Structural Reliability Analysis Based on Imperialist Competitive Algorithm

This paper deals with static and dynamic hand gesture (digits) recognition. The method provides a threefold novel contribution: (1) segmentation algorithm gives better results on any skin colour and any size of hand on complex and non-uniform background; (2) key frame finding algorithm and (3) the recognition technique of signs (static digits, alphabets and dynamic digits). We separate out key frames from a sequence of static gestures, which include correct gestures from a video sequence. The recognition efficiency of key frame detection is 93% using the proposed algorithm. The segmentation efficiency is almost 95%. Features are extracted using the proposed feature extraction algorithm, and gestures are recognised. We propose a novel algorithm for static and dynamic gesture recognition. The proposed algorithm shows recognition efficiency of 94.8% for static gestures and 94% for dynamic gestures.

Gesture recognition technology is widely used in the flexible and precise control of manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform dynamic gesture recognition. The result of surface EMG signal decoding is applied to the controller, which can improve the fluency of artificial hand control. Much current gesture recognition research using sEMG has focused on static gestures. In addition, the accuracy of recognition depends on the extraction and selection of features. However, Static gesture research cannot meet the requirements of natural human-computer interaction and dexterous control of manipulators. Therefore, a multi-stream residual network (MResLSTM) is proposed for dynamic hand movement recognition. This study aims to improve the accuracy and stability of dynamic gesture recognition. Simultaneously, it can also advance the research on the smooth control of the Manipulator. We combine the residual model and the convolutional short-term memory model into a unified framework. The architecture extracts spatiotemporal features from two aspects: global and deep, and combines feature fusion to retain essential information. The strategy of pointwise group convolution and channel shuffle is used to reduce the number of network calculations. A dataset is constructed containing six dynamic gestures for model training. The experimental results show that on the same recognition model, the gesture recognition effect of fusion of sEMG signal and acceleration signal is better than that of only using sEMG signal. The proposed approach obtains competitive performance on our dataset with the recognition accuracies of 93.52%, achieving state-of-the-art performance with 89.65% precision on the Ninapro DB1 dataset. Our bionic calculation method is applied to the controller, which can realize the continuity of human-computer interaction and the flexibility of manipulator control.

Human-computer interaction (HCI) is an important feature of augmented reality (AR) technology. The naturalness is the inevitable trend of HCI. Gesture is the most natural and frequently used body auxiliary interaction mode in daily interactions except for language. However, there are often meaningless, subconscious gesture intervals between the two adjacent dynamic gestures. So, continuous dynamic gesture spotting is the premise and basis of dynamic gesture recognition, but there is no mature and unified algorithm to solve this problem. In order to realize the natural HCI based on gesture recognition entirely, a general AR application development platform is presented in this paper. According to the position and pose tracking data of the user's hand, the dynamic gesture spotting algorithm based on evidence theory is proposed. Firstly, Through analysis of the speed change of hand motion during the dynamic gestures, three knowledge rules are summed up. Then, accurate dynamic gesture spotting is realized with the application of evidence reasoning. Moreover, this algorithm first detects the starting point of gesture in the rising trend of hand motion speed, eliminates the delay between spotting and recognition, and thus ensures real-time performance. Finally, the algorithm is verified in several AR applications developed on the platform. There are two main experimental results. First, there are six users participating in the dynamic gesture spotting experiment, and the gesture spotting accuracy can meet the demand. Second, The accuracy of recognition after spotting is higher than that of the simultaneous recognition and spotting. So, It can be concluded that the proposed continuous dynamic gesture spotting algorithm based on Dempster-Shafer theory can extract almost all the effective dynamic gestures in the HCI of our AR platform, and on this basis, it can effectively improve the accuracy of the subsequent dynamic gesture recognition.

Addressing the challenges of large-scale gesture recognition models, high computational complexity, and inefficient deployment on embedded devices, this study designs and implements a visual dynamic gesture recognition system based on a lightweight CNN-LSTM-DSA model. The system captures user hand images via a camera, extracts 21 keypoint 3D coordinates using MediaPipe, and employs a lightweight hybrid model to perform spatial and temporal feature modeling on keypoint sequences, achieving high-precision recognition of complex dynamic gestures. In static gesture recognition, the system determines the gesture state through joint angle calculation and a sliding window smoothing algorithm, ensuring smooth mapping of the servo motor angles and stability of the robotic hand's movements. In dynamic gesture recognition, the system models the key point time series based on the CNN-LSTM-DSA hybrid model, enabling accurate classification and reproduction of gesture actions. Experimental results show that the proposed system demonstrates good robustness under various lighting and background conditions, with a static gesture recognition accuracy of up to 96%, dynamic gesture recognition accuracy of 90.19%, and an overall response delay of less than 300 ms.

