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Walking is an exercise that uses muscles and joints of the human body and is essential for understanding body condition. Analyzing body movements through gait has been studied and applied in human identification, sports science, and medicine. This study investigated a spatiotemporal graph convolutional network model (ST-GCN), using attention techniques applied to pathological-gait classification from the collected skeletal information. The focus of this study was twofold. The first objective was extracting spatiotemporal features from skeletal information presented by joint connections and applying these features to graph convolutional neural networks. The second objective was developing an attention mechanism for spatiotemporal graph convolutional neural networks, to focus on important joints in the current gait. This model establishes a pathological-gait-classification system for diagnosing sarcopenia. Experiments on three datasets, namely NTU RGB+D, pathological gait of GIST, and multimodal-gait symmetry (MMGS), validate that the proposed model outperforms existing models in gait classification.

The prevention of falls has become crucial in the modern healthcare domain and in society for improving ageing and supporting the daily activities of older people. Falling is mainly related to age and health problems such as muscle, cardiovascular, and locomotive syndrome weakness, etc. Among elderly people, the number of falls is increasing every year, and they can become life-threatening if detected too late. Most of the time, ageing people consume prescription medication after a fall and, in the Japanese community, the prevention of suicide attempts due to taking an overdose is urgent. Many researchers have been working to develop fall detection systems to observe and notify about falls in real-time using handcrafted features and machine learning approaches. Existing methods may face difficulties in achieving a satisfactory performance, such as limited robustness and generality, high computational complexity, light illuminations, data orientation, and camera view issues. We proposed a graph-based spatial-temporal convolutional and attention neural network (GSTCAN) with an attention model to overcome the current challenges and develop an advanced medical technology system. The spatial-temporal convolutional system has recently proven the power of its efficiency and effectiveness in various fields such as human activity recognition and text recognition tasks. In the procedure, we first calculated the motion along the consecutive frame, then constructed a graph and applied a graph-based spatial and temporal convolutional neural network to extract spatial and temporal contextual relationships among the joints. Then, an attention module selected channel-wise effective features. In the same procedure, we repeat it six times as a GSTCAN and then fed the spatial-temporal features to the network. Finally, we applied a softmax function as a classifier and achieved high accuracies of 99.93%, 99.74%, and 99.12% for ImViA, UR-Fall, and FDD datasets, respectively. The high-performance accuracy with three datasets proved the proposed system’s superiority, efficiency, and generality.

The increase in e-learning platforms, especially Massive Open Online Courses (MOOCs), highlights the necessity for sophisticated, privacy-conscious recommendation algorithms that adjust to evolving learner interactions in IoT-integrated settings. This study introduces an innovative architecture that utilizes Federated Learning (FL) to safeguard user privacy during distributed training on educational platforms. This approach utilizes Graph Convolutional Networks (GCN) to depict intricate user-course interactions as a graph, adeptly capturing higher-order relational dependencies. Furthermore, DistilBERT-based feature extraction generates concise, semantically dense representations from course descriptions, hence improving content relevancy. Real-time IoT data, including user engagement metrics from smart devices, dynamically influences graph connections, facilitating context-aware recommendations.The suggested solution emphasizes scalability and privacy, tackling essential issues in contemporary e-learning environments. Thorough assessments indicate that our methodology substantially surpasses baseline methodologies across various performance indicators, providing exceptionally tailored course recommendations. This research promotes the advancement of adaptive, safe, and efficient recommendation systems for IoT-integrated e-learning, enhancing engaging and personalized learning experiences for users globally.

The prevention of falls is paramount in modern healthcare, particularly for the elderly, as falls can lead to severe injuries or even fatalities. Additionally, the growing incidence of falls among the elderly, coupled with the urgent need to prevent suicide attempts resulting from medication overdose, underscores the critical importance of accurate and efficient methods of detecting a fall. This makes a computer-aided fall detection system necessary to save elderly people’s lives worldwide. Many researchers have been working to develop fall detection systems. However, the existing systems often struggle with problems such as unsatisfactory accuracy, limited robustness, high computational complexity, and sensitivity to environmental factors. In response to these challenges, this paper proposes a novel three-stream spatio-temporal feature-based human fall detection system. Our system incorporates joint skeleton-based spatial and temporal Graph Convolutional Network (GCN) features, joint motion-based spatial and temporal GCN features, and residual connections-based features. Each stream employs adaptive graph-based feature aggregation and consecutive separable convolutional neural networks (Sep-TCN), significantly reducing the computational complexity and the number of parameters of the model compared to prior systems. Experimental results on multiple datasets demonstrate the superior effectiveness and efficiency of our proposed system, with accuracies of 99.68%, 99.97%, 99.47 % and 98.97% achieved on the ImViA, Fall-UP, FU-Kinect and UR-Fall datasets, respectively. The remarkable performance of our system highlights its superiority, efficiency, and generalizability in real-world human fall detection scenarios, offering significant advancements in healthcare and societal well-being.

