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Human action recognition has become a key direction in computer vision. Deep learning models, particularly when combined with sensor data fusion, can significantly enhance various applications by learning complex patterns and relationships from diverse data streams. Thus, this study proposes a new model, the Feature Fusion Graph Consecutive-Attention Network (FFGCAN), in order to enhance performance in the classification of the main tennis strokes: forehand, backhand, volley forehand, and volley backhand. The proposed network incorporates seven basic blocks that are combined with two types of module: an Adaptive Consecutive Attention Module, and Graph Self-Attention module. They are employed to extract joint information at different scales from the motion capture data. Due to focusing on relevant components, the model enriches the network’s comprehension of tennis motion data representation and allows for a more invested representation. Moreover, the FFGCAN utilizes a fusion of motion capture data that generates a channel-specific topology map for each output channel, reflecting how joints are connected when the tennis player is moving. The proposed solution was verified utilizing three well-known motion capture datasets, THETIS, Tennis-Mocap, and 3DTennisDS, each containing tennis movements in various formats. A series of experiments were performed, including data division into training (70%), validating (15%), and testing (15%) subsets. The testing utilized five trials. The FFCGAN model obtained very high results for accuracy, precision, recall, and F1-score, outperforming the commonly applied networks for action recognition, such as the Spatial-Temporal Graph Convolutional Network or its modifications. The proposed model demonstrated excellent tennis movement prediction ability. Full article

Motion capture (Mocap) data are widely used as time series to study human movement. Indeed, animation movies, video games, and biomechanical systems for rehabilitation are significant applications related to Mocap data. However, classifying multi-channel time series from Mocap requires coding the intrinsic dependencies (even nonlinear relationships) between human body joints. Furthermore, the same human action may have variations because the individual alters their movement and therefore the inter/intraclass variability. Here, we introduce an enhanced Hilbert embedding-based approach from a cross-covariance operator, termed EHECCO, to map the input Mocap time series to a tensor space built from both 3D skeletal joints and a principal component analysis-based projection. Obtained results demonstrate how EHECCO represents and discriminates joint probability distributions as kernel-based evaluation of input time series within a tensor reproducing kernel Hilbert space (RKHS). Our approach achieves competitive classification results for style/subject and action recognition tasks on well-known publicly available databases. Moreover, EHECCO favors the interpretation of relevant anthropometric variables correlated with players’ expertise and acted movement on a Tennis-Mocap database (also publicly available with this work). Thereby, our EHECCO-based framework provides a unified representation (through the tensor RKHS) of the Mocap time series to compute linear correlations between a coded metric from joint distributions and player properties, i.e., age, body measurements, and sport movement (action class). Full article