intervention is important for better results during the training sessions. Coaches must determine and confirm that every action performed by the footballers meets the minimum standards. It is because the actions of individual players are performed quickly; as a result, the coach's eye may not produce accurate results as human activities are prone to errors. Therefore, this paper designs and develops a footballer's motion and gesture recognition and intervention algorithm using a convolutional neural network (CNN). In this proposed algorithm, initially, texture features and HSV features of the footballer's posture image are extracted and then a dual-channel CNN is constructed. Each characteristic is extracted separately, and the output of the dual-channel network is combined. Finally, the obtained results are passed from a fully connected CNN to estimate and construct the posture image of the footballer. This article performs experimental testing and comparative analysis on a wide range of data and also conducts ablation studies. The experimental work shows that the proposed algorithm achieves better performance results.

Dynamic gesture recognition algorithm Combining Global Gesture Motion and Local Finger Motion for interactive teaching

Adding dynamic gesture recognition to the new interactive teaching system is of great significance to improve the teaching efficiency. However, the features extracted by traditional dynamic gesture recognition methods are usually difficult to accurately represent the differences between dynamic gestures. Aiming at the problems of complex time sequence and spatial variability of dynamic gestures, this paper proposes a gesture recognition method combining global motion and local motion of fingers. Firstly, preprocessing of dynamic gesture data is carried out, including removing invalid gesture frames, completing gesture frame data and normalizing joint length. Then, according to the given hand joint coordinates, dynamic gesture key frames are extracted by using gesture distance function, and the global motion features of hand in space and the local motion features of fingers in hand are extracted based on gesture key frames. Secondly, the key frame gesture features of gesture global motion and finger local motion are fused, and linear discriminant analysis is used for feature dimensionality reduction. Finally, support vector machine with Gaussian kernel is used to realize dynamic gesture recognition and classification. Experimental results on DHG-14/28 and FPHA dynamic gesture dataset show that the accuracy of classification and recognition is 98.57% 88.29% and 97.31% respectively.

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.

A gesture recognition algorithm based on improved YOLOv3

A gesture recognition algorithm based on improved YOLOv3

Software Implementation of Gesture Recognition Algorithm Using Computer Vision

This paper examines the main methods and principles of image formation, display of the sign language recognition algorithm using computer vision to improve communication between people with hearing and speech impairments. This algorithm allows to effectively recognize gestures and display information in the form of labels. A system that includes the main modules for implementing this algorithm has been designed. The modules include the implementation of perception, transformation and image processing, the creation of a neural network using artificial intelligence tools to train a model for predicting input gesture labels. The aim of this work is to create a full-fledged program for implementing a real-time gesture recognition algorithm using computer vision and machine learning.

An identity authentication method for ubiquitous electric power internet of things based on dynamic gesture recognition

This paper presents a novel algorithm for gesture recognition and identity authentication based on continuous hidden Markov model (CHMM) and optical flow method. This study aims to solve the information security problems about ubiquitous electric power internet of things. In this system, the optical flow method is used to segment and extract the features of the preprocessed dynamic gesture information to obtain the features of the dynamic gesture motion track, and the CHMM is chosen to establish a valid user dynamic gesture model, which leads to ensuring the dynamic gestures are accurately recognised. The proposed method is test on accurately recognise the dynamic gestures and the result is compared with the dynamic time warpring (DTW) algorithm and practical swarm optimisation-radial basis function network (PSO-RBFN) algorithm. The result of the comparisons illuminates the superiority of the proposed method in terms of accuracy of identity authentication.

Research on Key Technologies of Hand Function Rehabilitation Training Evaluation System Based on Leap Motion

This paper proposes an immersive training system for patients with hand dysfunction who can perform rehabilitation training independently. The system uses Leap Motion binocular vision sensors to collect human hand information, and uses the improved PCA (Principal Component Analysis) to perform data fusion on the real-time data collected by the sensor to obtain more hands with fewer principal components, and improve the stability and accuracy of the data. Immediately, the use of improved SVM (Support Vector Machine) and KNN (K-Nearest Neighbor Algorithm) for gesture recognition and classification is proposed to enable patients to perform rehabilitation training more effectively. Finally, the effective evaluation results of the rehabilitation effect of patients by the idea of AHP (Analytic Hierarchy Process) are taken as necessary reference factors for doctors to follow up treatment. Various experimental results show that the system has achieved the expected results and has a good application prospect.

Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model.

The deep learning gesture recognition based on surface electromyography plays an increasingly important role in human-computer interaction. In order to ensure the high accuracy of deep learning in multistate muscle action recognition and ensure that the training model can be applied in the embedded chip with small storage space, this paper presents a feature model construction and optimization method based on multichannel sEMG amplification unit. The feature model is established by using multidimensional sequential sEMG images by combining convolutional neural network and long-term memory network to solve the problem of multistate sEMG signal recognition. The experimental results show that under the same network structure, the sEMG signal with fast Fourier transform and root mean square as feature data processing has a good recognition rate, and the recognition accuracy of complex gestures is 91.40%, with the size of 1 MB. The model can still control the artificial hand accurately when the model is small and the precision is high.

