## 3 Proposed Approach
### Fully Gated and Anchored Approach, FGP-3D  
In this part, we provide FGP-3D, a unifying solution for skeleton-based action recognition with completely gated, anchored spatiotemporal dependencies. Figure 2 depicts the finished model's architecture. The connectivity between joints in the original human topology does not include this connectivity, which means that for many human movements, it is not the most appropriate to be utilized to extract features on skeletons. We introduce anchored pint, which can express the ideal dependencies between joints for efficient feature extraction, to sort out this issue. A stack of anchored point extraction, fully gated unit features, and fully popovicius features, spatial-temporal FGP-3D blocks to extract charac teristics from skeletal sequences, are present. By utilizing several dependency matrices and our suggested finalization technique, we may automatically collect the features focused on various body joints by repeating the fully gated anchored mechanism. We may utilize the number of sequences (s) from 1 to \(n\) as a flexible hyper-parameter to enhance the model as it is no longer constrained by the human topology. We used Equation (14) to solve it for each coordinate component, \(h_{i},h_{j},\text{and }h_{k}\), determined at each frame. We then used Equation (17) to use \(f_{pf}\), which contains the dependent information between the anchored joint \((i_{a},j_{a},k_{a})\) and any specific \(p^{th}\). We suggested the FPG-3D skeleton's joints-based action recognition descriptor as a blend of independent as well as dependent joints. Our theory was supported by the ablation investigation (see Equation (18)). Overall, our design approach increases the architecture's adaptability, efficiency, and genericity, which makes it easier to research cross-domain spatial and temporal feature learning in this field for datasets utilizing various joint distributions. \[\text{FGP-3D }=\left.\left[h_{i}h_{j}h_{k}f_{pf}\right]\right|_{S}^{s}=1 \tag{18}\]  
As we have computed the FPG-3D action descriptor in the form of a feature vector including the dependent and independent motion of all joints, we can say that it includes both the dependent and independent motion. Using Equation (14), the independent motion is determined, with each component capturing gradually finer motion details. While Equation (17) extracts the primary direction of a joint with regard to anchored joints. This spatial convolution relies on a joint representation during the early learning stage, but during the later learning stage, the relationships coded within the other features contain sparse and the joint connections required to represent a complete joint orientation, which is equivalent to an anchored point. The dependencies converge to a sparse representation in the subsequent stage of creating action descriptors with fully gated unit features, which is locally optimal but fundamentally different from the original Euclidean connectivity of the human body because it calculates the orientation of mutual and independent joint vectors. The dependencies eventually converge to a temporal representation yet again, containing orientation based on fully Popovicius features. This motivates us to modify the \"FGP-3D\"  
Figure 2: Schematic representation of the proposed system, FGP-3D. To identify the most informative representations for skeleton-based action identification, we offer a fully gated anchoring method. The anchored point infers a point’s inherent topology, which gives contextual information beyond normal physical connectivity. Using fully gated unit features and fully popovicius features, spatial and temporal dependencies will be derived from each joint block of the input skeleton. After classifying the suggested model, the FGP-3D module represents action more accurately by capturing context-dependent intrinsic joint topology.