## 1 Introduction  
Vision-based human action recognition is a subject of intense study because of the decade's rewarding progress in the fields of artificial intelligence and computer vision. Knowing the amount of uniquely human behavior in each frame is a major highlight, and the data gleaned is a boon to detecting dangerous or falling actions [1]. It's useful for (a) variety of things, including (but not limited to) (b) ambient assisted living [2], (c) medical activities [3], and many more [4]. Due to its value and adaptability in the field of health care, human pose estimation in particular has grown increasingly popular because of the availability of modern activity recognition sensors. Many studies have shown that accurate posture labeling in clinical settings may support research and improve patient medical cycle monitoring. Diverse frames of reference, both local and global, based on human motion are needed to distinguish between human acts. Small but significant variations in human motion may cause recognition errors at a number of different sites. As a result, recognition performance might suffer when comparable characteristics are shared across many classifiers. Due to their exclusive reliance on the positions of human pivotal joints, skeleton-based approaches to human action recognition are preferably able to ignore irrelevant information and zero in on the action itself, regardless of distracting factors like, for example, background clutter or fluctuating lighting [5]. For both spatial and temporal inferences, a new method was proposed in [6], which represents human joints and their natural connections (i.e., bones) as spatio-temporal graphs. As a result, a number of successors have been proposed and shown promising results, all of which make use of the spatial and temporal information contained within joints with optimized anchored point construction strategies to extract multi-scale structural features on skeletons and long-range temporal dependencies among them. While spatial and temporal inferences from the joint have achieved preferable results, they are restricted in comparison to RGB frames-based approaches like spatio-temporal convolutional neural networks [7], which are pre-trained to improve accuracy in downstream datasets and tasks [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39]. Based on our investigation of the datasets depicted in system diagram, we conclude that the necessity for distinct adaptive adjacency matrices when using different topological human structures (e.g., joint number, joint order, bones) limits the generalization capacities of these techniques. However, we note that such adaptive sparse adjacency matrices are transformed into fully dense matrices in deeper layers in order to capture long-range dependencies between joints. This new structure contradicts the initial and original topological skeleton structure. We believe that 3D skeletal joint action detection may be replaced by a more optimal and general initialization technique in consideration of these factors and the fact that the human body's skeletal joint action representation is profoundly transformed throughout training. FGP-3D is a new unified framework for action recognition based on skeletons, and it helps to prove the premise. Each joint in FGP-3D indicates the dependency weight between its associated pair of joints because, to the anchored point, the action features matrix is initialized to be a uniformly distributed dependency detector. After that, a fully gated, anchored approach to aggregation is carried out to learn and aggregate many joint dependency matrices using various action descriptors. This mechanism effectively learns the spatio-temporal properties of skeletons by combining information from several representation sub-spaces located at various points along the dependence action features.