## 3 Methodology
### Optimanzed  
PSO improves the weights of an FFNN by repeatedly altering particle locations to classify features. This technique improves the FFNN convergence rate and learning process, creating a more accurate and efficient neural network model. FFNN-PSO has four layers with 40, 30, 10, and 10 neurons, with 0.7%, 0.2%, and 0.1% training, validation, and testing tests. Stage 1 findings for the MSE class, where the proposed classifier predicts the MSE class with 98.95% accuracy, the MAE with 0.016, the MSE with 0.0168, and the RMSE with 0.129. Stage 3 of the proposed classifier predicts the SDL class with 97.94% accuracy, the MAE with 0.028, the MSE with 0.0266, and the RMSE with 0.137. Figure 5 shows that the proposed classifier predicts FKL with a 98.45% accuracy, the MAE with 0.018, the MSE with 0.0216, and the RMSE with 0.147. The proposed classifier has a FOL prediction accuracy of 98.76%, a FKL of 0.0176, a MSE of 0.0363, and a RMSE of 0.190. Six male and five female participants, aged 22 to 36, with a mean height of 176.09 cm and a mean weight of 77.63 kg, were asked to perform different falling activities (falling backward, falling forward, sitting in an empty chair, and falling sideways) to collect raw data from wearable (x, y, z) axis accelerometer sensors and (roll, pitch, yaw) gyroscope sensors at 100 Hz. Choose consistent missing value characteristics. The dataset has four subgroups. Find the frequency of each character in each chosen subgroup. Employ the classification techniques and accuracy measures to gradually examine feature prediction abilities. This study uses the dataset to anticipate three fall models. The PSO technique gave the FFNN classifier a final prediction accuracy of 98.615%, compared to 52.8% for the AEC in the first model (see Table 3). The mean absolute error rate also contrasts AEC with the suggested methods. PSO in the FFNN algorithm stages decreases the MAE rate from 0.0262 to 0.0193, improving performance and reducing errors. Figure 5: 3 SFNN of falling precision with the 10-cross valuation (no optimization): (**a**) accuracy; (**b**) MAE; (**c**) MSE; (**d**) RMSE. Similarly, there is an obvious increase in training and validation accuracy with the rise of the number of epochs, which has a significant impact on improving the system and reducing loss. As shown in the Figure 6, the increase in the number of epochs to 30 leads to a significant improvement, reaching 92.3% in training accuracy with 88.8% in validation, and, then, the increment during the range of 60 to 80 epoch matched to a great increase in each of training and validation accuracy, arriving at 98.1 and 95.3, respectively, until it stabilized between 80 to 100. Thus, it is clear how the appropriate choice of the number of epochs achieved best classification performance. With exact performance percentages (see Table 4), powerful human fall detection systems increase safety and wellbeing, especially for the elderly and those with medical issues. Caregivers and family members may rest easy with fall detection systems. These technologies can reduce falls that need emergency medical services and hospitalizations, lowering healthcare costs.