## 3 Proposed Approach
### Fully Gated Unit Features  
In order to estimate a human body mesh using the locations of 3D body skeleton joint points, we introduce an action recognition system based on 3D joint points. In order to do this, we study how to make educated assumptions about the 3D locations of all the body's joints, and we suggest an analytical approach to articulating a parametric body model, FGP-3D, using a series of simple geometric transformations. Our method outperforms the state-of-the-art methods in terms of alignment to video clips and the extraction of motion data since it depends directly on the coordination of all joints' locations. Our proposed approach utilizes gated recurrent units to pair mesh annotations and is able to achieve state-of-the-art mesh fittings through 3D skeletal joint regression alone. For machine learning applications that need memory and grouping, such as voice recognition, a gated recurrent unit is a component of a specialized type of recurrent neural network. In order to address the vanishing gradient problem, which often arises in recurrent neural networks, gated recurrent units may be used to modify the input weights of the network. In recurrent neural networks, the Gated Recurrent Unit is an effective solution for addressing the vanishing gradient issue [45]. In machine learning, the vanishing gradient issue arises when the gradient is so tiny that the weight cannot move. When depicted graphically, the operation of a gated recurrent unit network is not dissimilar to that of a basic recurrent neural network; the main difference between the two is in the internal workings of each recurrent unit, as gated recurrent unit networks are comprised of gates that modulate the current input and the previous hidden state. In addition to speech recognition, neural network models using gated recurrent units may be used for research on the human genome, handwriting analysis, and much more. Some of these innovative networks are used in stock market analysis and government work. Many of them leverage the simulated ability of machines to remember information. The update gate helps the model determine how much of the past information (from previous time steps) needs to be passed along to the future. That is really powerful because the model can decide to copy all the information from the past and eliminate the risk of vanishing gradient problems. We shall use the update gate in the next round since each component of \(p^{\text{th}}\) joint is required for the formation of \(z_{(i,j,k)}\). \[W_{(z,r,h)} =\frac{e^{2(i_{p}j_{p}k_{p})}+1}{e^{2(i_{p}j_{p}k_{p})}-1}, \tag{7}\] \[U_{(z,r,h)} =\frac{e^{2(i_{p}j_{p}k_{p})}}{e^{2(i_{p}j_{p}k_{p})}+1},\] (8) \[b_{(z,r,h)} =\frac{e^{2(i_{p}j_{p}k_{p})}}{e^{2(i_{p}j_{p}k_{p})}-1},\] (9) \[r_{t} =\sqrt{\left(i_{p}{}^{2}\right)+\left(j_{p}{}^{2}\right)+\left(k _{p}{}^{2}\right)}, \tag{10}\]  
Since each component \((z,r,h)\) of \(W,U,\) and \(b\) is calculated using Equations (7)-(9), the components of each cartesian coordinate are used to calculate each component of \((z,r,h)\), i.e., \((i,j,k)\). As a helpful factor to extract Equations (11) and (12), \(W,U,\) and \(b\) are used. Due to the fact that these equations and Equation (13) are the crucial elements for calculating Equation (14), which is an important consideration for calculating Equation (18), which includes the fully gated unit features. \[z_{(i,j,k)}=\gamma_{1}\Big{(}\,W_{z}(i_{p},l_{p},k_{p})+U_{z}h_{(i,j,k)-1}+b_{ z}\Big{)}, \tag{11}\]  
\[r_{(i,j,k)}=\gamma_{1}\Big{(}W_{r}(i_{p},l_{p},k_{p})+U_{r}h_{(i,j,k)-1}+b_{ r}\Big{)}, \tag{12}\]  
\[\widehat{h_{(i,j,k)}}=\gamma_{2}\Big{(}\,W_{h}(i_{p},l_{p},k_{p})+U_{h}\Big{(} z_{(i,j,k)}h_{(i,j,k)-1}\Big{)}+b_{h}\Big{)}, \tag{13}\]  
\[h_{(i,j,k)}=z_{i,j,k}\widehat{h_{(i,j,k)}}\big{(}1-z_{(i,j,k)}\big{)}\;h_{(i,j, k)-1}. \tag{14}\]  
Equation (14) incorporates fully gated unit features, which are represented for each coordinate by the notation \(h_{(i,j,k)}\).