# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 2 Related Works  
Tennis stroke recognition has been of great interest recently. Many studies concerning tennis movement detection are performed, utilizing various types of data, such as motion capture, images, video, and sensors. For these purposes, publicly available datasets are applied, as well as data captured by the authors. Many Machine Learning and Deep Learning models have been proposed for tennis stroke recognition. Motion capture data of movements have been obtained for various systems, including markerless and marker-based. The THETIS dataset was created utilizing the Microsoft Kinect system. It contains twelve tennis moves, such as forehand, backhand, volley, service, and smash, as well as their types. A great number of studies concerning tennis stroke recognition have been performed utilizing this dataset. The five-layer deep historical Long Short-Term Memory (LSTM) network together with the Inception V3 model were combined for classification of tennis moves from video sequences [8]. All captured moves were de tected using InceptionResNetV2 and ResNet152V2. The CNN-LSTM network was applied for feature extraction from RGB video and Xception for spatial feature extraction [9]. A 3-layered LSTM model was also proposed to classify these moves. In the studies in [10; 11], the Inception network was chosen for feature extraction from RGB videos. The LSTM model with channel and attention modules was also applied for recognition of six tennis moves [12]. Linear-Chain Conditional Random Fields and Support Vector Machine were also used for classification of tennis moves [13]. Distinguishing the level of tennis players, taking into account twelve strokes, was performed utilizing k-NN classification with Dynamic Time Warping (DTW) [14]. Tennis forehand and backhand swing were detected utilizing a time-series CNN based on MPU9250 sensor [15]. A Hilbert-embedding-based framework (EHECCO) was used to extract the nonlinear dependencies for time series classification based on motion capture data, Tennis-Mocap, CMU, and HDM05 [16]. A multimodal solution, the Adaptive Semantic-Enhanced Convolutional Neural Network, was proposed for complex action classification from tennis data using the THEIS and Tennis-Mocap datasets [17]. The model integrates a Large Language Model to obtain an action semantic enhancement mechanism. Apart from the available datasets, a lot of studies have used data captured for the purpose of the research. Forehand and backhand swing were analyzed utilizing a Hidden Markov Model, where hand positions were indicated [18]. Forehand, backhand, serve, and volley were detected based on inertial sensor data, from the PIQ Robot sports tracker, utilizing Learning Vector Quantization [19]. A Deep Neural Network was applied for serve, backhand, and forehand recognition using tennis racket orientation gathered from a wearable SensorTile [20]. Decision Tree (DT), SVM, Neural Networks (NN), and k-NN methods were applied for classification of six tennis movements [21]. A SVM with a radial basis function kernel and k-NN methods were chosen for tennis serve, forehand, and backhand recognition [22]. The study involved data gathered from an eight-video camera system and IMU sensor. A series of studies concerning the recognition of forehand, backhand, volley forehand, and volley backhand strokes obtained from motion capture data, registered with tennis players together with a tennis racket, can be found in [23; 24; 25; 26; 27]. The movements were performed with and without a tennis ball, which were gathered into the 3 DTennisDS dataset [24]. The adjusted Spatial-Temporal Graph Neural Network (ST-GCN) together with active features was applied for images presenting the model of a tennis player holding a tennis racket, and reflecting the silhouette and the tennis racket position for the above-mentioned moves [23]. Moreover, the fuzzification of input data with a trapezoidal function improved the classification accuracy.