Voxel event graph neural network for event-based human gait recognition

The later conculsions can be obtained from the experimental results of the fourth section. 1. The three parts of speech recognition are conjunct one another and exist the relation restricted among themselves. Bark wavelet was used in improving the feature of ZCPA and MFCC the latter effect is obviously better than the former.It illustrates that Bark wavelet and the speech character described by MFCC feature are more closer than Bark wavelet and the speech character described by ZCPA feature. The fact is also as such. Bark wavelet is constructed directly according to the hearing perception of human ear, and MFCC is the cepstrum coefficents on the basis of Mel frequency. While Mel frequency is just the hearing frequency of human ear. Though the frequency bins of ZCPA are divided according to the hearing perception, the zero-crossing rate and peak amplitude are time-domain parameters which are transformed nonlinearly mapping to frequency bin. This kind of nonlinear transform may affect the consistency of ZCPA and hearing frequency, that results in decreasing in function. If the selection of training or recognition network is different, they have different effect on the results. Furthermore, the function of recogntion network has direct relationship with front-end filter and feature extracted. This point can be seen from the experimental results of combination mode1 FIR+ZCPA+HMM and mode 2 FIR+ZCPA+WNN . Comparing the two modes, the former two parts are same and the third part is different from using HMM or WNN the results obtained have much more different.The wavelet neural network has bright foreground for speech recognition.Its training speed is fast, which is good for implementation in real time. Further,it has also good recognition rates under no noise or noise environment and the number of recogintion words is larger. The paper researched some kinds combination modes aiming to the three parts of speech recognition system in Fig. 1. For other combination modes, such as Bark+MFCC+WNN Bark+ZCPA+WNN and so on, we will research them in later work. Which of combination ever is optimal? This needs considering practical application case. We hope the research can be refered by interesting researcher and get to the purpose of communication mutually and progress.

Human gait recognition is crucial in multimedia, enabling identification through walking patterns without direct interaction, enhancing the integration across various media forms in real-world applications like smart homes, healthcare and non-intrusive security. LiDAR's ability to capture depth makes it pivotal for robotic perception and holds promise for real-world gait recognition. In this paper, based on a single LiDAR, we present the Hierarchical Multi-representation Feature Interaction Network (HMRNet) for robust gait recognition. Prevailing LiDAR-based gait datasets primarily derive from controlled settings with predefined trajectory, remaining a gap with real-world scenarios. To facilitate LiDAR-based gait recognition research, we introduce FreeGait, a comprehensive gait dataset from large-scale, unconstrained settings, enriched with multi-modal and varied 2D/3D data. Notably, our approach achieves state-of-the-art performance on prior dataset (SUSTech1K) and on FreeGait. https://4dvlab.github.io/project_page/FreeGait.html

This paper presents the neuro-fuzzy Takagi-Sugeno-Kang (TSK) network for the recognition and classification of flavor. The important role in this process fulfills the self-organizing process used for the creation of the inference rules. The self-organizing neurons perform the role of clustering data into fuzzy groups with different membership values (the preprocessing stage). Applying the automatic control of clusters, we have the optimal size of the TSK network. The developed measuring system has been applied for the recognition of flavor of different brands of beer. The fuzzy neural network is used for processing signals obtained from the semiconductor sensor array. The results of numerical experiments have confirmed the excellent performance of such solutions.

DCapsNet: Deep capsule network for human activity and gait recognition with smartphone sensors

The sensor-based human activity recognition is a key technology for modern intelligent sports. However, the complexity of sport activities and lacking of large-scale dataset give rise to the challenges on training effective deep neural networks for it. On image/video-based computer vision tasks, deep learning models can be pretrained on large-scale datasets which are semantically similar with specific tasks. However, we cannot pretrain deep learning models for sensor-based human activity recognition due to lacking public large-scale datasets. To get rid of this problem, we propose a similarity-based graph network for the sensor-based human activity recognition. Specifically, it is a Convolutional Neural Network (CNN) being enhanced with an embedded Graph Neural Network (GNN) for learning the label relationship in terms of two proposed similarity measures. The experimental results on BSS-V2 dataset demonstrate that our proposed network outperforms prior state-of-the-art work by 10.3% in accuracy and 13.3% better than backbone CNN model.

