## 3 Methodology
### Prediction Model
#### 3.2.3 Classifiers  
To categorize the labeled drop data, a neural network with four steps of feed-forward classification is utilized. To increase the accuracy of data predictions, a supervised learning approach known as a \"4-stage forward neural network\" (4 SFNN) was implemented. Each categorization level is fine-tuned using a technique known as particle swarm optimization (PSO). Table 1 shows the list of configuration parameters of the proposed model. PSO is used to improve training quality by roughly modeling mistakes and, hence, maximizing the performance of each step of the classification process. Weight/bias estimation is used to achieve this purpose. This approach assigns weight/bias values depending on the changing value of the training MSE that happens during the investigation. Using this approach, the information is divided into four groups based on the characteristics of the fall: MSC, FKL, FOL, and SDL (each one represents a type of falling mentioned in Table 1). As a result, every incoming class is subjected to a four-stage categorization procedure (Figure 3 demonstrates the underlying structure of the proposed \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Activity**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Data**} & \multicolumn{2}{c}{**AEC**} & \multicolumn{2}{c}{**FFNN**} & \multicolumn{2}{c}{**FFNN-PSO**} \\ \cline{3-10}  & & & **Training** & **Testing** & **Training** & **Validation** & **Testing** & **Training** & **Validation** & **Testing** \\
2 & & & **80\%** & **20\%** & **80\%** & **20\%** & **20\%** & **70\%** & **20\%** & **10\%** \\ \hline
1 & Falling forward using hands & 159,333 & 127,466.4 & 31,866.6 & 127,466.4 & 15,933.3 & 111,533.1 & 11,533.1 & 31,866.6 & 15,933.3 \\
2 & Falling forward using knees & 161,716 & 128,940.8 & 32,255.2 & 128,940.8 & 16,117.6 & 112,822.2 & 112,823.2 & 32,235.2 & 16,117.6 \\
3 & Falling backward & 158,528 & 126,822.4 & 31,705.6 & 126,822.4 & 15,852.8 & 110,969.6 & 110,969.6 & 31,705.6 & 15,852.8 \\
4 & Falling sideward & 158,090 & 126,472 & 31,618 & 126,472 & 15,809 & 110,663 & 110,663 & 31,618 & 15,809 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The distribution of activity data. Figure 2: Falling precision with the 10-cross valuation; (**a**) AEC accuracy; (**b**) AEC MAE. classifier). As result of the labeling challenge, it is difficult to use appropriate classifiers; extensive performance evaluation may be performed to strengthen the reliability of the proposed method. To ensure that the findings are valid, each suggested classifier must be evaluated using metrics, such as the proportion of correct classifications and the MAE. When the classification is being trained, an epoch is a full pass through the training data. Low epoch counts can lead to under-fitting, which occurs when the model has not learned enough from the training set of data to make reliable predictions about the set of incoming data. On the other hand, too many epochs may lead to overfitting, which prevents the model from generalizing well to new data, since it has learned too much from the training set. Therefore, it is vital to be vigilant about the model's performance on a validation dataset throughout the training process and to stop training when the validation loss starts to rise the overfitting. The common process for adjusting the ideal number of epochs is called early stopping, which automatically determines the optimal number of epochs based on the performance of your model on a validation dataset. Divide the dataset into three sets: a training, a validation, and a test set. Train the model on the training set for a large number of epochs, which is significantly greater than anticipated. After each epoch, evaluate how well the model performs on the validation set. Whenever the enhancement of a validation set's performance is stopping training, then use the epoch with the best validation set performance.