International retailing: B. Sternquist, Fairchild Publications, New York, five parts, fifteen chapters, 592 pages, hardback, 1998, ISBN 1-56367-103-4

With the constant development of smart devices, Gesture recognition is applied on more and more fields. Gesture recognition devices currently on the market are inconvenient and expensive. A gesture recognition method based on Kinect is proposed in this paper. The camera of Kinect is used to get gesture images and then a hidden Markov model is established to recognize dynamic gesture so that the operation is simple. The assessment, decoding algorithms and training issues of hidden Markov model in gesture recognition is focused in this paper. Viterbi algorithm and Baum-Welch algorithm are used to solve the problems above. After simulation experiments, it is discovered that the gesture recognition algorithm based on Kinect proposed in this paper has a high accuracy rate for a variety of gestures, simultaneously the naturalness of human-computer interaction has been greatly improved.

This paper presents a novel vision-based hand gesture recognition (HGR) and training system for a human-like robot hand. We implemented and trained a multiclass-SVM classifier and N-Dimensional DTW (ND-DTW) classifier for static posture recognition and dynamic gesture recognition. Training features were extracted from the raw gestures depth data captured by Leap Motion Controller. The experimental results show that multiclass SVM method has an average 98.25% recognition rates and the shortest run time when compared to k-NN and ANBC. For dynamic gestures, ND-DTW classifier displays a better performance than DHMM with an average 95.5% recognition rate and significantly shorter run time. In conclusion, the combination of SVMs and DTW proves the efficiency and high accuracy in proposed human-robot interaction system.

Dynamic gesture recognition systems face persistent challenges in achieving real-time performance and high recognition efficiency. This paper presents a novel framework integrating computer vision techniques with machine learning algorithms to address these issues. The approach utilizes OpenCV for dynamic gesture detection, employing gesture contour extraction using skin color feature segmentation, fingertip detection for static gesture recognition, and dynamic gesture tracking through Hidden Markov Models (HMM). Experimental results demonstrate high recognition accuracy of 95.8 ± 1.4% across various gesture types, with individual rates ranging from 95.2 ± 1.6% to 98.5 ± 0.8%. Real-time performance is achieved with average processing time of 82.71 ± 3.2 ms per frame (12.1 FPS). Our method demonstrates 1.15× to 1.49× speed improvements over state-of-the-art approaches while maintaining superior accuracy. Validation on the DHG-14/28 public benchmark confirms generalizability with 93.4 ± 1.6% accuracy. The framework shows robust performance under challenging conditions: low-light environments (92.4 ± 2.1%), fast motion (91.8 ± 1.9%), and complex backgrounds (93.2 ± 1.7%). Statistical significance was confirmed through comprehensive evaluation across diverse demographic groups. This research has implications for human-computer interaction applications, including smart home systems, augmented reality, and industrial automation.

Gesture is a natural form of human communication, and it is of great significance in human–computer interaction. In the dynamic gesture recognition method based on deep learning, the key is to obtain comprehensive gesture feature information. Aiming at the problem of inadequate extraction of spatiotemporal features or loss of feature information in current dynamic gesture recognition, a new gesture recognition architecture is proposed, which combines feature fusion network with variant convolutional long short‐term memory (ConvLSTM). The architecture extracts spatiotemporal feature information from local, global and deep aspects, and combines feature fusion to alleviate the loss of feature information. Firstly, local spatiotemporal feature information is extracted from video sequence by 3D residual network based on channel feature fusion. Then the authors use the variant ConvLSTM to learn the global spatiotemporal information of dynamic gesture, and introduce the attention mechanism to change the gate structure of ConvLSTM. Finally, a multi‐feature fusion depthwise separable network is used to learn higher‐level features including depth feature information. The proposed approach obtains very competitive performance on the Jester dataset with the classification accuracies of 95.59%, achieving state‐of‐the‐art performance with 99.65% accuracy on the SKIG (Sheffifield Kinect Gesture) dataset.