Hand gesture recognition based on sparse multichannel surface electromyography (sEMG) still poses a significant challenge to deployment as a muscle–computer interface. Many researchers have been working to develop an sEMG-based hand gesture recognition system. However, the existing system still faces challenges in achieving satisfactory performance due to ineffective feature enhancement, so the prediction is erratic and unstable. To comprehensively tackle these challenges, we introduce a novel approach: a lightweight sEMG-based hand gesture recognition system using a 4-stream deep learning architecture. Each stream strategically combines Temporal Convolutional Network (TCN)-based time-varying features with Convolutional Neural Network (CNN)-based frame-wise features. In the first stream, we harness the power of the TCN module to extract nuanced time-varying temporal features. The second stream integrates a hybrid Long short-term memory (LSTM)-TCN module. This stream extracts temporal features using LSTM and seamlessly enhances them with TCN to effectively capture intricate long-range temporal relations. The third stream adopts a spatio-temporal strategy, merging the CNN and TCN modules. This integration facilitates concurrent comprehension of both spatial and temporal features, enriching the model’s understanding of the underlying dynamics of the data. The fourth stream uses a skip connection mechanism to alleviate potential problems of data loss, ensuring a robust information flow throughout the network and concatenating the 4 stream features, yielding a comprehensive and effective final feature representation. We employ a channel attention-based feature selection module to select the most effective features, aiming to reduce the computational complexity and feed them into the classification module. The proposed model achieves an average accuracy of 94.31% and 98.96% on the Ninapro DB1 and DB9 datasets, respectively. This high-performance accuracy proves the superiority of the proposed model, and its implications extend to enhancing the quality of life for individuals using prosthetic limbs and advancing control systems in the field of robotic human–machine interfaces.

We address the prediction of non-imaging variables based on structural brain connectivity derived from diffusion magnetic resonance images, using graph-based machine learning. We predict age and the mini-mental state examination (MMSE) score as examples of a demographic and a clinical variable. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity graph as input and processes the data separately through a parallel GCN mechanism with multiple branches. The novelty of our work lies in the model architecture, especially the Connectivity Attention Block, which learns an embedding representation of brain graphs while providing graph-level attention. We show experiments on publicly available datasets of PREVENT-AD and OASIS3. We validate our model by comparing it to existing methods and via ablations. This quantifies the degree to which the connectome varies depending on the task, which is important for improving our understanding of health and disease across the population. The proposed model generally demonstrates higher performance especially for age prediction compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning.

Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain–computer interface (BCI). Researchers initially developed simple linear models and machine learning algorithms to classify and recognize brain activities. With the great success of deep learning on image recognition and generation, deep neural networks (DNN) have been engaged in reconstructing visual stimuli from human brain activity via functional magnetic resonance imaging (fMRI). In this paper, we reviewed the brain activity decoding models based on machine learning and deep learning algorithms. Specifically, we focused on current brain activity decoding models with high attention: variational auto-encoder (VAE), generative confrontation network (GAN), and the graph convolutional network (GCN). Furthermore, brain neural-activity-decoding-enabled fMRI-based BCI applications in mental and psychological disease treatment are presented to illustrate the positive correlation between brain decoding and BCI. Finally, existing challenges and future research directions are addressed.

Graph Neural Networks (GNNs), as a deep learning framework specifically designed for graph-structured data, have achieved deep representation learning of graph data through message passing mechanisms and have become a core technology in the field of graph analysis. However, current reviews on GNN models are mainly focused on smaller domains, and there is a lack of systematic reviews on the classification and applications of GNN models. This review systematically synthesizes the three canonical branches of GNN, Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sampling Aggregation Network (GraphSAGE), and analyzes their integration pathways from both structural and feature perspectives. Drawing on representative studies, we identify three major integration patterns: cascaded fusion, where heterogeneous modules such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and GraphSAGE are sequentially combined for hierarchical feature learning; parallel fusion, where multi-branch architectures jointly encode complementary graph features; and feature-level fusion, which employs concatenation, weighted summation, or attention-based gating to adaptively merge multi-source embeddings. Through these patterns, integrated GNNs achieve enhanced expressiveness, robustness, and scalability across domains including transportation, biomedicine, and cybersecurity.

Stock prices movement prediction has been a longstanding research topic. Many studies have introduced several kinds of external information like relations of stocks, combined with internal information of trading characteristics to promote forecasting. Different from previous cases, this article proposes a reasonable assumption that major fluctuations of stock prices are mainly triggered by high-volume transactions which usually occur on a group of stocks that share some common features (e.g., stocks in the same industry, region, concept or yield similar volatility), and further develops an integrated GCN-LSTM method to achieve more precise predictions from the perspective of modelling capital flows. First, we construct four kinds of graphs incorporating various relational knowledge (edge) and utilize graph convolutional network (GCN) to extract stock (node) embeddings in multiple time-periods. Then, the obtained temporal sequences of stock embeddings are put into long short-term memory recurrent neural network (LSTM) to discriminate the moving direction of prices. Extensive experiments on major Chinese stock indexes have demonstrated the effectiveness of our model with best accuracy of 57.81% acquired, which is much better than baselines. Moreover, experimental results of GCN-LSTM under different graphs and various node embedding dimensions have been compared and analyzed, indicating the selection of key parameters to achieve optimal performances. Our research findings provide an improved model to forecast stock prices movement directions with a reliable theoretical interpretation, and in depth exhibit insights for further applications of graph neural networks and graph data in business analytics, quantitative finance, and risk management decision-makings.

Point cloud upsampling algorithms can improve the resolution of point clouds and generate dense and uniform point clouds, and are an important image processing technology. Significant progress has been made in point cloud upsampling research in recent years. This paper provides a comprehensive survey of point cloud upsampling algorithms. We classify existing point cloud upsampling algorithms into optimization-based methods and deep learning-based methods, and analyze the advantages and limitations of different algorithms from a modular perspective. In addition, we cover some other important issues such as public datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future research directions and open issues that should be further addressed.