Graph Convolutional Networks (GCNs) have been widely used in skeleton-based human action recognition and have achieved promising results. However, current GCN-based methods are limited by their inability to refine semantic-guided joint relations and perform adaptive multi-scale analysis. These limitations impair their performance, particularly for analogical actions involving the interaction of the same body parts (e.g., drinking water and eating) as well as deficient actions with limited spatial-temporal information (e.g., subtle action writing and transient action sneezing). To solve these problems, we propose Part-level Refined Spatial Graph Convolution (PR-SGC) and Scale-aware Temporal Graph Convolution (Sa-TGC) for optimal action representation. The PR-SGC divides the skeleton into body parts and embeds this high-level semantics to refine the physical adjacency matrix. The Sa-TGC leverages the dynamic scale-aware mechanism to extract context-dependent multi-scale features. On this basis, we develop a novel Scale-aware Graph Convolutional Network with Part-level Refinement (SaPR-GCN), which is on par with state-of-the-art benchmarks on NTU RGB+D 60, NTU RGB+D 120, and NW-UCLA datasets.

The Internet of Medical Things (IoMT) stimulates the development of intelligent medical applications. As mental disorders become a global problem, emotion recognition has received widespread attention, as it can contribute to more comprehensive mental health monitoring and psychological assessment. Physiological signal-based emotion-aware monitoring is a particularly promising application due to its noninvasive and objective data collection. Recently, multimodal emotion recognition has been enhanced with wireless body area network (WBAN) access to IoMT, where wireless medical sensors are interconnected and abundant signals are acquired conveniently. However, how to synthesize these multisource physiological signals to facilitate emotion recognition is a challenging problem due to their heterogeneity and interference. To solve this problem, we propose a real-time differential multimodal transformer (Diff-MT), where the main components are the differential hyperinformation extraction (DHE) module, the multimodal global cross-attention encoder (MGCE), and the difference-augmented feature fusion (DFF). Ultimately, we endow the system with emotional awareness and distribute the state to IoMT devices. Extensive experiments demonstrate that the proposed Diff-MT exhibits superior performance compared to existing methods on the WESAD and DEAP datasets and is appropriate for IoMT-based healthcare.

Recently, human action recognition has gained widespread use in fields such as human–robot interaction, healthcare, and sports. With the popularity of wearable devices, we can easily access sensor data of human actions for human action recognition. However, extracting spatio-temporal motion patterns from sensor data and capturing fine-grained action processes remain a challenge. To address this problem, we proposed a novel hierarchical multi-scale adaptive Conv-LSTM network structure called HMA Conv-LSTM. The spatial information of sensor signals is extracted by hierarchical multi-scale convolution with finer-grained features, and the multi-channel features are fused by adaptive channel feature fusion to retain important information and improve the efficiency of the model. The dynamic channel-selection-LSTM based on the attention mechanism captures the temporal context information and long-term dependence of the sensor signals. Experimental results show that the proposed model achieves Macro F1-scores of 0.68, 0.91, 0.53, and 0.96 on four public datasets: Opportunity, PAMAP2, USC-HAD, and Skoda, respectively. Our model demonstrates competitive performance when compared to several state-of-the-art approaches.

Human action recognition has facilitated the development of artificial intelligence devices focusing on human activities and services. This technology has progressed by introducing 3D point clouds derived from depth cameras or radars. However, human behavior is intricate, and the involved point clouds are vast, disordered, and complicated, posing challenges to 3D action recognition. To solve these problems, we propose a Symmetric Fine-coarse Neural Network (SFCNet) that simultaneously analyzes human actions’ appearance and details. Firstly, the point cloud sequences are transformed and voxelized into structured 3D voxel sets. These sets are then augmented with an interval-frequency descriptor to generate 6D features capturing spatiotemporal dynamic information. By evaluating voxel space occupancy using thresholding, we can effectively identify the essential parts. After that, all the voxels with the 6D feature are directed to the global coarse stream, while the voxels within the key parts are routed to the local fine stream. These two streams extract global appearance features and critical body parts by utilizing symmetric PointNet++. Subsequently, attention feature fusion is employed to capture more discriminative motion patterns adaptively. Experiments conducted on public benchmark datasets NTU RGB+D 60 and NTU RGB+D 120 validate SFCNet’s effectiveness and superiority for 3D action recognition.