A dynamic gesture recognition and understanding method in natural human-computer interaction under desktop environment is proposed, including the “reach”, “take up”, “move”, “put down”, “return”, “point” and other natural interactive gestures. In preprocess procedure of each frame of the video, the Gaussian background model and HSV skin-color model is employed to remove background and segment hand gestures. The temporal and spatial information of multi frame images is combined to construct temporal and spatial features of dynamic gestures images. Then a convolution neural network is built for recognize the dynamic characteristics of gesture image. Finally, the classification result is denoised to achieve the robust recognition and understanding of gestures. Experimental results show that the proposed method has a good ability of recognizing and understanding the dynamic gestures in the desktop environment.

This paper describes a hidden Markov model-based static and dynamic 3D gesture recognition system. The shape and position of the hands, segmented and tracked using a novel 3D statistical model for the upper body in stereo sequences, are used as observation vectors. The upper body model allows for accurate 3D localization of the hands in the presence of partial occlusions, self occlusions and different illumination conditions. The accuracy of our approach is reflected by the performance of our 3D gesture based editing system, that reaches 96% over 12 dynamic gestures and four static gestures.

Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction

Airwriting recognition is the task of identifying letters written in free space with finger movement. It is a dynamic gesture recognition with the vocabulary of gestures corresponding to letters in a given language. Electromyography (EMG) is a technique used to record electrical activity during muscle contraction and relaxation as a result of movement and is widely used for gesture recognition. Most of the current research in gesture recognition is focused on identifying static gestures. However, dynamic gestures are natural and user-friendly for being used as alternate input methods in human–computer interaction (HCI) applications. Airwriting recognition using EMG signals recorded from forearm muscles is, therefore, a viable solution. Since the user does not need to learn any new gestures and a large range of words can be formed by concatenating these letters, it is generalizable to a wider population. There has been limited work in recognition of airwriting using EMG signals and forms the core idea of the current work. The SurfMyoAiR dataset comprising of EMG signals recorded during writing English uppercase alphabets is constructed. Several different time-domain features to construct EMG envelope and two different time–frequency image representations: short-time Fourier transform and continuous wavelet transform were explored to form the input to a deep learning model for airwriting recognition. Several different deep learning architectures were exploited for this task. In addition, the effect of various parameters, such as signal length, window length, and interpolation techniques on the recognition performance, is comprehensively explored. The best-achieved accuracy was 78.50% and 62.19% in user-dependent and user-independent scenarios, respectively, by using short-time Fourier transform in conjunction with a 2-D convolutional neural network (CNN)-based classifier. Airwriting has great potential as a user-friendly modality to be used as an alternate input method in HCI applications.

Low-pixel time-of-flight (ToF) sensors offer advantages in low power consumption, small size, and low cost. For gesture recognition, there are two advantages. Firstly, they are robust to changes in lighting conditions. Secondly, they could protect privacy. This paper addresses the challenge of utilizing an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8}\times \mathbf{8}$</tex> pixel ToF sensor with extremely low resolution by proposing a signal processing system. The system includes pre-processing the data through non-global mean filtering to calibrate outliers. Additionally, we design a frames difference sliding window to ensure the integrity of gesture sequences and use Lagrangian interpolation to norm the fixed length. Finally, we design a tiny parallel convolutional network (TPC-Net) to recognize dynamic gestures. Experimental results demonstrate that the proposed system achieves an average accuracy of 98.47% over 6 dynamic gestures. Furthermore, our system achieves better performance with less model complexity compared to existing methods using low-pixel ToF sensors. These results illustrate the effectiveness of our system.

Gesture recognition based on body-worn devices can be used for healthy improvement and life support. Many methods have been proposed for gesture recognition. However, most of them are concerned about recognition accuracy only. In some practical applications, real-time performance of a recognition method on a wearable device is also a key problem. In the paper, we propose a probability model for accurate and real-time recognition of dynamic gestures. The model learns from HMM but difference in the sense that our model builds probability matrices of feature distribution for each observation points instead of each gesture, which reduces the number of probability matrix to improve processing efficiency. A gesture can be recognized by the way of look-up table to search maximum similarity to pre-stored gestures in the matrices. To verify the model, eight kinds of one-stroke finger gestures are taken as the target of recognition. Result shows reasonable recognition accuracy and computational complexity.KeywordsWearable ComputingGesture RecognitionOne-stroke GesturesProbability Model