Explore the vast range of titles available.

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.

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.

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.

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.

Recently, the challenge of the boundary detection of isolated signs in a continuous sign video has been studied by researchers. To enhance the model performance, replace the handcrafted feature extractor, and also consider the hand structure in these models, we propose a deep learning-based approach, including a combination of the Graph Convolutional Network (GCN) and the Transformer models, along with a post-processing mechanism for final boundary detection. More specifically, the proposed approach includes two main steps: Pre-training on the isolated sign videos and Deploying on the continuous sign videos. In the first step, the enriched spatial features obtained from the GCN model are fed to the Transformer model to push the temporal information in the video stream. This model in pre-trained only using the pre-processed isolated sign videos with same frame lengths. During the second step, the sliding window method with the pre-defined window size is moved on the continuous sign video, including the un-processed isolated sign videos with different frame lengths. More concretely, the content of each window is processed using the pre-trained model obtained from the first step and the class probabilities of the Fully Connected (FC) layer embedded in the Transformer model are fed to the post-processing module, which aims to detect the accurate boundary of the un-processed isolated signs. In addition, we propose to present a non-anatomical graph structure to better present the hand joints movements and relations during the signing. Relying on the proposed non-anatomical hand graph structure as well as the self-attention mechanism in the Transformer model, the proposed model can successfully tackle the challenges of boundary detection in continuous sign videos. Experimental results on two datasets show the superiority of the proposed model in dealing with isolated sign boundary detection in continuous sign sequences.

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.

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.

The proliferation of Internet of Medical Things (IoMT) devices connected to the internet poses significant challenges to data integrity, confidentiality, and patient safety due to their vulnerability to outside exploitation. Specifically, IoMT devices capture and process vast amounts of sensitive patient data but often lack adequate security mechanisms, making them susceptible to attacks that compromise data integrity—such as the injection of false or fabricated information—which imposes significant risks on the patient. To address this, we introduce a novel hybrid anomaly detection model combining a Graph Convolutional Network (GCN) with a transformer architecture. The GCN captures the structural relationships within the IoMT data, while the transformer models the sequential dependencies in the anomalies. We evaluate our approach using the novel CICIOMT24 dataset, the first of its kind to emulate real-world IoMT network traffic from over 40 devices and 18 distinct cyberattacks. Compared against several machine learning baselines (including Logistic Regress, Random Forest, and Adaptive Boosting), the hybrid model effectively captures attacks and provides early detection capabilities. This work demonstrates a scalable and robust solution to enhance the safety and security of both IoMT devices and critical patient data.

We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network. Compared with the existing techniques, the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction ability. On this basis, a framework for transient fault detection and classification is created. Graph structure is generated to provide topology information to the task. Our approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs, and outputs the predicted classification results rapidly. Furthermore, the proposed approach is tested in various situations and its generalization ability is verified by experimental results. The results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques, and it is practical for online transmission line protection for its rapidness, high robustness and generalization ability.

This paper proposes a unified data-driven framework for topology identification, risk quantification, and reconfiguration optimization in power distribution networks under incomplete and fragmented observability. Motivated by real-world challenges where asset metadata, SCADA records, GIS layouts, and dispatcher logs are misaligned or incomplete, the proposed approach reconstructs network topology using a graph convolutional network (GCN) that fuses heterogeneous data attributes and learns structural representations from partial connectivity information. On the inferred topology, a scenario-based risk evaluation model is formulated to capture both local fragility and spatial risk propagation, integrating factors such as load stress, asset aging, and nodal redundancy into a unified zone-level risk index. To mitigate this risk, a bilevel reconfiguration optimization model is developed, in which the upper level minimizes cumulative risk and switching cost while maximizing load restoration, and the lower level enforces electrical feasibility under contingency-aware constraints. The full pipeline is tested on a 58-node synthetic distribution system with embedded DERs, showcasing the ability of the framework to reduce peak nodal risk by 52.7%, restore over 94% of total demand in 90% of scenarios, and maintain tractable computation times under 9 mins per scenario across 100 fault cases. A suite of detailed visualizations–including confidence-based topology maps, switching heatmaps, congestion-weighted flow diagrams, and fairness-control tradeoff surfaces–demonstrates the interpretability and operational relevance of the results. The proposed framework offers a scalable, adaptive solution for resilient distribution network management under uncertainty and fragmented digital infrastructure.