# Forward Fall Detection Using Inertial Data and Machine Learning
## 4 Testing the Developed Fall-Detection Models Using Experimental Measurements
### Testing the LSTM Neural Network  
After the acceleration data was recorded for all six volunteers while performing both undamped and damped forward falls, the values were inserted into the LSTM network and the accuracy of the predictions was evaluated. In Figures 17-24, the number of samples is relative to the time recorded for each participant to perform the simulated fall and consists of several samples between 240 and 450. For all six subjects, the predictions given by the trained LSTM are presented in Figures 20-25. Output 1 is given for damped fall, output 2 for forward undamped fall, and output 0 for normal activities. The mean predicted value of each case is shown in the title of the graph. Figure 19: Comparison of the recorded Y-axis normalized acceleration values for the forward damped fall event between simulated and experimental measurements for the 5 subjects. Figure 21: LSTM network fall event predictions for Subject 2: (**a**) Predictions for the damped forward fall; (**b**) Predictions for the undamped forward fall. Figure 22: LSTM network fall event predictions for Subject 3: (**a**) Predictions for the damped forward fall; (**b**) Predictions for the undamped forward fall. Based on the results presented in Figures 20-25, it can be concluded that fall events were accurately detected across all cases, and the majority were correctly classified accord  
Figure 23: LSTM network fall event predictions for Subject 4: (**a**) Predictions for the damped forward fall; (**b**) Predictions for the undamped forward fall. Figure 24: LSTM network fall event predictions for Subject 5: (**a**) Predictions for the damped forward fall; (**b**) Predictions for the undamped forward fall. Figure 25: LSTM network fall event predictions for Subject 6: (**a**) Predictions for the damped forward fall; (**b**) Predictions for the undamped forward fall. ing to fall type. However, some classification errors occurred in subjects 1 to 4, where forward falls were misclassified as damped falls. This misclassification may be partially attributed to the 10.2% False Negative Rate for Class 2, which indicates a tendency to confuse certain fall types, particularly forward falls, with other categories. These errors can also be attributed to the subjects' tendency to instinctively dampen their falls, despite being instructed otherwise. For all six volunteers, the acceleration values during normal gait movements were recorded, normalized, and combined into one single test sample. The results illustrate in Figure 26 that the predicted results were very close to the expected target 0, indicating that the network can predict normal walking activities to fall events. Furthermore, to test if the developed LSTM model is capable of distinguishing between fall events and normal activities like sitting in bed, the acceleration is measured on Subject 1 as he performs a 5-step walk and sits in bed by engaging the sitting position at first and, afterwards, laying down on his back. The results are presented in Figure 27.  
Because the maximum outputs did not exceed 0.6 as illustrated in the predictions in Figure 27, one can assume that by setting the appropriate threshold, the network does not indicate a fall event. The network correctly detected forward fall events and distinguished between the two types of falls correctly. The network predicted normal walking activities accurately, with values close to the expected target of 0, and no false positives were obtained for normal walking activities. For Subject 1, during a 5-step walk and sitting in bed, the network's predictions did not exceed 0.42, indicating no fall event, which resulted in the correct identification of sitting in bed and walking as non-falls with no false negatives. These findings lead to the conclusion that with the LSTM network, the events were correctly detected, there were no false positives, the non-fall activities were correctly identified as non-falls, and there were no false negative results. Figure 26: LSTM network prediction for normal walking of the six participants.