IntroductionHuman gait motion intention recognition is very important for the lower extremity exoskeleton robot to accurately synchronize and respond to the user’s natural motion. And motion intention recognition is generally performed through sEMG. Deep learning neural networks perform well in dealing with high-dimensional data and nonlinear relationships such as sEMG, but different deep learning neural networks have their own advantages in dealing with different types of data. Therefore, a multi-branch deep learning neural network, which enables different neural networks to process different feature items, could achieve more accurate and efficient motion intention recognition. The purpose of this study is to 1) Establish a multi-branch deep learning neural network model to achieve accurate gait recognition and effective estimation of joint angles. 2) Quantify the performance of the multi-branch deep learning neural network model in gait recognition and joint angle prediction using sEMG.MethodologyThis study involved the collection of sEMG and plantar pressure data during walking in human subjects. Firstly, the collected signals are filtered and denoised to ensure the quality and reliability of the data. Calculate the time domain features and the frequency domain features to capture the key information of gait. Then, using the sensitivity difference of different structural neural networks to different feature data, a multi-branch deep learning neural network model is developed, in which the extracted features are used as the input of the model. The output of the model includes gait cycle and joint angle, so as to realize the accurate recognition of human gait and the effective estimation of joint angle.ResultsThe results show that the proposed method has high accuracy in identifying human gait and estimating joint angles. The multi-branch neural network model successfully integrates time-domain and frequency-domain features and provides reliable prediction of gait cycle and joint angle. The highest accuracy of gait recognition is 95.42%, the lowest is 90.11%, and the average is 92.16%. The average error of joint angle estimation is 3.19.DiscussionThis study designed a human walking gait recognition and joint angle prediction model to achieve accurate human lower limb motion intention recognition.The model can be integrated into the sEMG sensor to design a angular biosensors, which can predict the human joint angle in real time.

Deep learning techniques have gained success in radar-based human gait recognition in recent years. To exploit the time-varying micro-Doppler information of human gait, most existing methods generate samples using short time Fourier transform (STFT). By extracting the features of STFT images with different sliding window lengths, dual-channel deep convolutional neural network (DC-DCNN) can obtain high accuracy for human gait. To further improve the performance of the network, this paper designs a multi-spectral-attention dual-channel network for gait recognition. Combined with the channel attention mechanism, the proposed method can achieve higher recognition accuracy without incurring additional radar resources. Experimental results have demonstrated the effectiveness of the method.

Complex systems are often modelled using graphs, and one of the key tasks in complex system analysis is identifying anomalies in a graph. A graph anomaly is a pattern that does not follow the typical patterns predicted by the graph's structures and/or properties. The present article provides a comprehensive review of the techniques, challenges, and advancements in the field of Deep Learning and Graph Neural Networks for Mathematical Pattern Recognition. This review highlights the effectiveness of Deep Learning (DL) and Graph Neural Networks (GNNs) in mathematical pattern recognition. Graph-based models, particularly GraphMR built on Graph2Seq, demonstrate superior performance in model accuracy and efficiency over traditional Seq2Seq methods. GNNs effectively handle structured data like ASTs and DAGs, preserving semantic and syntactic information. The integration of encoder–decoder architectures and graph-based reasoning shows significant advancements in recognizing mathematical structures. The evolution from structural methods to DL and GNN approaches underscores the progress in recognition accuracy. As ML adoption grows, the need for large, high-quality datasets becomes critical for training next-generation models.

Mathematical formula recognition aims to automatically convert formula images into their structured description formats. Recently, some encoder-decoder models have been presented for this task, while they seldom explicitly consider spatial relationship among symbols. In this paper, we proposed a novel encoder-decoder model with Graph Neural Network (GNN) to translate mathematical formula images into LaTeX codes. In the proposed model, the symbols segmented from the raw image are used to build graphs based on their spatial connection. The encoder consists of Convolutional Neural Network (CNN) and GNN. CNN is utilized to extract the visual features from the whole formula or symbols, and GNN is used to transmit the spatial information embedded in the built graphs. The adopted decoder is a Recurrent Neural Network (RNN) model, which implements a language model to generate the output sentences based on the encoded features with attention mechanism. The experimental results on IM2LATEX-100K dataset demonstrated that the proposed model obtained a better performance than state-of-the-art approaches.