Dynamic Gesture Recognition Algorithm Based on 3D Convolutional Neural Network.

Gesture recognition is one of the important ways of human-computer interaction, which is mainly detected by visual technology. The temporal and spatial features are extracted by convolution of the video containing gesture. However, compared with the convolution calculation of a single image, multiframe image of dynamic gestures has more computation, more complex feature extraction, and more network parameters, which affects the recognition efficiency and real-time performance of the model. To solve above problems, a dynamic gesture recognition model based on CBAM-C3D is proposed. Key frame extraction technology, multimodal joint training, and network optimization with BN layer are used for making the network performance better. The experiments show that the recognition accuracy of the proposed 3D convolutional neural network combined with attention mechanism reaches 72.4% on EgoGesture dataset, which is improved greatly compared with the current main dynamic gesture recognition methods, and the effectiveness of the proposed algorithm is verified.

Dynamic Hand Gesture Recognition Based on Signals From Specialized Data Glove and Deep Learning Algorithms

Gesture recognition as a natural, convenient and recognizable way has been received more and more attention on human–machine interaction (HMI) recently. However, visual-based gesture recognition methods are often restricted by environments and classical wearable device-based strategies are suffered from relatively low accuracy or the complicated structures. In this study, we first design a low-cost and efficient data glove with simple hardware structure to capture finger movement and bending simultaneously. Second, a novel dynamic hand gesture recognition algorithm (DGDL-GR) is proposed to recognize human dynamic sign language, in which a fusion model of convolutional neural network (fCNN) and generic temporal convolutional network (TCN) is fully utilized. The fCNN (fusion of 1-D CNN and 2-D CNN) is proposed to extract time-domain features of finger resistance movement and spatial domain features of finger resistance bending simultaneously. Moreover, due to the superiorities of TCN in sequence modeling task, this work proposes a novel hand gesture recognition method based on the TCN, which includes causal convolution, dilation convolution, and a residual network with appropriate layers. Both long- and short-time dependencies of the hand gesture features are deeply mined and classified in the end. Results of extensive experiments have demonstrated that the proposed DGDL-GR algorithm outperforms many state-of-the-art algorithms on the measure of accuracy, F1 score, precision score, and recall score with the real-world dataset. Moreover, the number of residual blocks and some key hyperparameters of the proposed DGDL-GR algorithm has been studied thoroughly in this work.

Gesture Recognition Based on Improved YOLOv4-tiny Algorithm

With the development of human-computer interaction, gesture recognition is becoming more and more important. At the same time, mobile applications are developing rapidly, it is a development trend to implement human-computer interaction technology on the mobile terminal. An improved YOLOv4-tiny gesture recognition algorithm is proposed. Firstly, on the basis of YOLOv4-tiny network, the Spatial Pyramid Pooling(SPP) module is added to integrate the local and global features of the image to enhance the accurate positioning ability of the network. Secondly, a 1×1 convolution is added after the 3 maximum pooling layers of the original YOLOv4-tiny network and the newly added SPP module, which reduces the network parameters and improves the prediction speed of the network. On this basis, the K-means++ algorithm is used to generate an anchor box suitable for detecting gestures to speed up the network detection of gestures. In the gesture dataset NUS-II, compared with the YOLOv3-tiny algorithm and the YOLOv4-tiny algorithm, the improved algorithm mean Average Precision(mAP) is 100%, Frames Per Second(FPS) is 377, which can detect and recognize gestures quickly and accurately. The improved algorithm of this paper is deployed on the Android mobile terminal to realize the real-time gesture detection and recognition on the mobile terminal, which has great research significance for the development of human-computer interaction.

Shape-Based Features for Optimized Hand Gesture Recognition

Gesture recognition is the most intuitive form of human-computer interface. Hand gestures provide a natural way for humans to interact with computers to perform a variety of different applications. However, factors such as complexity of hand gesture structures, differences in hand size, hand posture, and environmental illumination can influence the performance of hand gesture recognition algorithms. Considering the above factors, this paper aims to present a real time system for hand gesture recognition on the basis of detection of some meaningful shape-based features like orientation, center of mass, status of fingers, thumb in terms of raised or folded fingers of hand and their respective location in image. The internet is growing at a very fast pace. The use of web browser is also growing. Everyone has at least two or three most frequently visited website. Thus, in this paper, effectiveness of the gesture recognition and its ability to control the browser via the recognized hand gestures are experimented and the results are analyzed.