Aim: The study aims to identify or recognize the alphabets using neural networks and fuzzy classifier/logic. Methods and materials: Neural network and fuzzy classifier are used for comparing the recognition of characters. For each classifier sample size is 20. Character recognition was developed using MATLAB R2018a, a software tool. The algorithm is again compared with the Fuzzy classifier to know the accuracy level. Results: Performance of both fuzzy classifier and neural networks are calculated by the accuracy value. The mean value of the fuzzy classifier is 82 and the neural network is 77. The recognition rate (accuracy) with the data features is found to be 98.06%. Fuzzy classifier shows higher significant value of P=0.002 < P=0.005 than the neural networks in recognition of characters. Conclusion: The independent tests for this study shows a higher accuracy level of alphabetical character recognition for Fuzzy classifier when compared with neural networks. Henceforth, the fuzzy classifier shows higher significant than the neural networks in recognition of characters.

Smart home is an alternative function in present accommodation. Many technologies are developed for smart home. Human gait recognition system for indoor area is applied for monitoring purpose such as elder health care system. However, simple and effective algorithm is necessary for real-world applications. In this paper, we propose a new method of human gait identification system for indoor area by using human joints projection and closest distance technique. The algorithm consists of four step: image sequence acquisition, human joints segmentation, human joints projection and gait recognition. Firstly, human 2D sequence image is acquired. Red nine markers are attached on human joints. Secondly, markers are extracted by color thresholding technique. Human joints are then tracked. Third, such joints is projected in a dimension. Finally, Euclidean distance technique is used to compare two 2D human points between test data and train data. Human gait is recognized by the closest distance. To evaluate the proposed method, fifteen subjects walk pass 2D camera in same one way. A subject was tested three time. Average accuracy rate is 95.55%. This method perform effectively. The advantages of this method over existing methods is simple algorithm and effective for small size of target as using in accommodation.

With the development of deep learning, graph neural networks have attracted ever-increasing attention due to their exciting results on handling data from non-Euclidean space in recent years. However, existing graph neural networks frameworks are designed based on simple graphs, which limits their ability to handle data with complex correlations. Therefore, in some special cases, especially when the data have interdependence, the complexity of the data poses a significant challenge to traditional graph neural networks algorithm. To overcome this challenge, researchers model the complex relationship of data by constructing hypergraph, and use hypergraph neural networks to learn the complex relationship within data, so as to effectively obtain higher-order feature representations of data. In this paper, we first review the basics of hypergraph, then provide a detailed analysis and comparison of some recently proposed hypergraph neural networks algorithm, next some applications of hypergraph neural networks for action recognition are listed, and finally propose potential future research directions of hypergraph neural networks to provide ideas for subsequent research.

This paper provides a brief review of the state-of-the-art of neural networks in off-line text recognition. We discuss the role that neural networks have played in text recognition. We also assess the state of the art of neural networks in character and word recognition. Despite the success of neural networks in character and word recognition, there are still many challenging problems.

In this paper, we propose a unified convolutional neural network (CNN) framework for robust gait recognition against posture changes (e.g., those induced by walking speed changes). In order to mitigate the posture changes, we first register an input matching pair of gait features with different postures by a deformable registration network, which estimates a deformation field to transform the input pair both into their intermediate posture. The pair of the registered features is then fed into a recognition network. Furthermore, ways of the deformation (i.e., deformation patterns) can differ between the same subject pairs (e.g., only posture deformation) and different subject pairs (e.g., not only posture deformation but also body shape deformation), which implies the deformation pattern can be another cue to distinguish the same subject pairs from the different subject pairs. We therefore introduce another recognition network whose input is the deformation pattern. Finally, the deformable registration network, and the two recognition networks for the registered features and the deformation patterns, constitute the whole framework, named DeformGait, and they are trained in an end-to-end manner by minimizing a loss function which is appropriately designed for each of verification and identification scenario. Experiments on the publicly available dataset containing the largest speed variations demonstrate that the proposed method achieves the state-of-the-art performance in both identification and verification scenarios.

In this paper, we propose a robust end-to-end classification model, Graph-in-Graph Neural Network (GIGNet), for automatic modulation recognition (AMR). In GIGNet, multi-level graph neural networks (GNNs) are utilized to extract internal graph-based features from signal samples and correlation information between different signals treated as nodes in a graph. Specifically, a graph-level GNN is utilized to extract local and global features of signal samples transformed into graphs. Next, a method for constructing a graph that corresponds signals to nodes is proposed to assess the degree of association between nodes and to find closer neighbors of nodes. These closer neighbors enable the subsequent node-level GNN to incorporate appropriate correlation information for the further classification task. Compared to classical deep learning models and existing GNN-based models, experimental results justify the advantages of the proposed GIGNet model on recognition accuracy and robustness at low signal-to-noise ratio (SNR).